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FLI Podcast: On Global Priorities, Existential Risk, and What Matters Most with Sam Harris

Human civilization increasingly has the potential both to improve the lives of everyone and to completely destroy everything. The proliferation of emerging technologies calls our attention to this never-before-seen power — and the need to cultivate the wisdom with which to steer it towards beneficial outcomes. If we’re serious both as individuals and as a species about improving the world, it’s crucial that we converge around the reality of our situation and what matters most. What are the most important problems in the world today and why? In this episode of the Future of Life Institute Podcast, Sam Harris joins us to discuss some of these global priorities, the ethics surrounding them, and what we can do to address them.

Topics discussed in this episode include:

  • The problem of communication 
  • Global priorities 
  • Existential risk 
  • Animal suffering in both wild animals and factory farmed animals 
  • Global poverty 
  • Artificial general intelligence risk and AI alignment 
  • Ethics
  • Sam’s book, The Moral Landscape

You can take a survey about the podcast here

Submit a nominee for the Future of Life Award here

 

Timestamps: 

0:00 Intro

3:52 What are the most important problems in the world?

13:14 Global priorities: existential risk

20:15 Why global catastrophic risks are more likely than existential risks

25:09 Longtermist philosophy

31:36 Making existential and global catastrophic risk more emotionally salient

34:41 How analyzing the self makes longtermism more attractive

40:28 Global priorities & effective altruism: animal suffering and global poverty

56:03 Is machine suffering the next global moral catastrophe?

59:36 AI alignment and artificial general intelligence/superintelligence risk

01:11:25 Expanding our moral circle of compassion

01:13:00 The Moral Landscape, consciousness, and moral realism

01:30:14 Can bliss and wellbeing be mathematically defined?

01:31:03 Where to follow Sam and concluding thoughts

 

You can follow Sam here: 

samharris.org

Twitter: @SamHarrisOrg

 

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You can listen to the podcast above or read the transcript below. 

Lucas Perry: Welcome to the Future of Life Institute Podcast. I’m Lucas Perry. Today we have a conversation with Sam Harris where we get into issues related to global priorities, effective altruism, and existential risk. In particular, this podcast covers the critical importance of improving our ability to communicate and converge on the truth, animal suffering in both wild animals and factory farmed animals, global poverty, artificial general intelligence risk and AI alignment, as well as ethics and some thoughts on Sam’s book, The Moral Landscape. 

If you find this podcast valuable, you can subscribe or follow us on your preferred listening platform, like on Apple Podcasts, Spotify, Soundcloud, or whatever your preferred podcasting app is. You can also support us by leaving a review. 

Before we get into it, I would like to echo two announcements from previous podcasts. If you’ve been tuned into the FLI Podcast recently you can skip ahead just a bit. The first is that there is an ongoing survey for this podcast where you can give me feedback and voice your opinion about content. This goes a super long way for helping me to make the podcast valuable for everyone. You can find a link for the survey about this podcast in the description of wherever you might be listening. 

The second announcement is that at the Future of Life Institute we are in the midst of our search for the 2020 winner of the Future of Life Award. The Future of Life Award is a $50,000 prize that we give out to an individual who, without having received much recognition at the time of their actions, has helped to make today dramatically better than it may have been otherwise. The first two recipients of the Future of Life Award were Vasili Arkhipov and Stanislav Petrov, two heroes of the nuclear age. Both took actions at great personal risk to possibly prevent an all-out nuclear war. The third recipient was Dr. Matthew Meselson, who spearheaded the international ban on bioweapons. Right now, we’re not sure who to give the 2020 Future of Life Award to. That’s where you come in. If you know of an unsung hero who has helped to avoid global catastrophic disaster, or who has done incredible work to ensure a beneficial future of life, please head over to the Future of Life Award page and submit a candidate for consideration. The link for that page is on the page for this podcast or in the description of wherever you might be listening. If your candidate is chosen, you will receive $3,000 as a token of our appreciation. We’re also incentivizing the search via MIT’s successful red balloon strategy, where the first to nominate the winner gets $3,000 as mentioned, but there are also tiered pay outs where the first to invite the nomination winner gets $1,500, whoever first invited them gets $750, whoever first invited the previous person gets $375, and so on. You can find details about that on the Future of Life Award page. 

Sam Harris has a PhD in neuroscience from UCLA and is the author of five New York Times best sellers. His books include The End of Faith, Letter to a Christian Nation, The Moral Landscape, Free Will, Lying, Waking Up, and Islam and the Future of Tolerance (with Maajid Nawaz). Sam hosts the Making Sense Podcast and is also the creator of the Waking Up App, which is for anyone who wants to learn to meditate in a modern, scientific context. Sam has practiced meditation for more than 30 years and studied with many Tibetan, Indian, Burmese, and Western meditation teachers, both in the United States and abroad.

And with that, here’s my conversation with Sam Harris.

Starting off here, trying to get a perspective on what matters most in the world and global priorities or crucial areas for consideration, what do you see as the most important problems in the world today?

Sam Harris: There is one fundamental problem which is encouragingly or depressingly non-technical, depending on your view of it. I mean it should be such a simple problem to solve, but it’s seeming more or less totally intractable and that’s just the problem of communication. The problem of persuasion, the problem of getting people to agree on a shared consensus view of reality, and to acknowledge basic facts and to have their probability assessments of various outcomes to converge through honest conversation. Politics is obviously the great confounder of this meeting of the minds. I mean, our failure to fuse cognitive horizons through conversation is reliably derailed by politics. But there are other sorts of ideology that do this just as well, religion being perhaps first among them.

And so it seems to me that the first problem we need to solve, the place where we need to make progress and we need to fight for every inch of ground and try not to lose it again and again is in our ability to talk to one another about what is true and what is worth paying attention to, to get our norms to align on a similar picture of what matters. Basically value alignment, not with superintelligent AI, but with other human beings. That’s the master riddle we have to solve and our failure to solve it prevents us from doing anything else that requires cooperation. That’s where I’m most concerned. Obviously technology influences it, social media and even AI and the algorithms behind the gaming of everyone’s attention. All of that is influencing our public conversation, but it really is a very apish concern and we have to get our arms around it.

Lucas Perry: So that’s quite interesting and not the answer that I was expecting. I think that that sounds like quite the crucial stepping stone. Like the fact that climate change isn’t something that we’re able to agree upon, and is a matter of political opinion drives me crazy. And that’s one of many different global catastrophic or existential risk issues.

Sam Harris: Yeah. The COVID pandemic has made me, especially skeptical of our agreeing to do anything about climate change. The fact that we can’t persuade people about the basic facts of epidemiology when this thing is literally coming in through the doors and windows, and even very smart people are now going down the rabbit hole of this is on some level a hoax, people’s political and economic interests just bend their view of basic facts. I mean it’s not to say that there hasn’t been a fair amount of uncertainty here, but it’s not the sort of uncertainty that should give us these radically different views of what’s happening out in the world. Here we have a pandemic moving in real time. I mean, where we can see a wave of illness breaking in Italy a few weeks before it breaks in New York. And again, there’s just this Baghdad Bob level of denialism. The prospects of our getting our heads straight with respect to climate change in light of what’s possible in the middle of a pandemic, that seems at the moment, totally farfetched to me.

For something like climate change, I really think a technological elite needs to just decide at the problem and decide to solve it by changing the kinds of products we create and the way we manufacture things and we just have to get out of the politics of it. It can’t be a matter of persuading more than half of American society to make economic sacrifices. It’s much more along the lines of just building cars and other products that are carbon neutral that people want and solving the problem that way.

Lucas Perry: Right. Incentivizing the solution by making products that are desirable and satisfy people’s self-interest.

Sam Harris: Yeah. Yeah.

Lucas Perry: I do want to explore more actual global priorities. This point about the necessity of reason for being able to at least converge upon the global priorities that are most important seems to be a crucial and necessary stepping stone. So before we get into talking about things like existential and global catastrophic risk, do you see a way of this project of promoting reason and good conversation and converging around good ideas succeeding? Or do you have any other things to sort of add to these instrumental abilities humanity needs to cultivate for being able to rally around global priorities?

Sam Harris: Well, I don’t see a lot of innovation beyond just noticing that conversation is the only tool we have. Intellectual honesty spread through the mechanism of conversation is the only tool we have to converge in these ways. I guess the thing to notice that’s guaranteed to make it difficult is bad incentives. So we should always be noticing what incentives are doing behind the scenes to people’s cognition. There are things that could be improved in media. I think the advertising model is a terrible system of incentives for journalists and anyone else who’s spreading information. You’re incentivized to create sensational hot takes and clickbait and depersonalize everything. Just create one lurid confection after another, that really doesn’t get at what’s true. The fact that this tribalizes almost every conversation and forces people to view it through a political lens. The way this is all amplified by Facebook’s business model and the fact that you can sell political ads on Facebook and we use their micro-targeting algorithm to frankly, distort people’s vision of reality and get them to vote or not vote based on some delusion.

All of this is pathological and it has to be disincentivized in some way. The business model of digital media is part of the problem. But beyond that, people have to be better educated and realize that thinking through problems and understanding facts and creating better arguments and responding to better arguments and realizing when you’re wrong, these are muscles that need to be trained, and there are certain environments in which you can train them well. And there’s certain environments where they are guaranteed to atrophy. Education largely consists in the former, in just training someone to interact with ideas and with shared perceptions and with arguments and evidence in a way that is agnostic as to how things will come out. You’re just curious to know what’s true. You don’t want to be wrong. You don’t want to be self-deceived. You don’t want to have your epistemology anchored to wishful thinking and confirmation bias and political partisanship and religious taboos and other engines of bullshit, really.

I mean, you want to be free of all that, and you don’t want to have your personal identity trimming down your perception of what is true or likely to be true or might yet happen. People have to understand what it feels like to be willing to reason about the world in a way that is unconcerned about the normal, psychological and tribal identity formation that most people, most of the time use to filter against ideas. They’ll hear an idea and they don’t like the sound of it because it violates some cherished notion they already have in the bag. So they don’t want to believe it. That should be a tip off. That’s not more evidence in favor of your worldview. That’s evidence that you are an ape who’s disinclined to understand what’s actually happening in the world. That should be an alarm that goes off for you, not a reason to double down on the last bad idea you just expressed on Twitter.

Lucas Perry: Yeah. The way the ego and concern for reputation and personal identity and shared human psychological biases influence the way that we do conversations seems to be a really big hindrance here. And being aware of how your mind is reacting in each moment to the kinetics of the conversation and what is happening can be really skillful for catching unwholesome or unskillful reactions it seems. And I’ve found that non-violent communication has been really helpful for me in terms of having valuable open discourse where one’s identity or pride isn’t on the line. The ability to seek truth with another person instead of have a debate or argument is a skill certainly developed. Yet that kind of format for discussion isn’t always rewarded or promoted as well as something like an adversarial debate, which tends to get a lot more attention.

Sam Harris: Yeah.

Lucas Perry: So as we begin to strengthen our epistemology and conversational muscles so that we’re able to arrive at agreement on core issues, that’ll allow us to create a better civilization and work on what matters. So I do want to pivot here into what those specific things might be. Now I have three general categories, maybe four, for us to touch on here.

The first is existential risk that primarily come from technology, which might lead to the extinction of Earth originating life, or more specifically just the extinction of human life. You have a Ted Talk on AGI risk, that’s artificial general intelligence risk, the risk of machines becoming as smart or smarter than human beings and being misaligned with human values. There’s also synthetic bio risk where advancements in genetic engineering may unleash a new age of engineered pandemics, which are more lethal than anything that is produced by nature. We have nuclear war, and we also have new technologies or events that might come about that we aren’t aware of or can’t predict yet. And the other categories in terms of global priorities, I want to touch on are global poverty, animal suffering and human health and longevity. So how is it that you think of and prioritize and what is your reaction to these issues and their relative importance in the world?

Sam Harris: Well, I’m persuaded that thinking about existential risk is something we should do much more. It is amazing how few people spend time on this problem. It’s a big deal that we have the survival of our species as a blind spot, but I’m more concerned about what seems likelier to me, which is not that we will do something so catastrophically unwise as to erase ourselves, certainly not in the near term. And we’re capable of doing that clearly, but I think it’s more likely we’re capable of ensuring our unrecoverable misery for a good long while. We could just make life basically not worth living, but we’ll be forced or someone will be forced to live it all the while, basically a Road Warrior like hellscape could await us as opposed to just pure annihilation. So that’s a civilizational risk that I worry more about than extinction because it just seems probabilistically much more likely to happen no matter how big our errors are.

I worry about our stumbling into an accidental nuclear war. That’s something that I think is still pretty high on the list of likely ways we could completely screw up the possibility of human happiness in the near term. It’s humbling to consider what an opportunity cost this, compared to what’s possible, minor pandemic is, right. I mean, we’ve got this pandemic that has locked down most of humanity and every problem we had and every risk we were running as a species prior to anyone learning the name of this virus is still here. The threat of nuclear war has not gone away. It’s just, this has taken up all of our bandwidth. We can’t think about much else. It’s also humbling to observe how hard a time we’re having, even agreeing about what’s happening here, much less responding intelligently to the problem. If you imagine a pandemic that was orders of magnitude, more deadly and more transmissible, man, this is a pretty startling dress rehearsal.

I hope we learn something from this. I hope we think more about things like this happening in the future and prepare for them in advance. I mean, the fact that we have a CDC, that still cannot get its act together is just astounding. And again, politics is the thing that is gumming up the gears in any machine that would otherwise run halfway decently at the moment. I mean, we have a truly deranged president and that is not a partisan observation. That is something that can be said about Trump. And it would not be said about most other Republican presidents. There’s nothing I would say about Trump that I could say about someone like Mitt Romney or any other prominent Republican. This is the perfect circumstance to accentuate the downside of having someone in charge who lies more readily than any person in human history perhaps.

It’s like toxic waste at the informational level has been spread around for three years now and now it really matters that we have an information ecosystem that has no immunity against crazy distortions of the truth. So I hope we learn something from this. And I hope we begin to prioritize the list of our gravest concerns and begin steeling our civilization against the risk that any of these things will happen. And some of these things are guaranteed to happen. The thing that’s so bizarre about our failure to grapple with a pandemic of this sort is, this is the one thing we knew was going to happen. This was not a matter of “if.” This was only a matter of “when.” Now nuclear war is still a matter of “if”, right? I mean, we have the bombs, they’re on hair-trigger, overseen by absolutely bizarre and archaic protocols and highly outdated technology. We know this is just a doomsday system we’ve built that could go off at any time through sheer accident or ineptitude. But it’s not guaranteed to go off.

But pandemics are just guaranteed to emerge and we still were caught flat footed here. And so I just think we need to use this occasion to learn a lot about how to respond to this sort of thing. And again, if we can’t convince the public that this sort of thing is worth paying attention to, we have to do it behind closed doors, right? I mean, we have to get people into power who have their heads screwed on straight here and just ram it through. There has to be a kind of Manhattan Project level urgency to this, because this is about as benign a pandemic as we could have had, that would still cause significant problems. An engineered virus, a weaponized virus that was calculated to kill the maximum number of people. I mean, that’s a zombie movie, all of a sudden, and we’re not ready for the zombies.

Lucas Perry: I think that my two biggest updates from the pandemic were that human civilization is much more fragile than I thought it was. And also I trust the US government way less now in its capability to mitigate these things. I think at one point you said that 9/11 was the first time that you felt like you were actually in history. And as someone who’s 25, being in the COVID pandemic, this is the first time that I feel like I’m in human history. Because my life so far has been very normal and constrained, and the boundaries between everything has been very rigid and solid, but this is perturbing that.

So you mentioned that you were slightly less worried about humanity just erasing ourselves via some kind of existential risk and part of the idea here seems to be that there are futures that are not worth living. Like if there’s such thing as a moment or a day that isn’t worth living then there are also futures that are not worth living. So I’m curious if you could unpack why you feel that these periods of time that are not worth living are more likely than existential risks. And if you think that some of those existential conditions could be permanent, and could you speak a little bit about the relative likely hood of existential risk and suffering risks and whether you see the higher likelihood of the suffering risks to be ones that are constrained in time or indefinite.

Sam Harris: In terms of the probabilities, it just seems obvious that it is harder to eradicate the possibility of human life entirely than it is to just kill a lot of people and make the remaining people miserable. Right? If a pandemic spreads, whether it’s natural or engineered, that has 70% mortality and the transmissibility of measles, that’s going to kill billions of people. But it seems likely that it may spare some millions of people or tens of millions of people, even hundreds of millions of people and those people will be left to suffer their inability to function in the style to which we’ve all grown accustomed. So it would be with war. I mean, we could have a nuclear war and even a nuclear winter, but the idea that it’ll kill every last person or every last mammal, it would have to be a bigger war and a worse winter to do that.

So I see the prospect of things going horribly wrong to be one that yields, not a dial tone, but some level of remaining, even civilized life, that’s just terrible, that nobody would want. Where we basically all have the quality of life of what it was like on a mediocre day in the middle of the civil war in Syria. Who wants to live that way? If every city on Earth is basically a dystopian cell on a prison planet, that for me is a sufficient ruination of the hopes and aspirations of civilized humanity. That’s enough to motivate all of our efforts to avoid things like accidental nuclear war and uncontrolled pandemics and all the rest. And in some ways it’s more of motivating because when you ask people, what’s the problem with the failure to continue the species, right? Like if we all died painlessly in our sleep tonight, what’s the problem with that?

That actually stumps some considerable number of people because they immediately see that the complete annihilation of the species painlessly is really a kind of victimless crime. There’s no one around to suffer our absence. There’s no one around to be bereaved. There’s no one around to think, oh man, we could have had billions of years of creativity and insight and exploration of the cosmos and now the lights have gone out on the whole human project. There’s no one around to suffer that disillusionment. So what’s the problem? I’m persuaded that that’s not the perfect place to stand to evaluate the ethics. I agree that losing that opportunity is a negative outcome that we want to value appropriately, but it’s harder to value it emotionally and it’s not as clear. I mean it’s also, there’s an asymmetry between happiness and suffering, which I think is hard to get around.

We are perhaps rightly more concerned about suffering than we are about losing opportunities for wellbeing. If I told you, you could have an hour of the greatest possible happiness, but it would have to be followed by an hour of the worst possible suffering. I think most people given that offer would say, oh, well, okay, I’m good. I’ll just stick with what it’s like to be me. The hour of the worst possible misery seems like it’s going to be worse than the highest possible happiness is going to be good and I do sort of share that intuition. And when you think about it, in terms of the future of humanity, I think it is more motivating to think, not that your grandchildren might not exist, but that your grandchildren might live horrible lives, really unendurable lives and they’ll be forced to live them because there’ll be born. If for no other reason, then we have to persuade some people to take these concerns seriously, I think that’s the place to put most of the emphasis.

Lucas Perry: I think that’s an excellent point. I think it makes it more morally salient and leverages human self-interest more. One distinction that I want to make is the distinction between existential risks and global catastrophic risks. Global catastrophic risks are those which would kill a large fraction of humanity without killing everyone, and existential risks are ones which would exterminate all people or all Earth-originating intelligent life. And this former risk, the global catastrophic risks are the ones which you’re primarily discussing here where something goes really bad and now we’re left with some pretty bad existential situation.

Sam Harris: Yeah.

Lucas Perry: Now we’re not locked in that forever. So it’s pretty far away from being what is talked about in the effective altruism community as a suffering risk. That actually might only last a hundred or a few hundred years or maybe less. Who knows. It depends on what happened. But now taking a bird’s eye view again on global priorities and standing on a solid ground of ethics, what is your perspective on longtermist philosophy? This is the position or idea that the deep future has overwhelming moral priority, given the countless trillions of lives that could be lived. So if an existential risk occur, then we’re basically canceling the whole future like you mentioned. There won’t be any suffering and there won’t be any joy, but we’re missing out on a ton of good it would seem. And with the continued evolution of life, through genetic engineering and enhancements and artificial intelligence, it would seem that the future could also be unimaginably good.

If you do an expected value calculation about existential risks, you can estimate very roughly the likelihood of each existential risk, whether it be from artificial general intelligence or synthetic bio or nuclear weapons or a black swan event that we couldn’t predict. And you multiply that by the amount of value in the future, you’ll get some astronomical number, given the astronomical amount of value in the future. Does this kind of argument or viewpoint do the work for you to commit you to seeing existential risk as a global priority or the central global priority?

Sam Harris: Well, it doesn’t do the emotional work largely because we’re just bad at thinking about longterm risk. It doesn’t even have to be that long-term for our intuitions and concerns to degrade irrationally. We’re bad at thinking about the well-being, even of our future selves as you get further out in time. The term of jargon is that we “hyperbolically discount” our future well being. People will smoke cigarettes or make other imprudent decisions in the present. They know they will be the inheritors of these bad decisions, but there’s some short-term upside.

The mere pleasure of the next cigarette say, that convinces them that they don’t really have to think long and hard about what their future self will wish they had done at this point. Our ability to be motivated by what we think is likely to happen in the future is even worse when we’re thinking about our descendants. Right? People we either haven’t met yet or may never meet. I have kids, but I don’t have grandkids. How much of my bandwidth is taken up thinking about the kinds of lives my grandchildren will have? Really none. It’s conserved. It’s safeguarded by my concern about my kids, at this point.

But, then there are people who don’t have kids and are just thinking about themselves. It’s hard to think about the comparatively near future. Even a future that, barring some real mishap, you have every expectation of having to live in yourself. It’s just hard to prioritize. When you’re talking about the far future, it becomes very, very difficult. You just have to have the science fiction geek gene or something disproportionately active in your brain, to really care about that.

Unless you think you are somehow going to cheat death and get aboard the starship when it’s finally built. You’re popping 200 vitamins a day with Ray Kurzweil and you think you might just be in the cohort of people who are going to make it out of here without dying because we’re just on the cusp of engineering death out of the system, then I could see, okay. There’s a self interested view of it. If you’re really talking about hypothetical people who you know you will never come in contact with, I think it’s hard to be sufficiently motivated, even if you believe the moral algebra here.

It’s not clear to me that it need run through. I agree with you that if you do a basic expected value calculation here, and you start talking about trillions of possible lives, their interests must outweigh the interests of the 7.8 or whatever it is, billion of us currently alive. A few asymmetries here, again. The asymmetry between actual and hypothetical lives, there are no identifiable lives who would be deprived of anything if we all just decided to stop having kids. You have to take the point of view of the people alive who make this decision.

If we all just decided, “Listen. These are our lives to live. We can decide how we want to live them. None of us want to have kids anymore.” If we all independently made that decision, the consequence on this calculus is we are the worst people, morally speaking, who have ever lived. That doesn’t quite capture the moment, the experience or the intentions. We could do this thing without ever thinking about the implications of existential risk. If we didn’t have a phrase for this and we didn’t have people like ourselves talking about this is a problem, people could just be taken in by the overpopulation thesis.

That that’s really the thing that is destroying the world and what we need is some kind of Gaian reset, where the Earth reboots without us. Let’s just stop having kids and let nature reclaim the edges of the cities. You could see a kind of utopian environmentalism creating some dogma around that, where it was no one’s intention ever to create some kind of horrific crime. Yet, on this existential risk calculus, that’s what would have happened. It’s hard to think about the morality there when you talk about people deciding not to have kids and it would be the same catastrophic outcome.

Lucas Perry: That situation to me seems to be like looking over the possible moral landscape and seeing a mountain or not seeing a mountain, but there still being a mountain. Then you can have whatever kinds of intentions that you want, but you’re still missing it. From a purely consequentialist framework on this, I feel not so bad saying that this is probably one of the worst things that have ever happened.

Sam Harris: The asymmetry here between suffering and happiness still seems psychologically relevant. It’s not quite the worst thing that’s ever happened, but the best things that might have happened have been canceled. Granted, I think there’s a place to stand where you could think that is a horrible outcome, but again, it’s not the same thing as creating some hell and populating it.

Lucas Perry: I see what you’re saying. I’m not sure that I quite share the intuition about the asymmetry between suffering and well-being. I feel somewhat suspect about that, but that would be a huge tangent right now, I think. Now, one of the crucial things that you said was, for those that are not really compelled to care about the long-term future argument, if you don’t have the science fiction geek gene and are not compelled by moral philosophy, the essential way it seems to be that you’re able to compel people to care about global catastrophic and existential risk is to demonstrate how they’re very likely within this century.

And so their direct descendants, like their children or grandchildren, or even them, may live in a world that is very bad or they may die in some kind of a global catastrophe, which is terrifying. Do you see this as the primary way of leveraging human self-interest and feelings and emotions to make existential and global catastrophic risk salient and pertinent for the masses?

Sam Harris: It’s certainly half the story, and it might be the most compelling half. I’m not saying that we should be just worried about the downside because the upside also is something we should celebrate and aim for. The other side of the story is that we’ve made incredible progress. If you take someone like Steven Pinker and his big books of what is often perceived as happy talk. He’s pointing out all of the progress, morally and technologically and at the level of public health.

It’s just been virtually nothing but progress. There’s no point in history where you’re luckier to live than in the present. That’s true. I think that the thing that Steve’s story conceals, or at least doesn’t spend enough time acknowledging, is that the risk of things going terribly wrong is also increasing. It was also true a hundred years ago that it would have been impossible for one person or a small band of people to ruin life for everyone else.

Now that’s actually possible. Just imagine if this current pandemic were an engineered virus, more like a lethal form of measles. It might take five people to create that and release it. Here we would be locked down in a truly terrifying circumstance. The risk is ramped up. I think we just have to talk about both sides of it. There is no limit to how beautiful life could get if we get our act together. Take an argument of the sort that David Deutsch makes about the power of knowledge.

Every problem has a solution born of a sufficient insight into how things work, i.e. knowledge, unless the laws of physics rules it out. If it’s compatible with the laws of physics, knowledge can solve the problem. That’s virtually a blank check with reality that we could live to cash, if we don’t kill ourselves in the process. Again, as the upside becomes more and more obvious, the risk that we’re going to do something catastrophically stupid is also increasing. The principles here are the same. The only reason why we’re talking about existential risk is because we have made so much progress. Without the progress, there’d be no way to make a sufficiently large mistake. It really is two sides of the coin of increasing knowledge and technical power.

Lucas Perry: One thing that I wanted to throw in here in terms of the kinetics of long-termism and emotional saliency, it would be stupidly optimistic I think, to think that everyone could become selfless bodhisattvas. In terms of your interest, the way in which you promote meditation and mindfulness, and your arguments against the conventional, experiential and conceptual notion of the self, for me at least, has dissolved much of the barriers which would hold me from being emotionally motivated from long-termism.

Now, that itself I think, is another long conversation. When your sense of self is becoming nudged, disentangled and dissolved in new ways, the idea that it won’t be you in the future, or the idea that the beautiful dreams that Dyson spheres will be having in a billion years are not you, that begins to relax a bit. That’s probably not something that is helpful for most people, but I do think that it’s possible for people to adopt and for meditation, mindfulness and introspection to lead to this weakening of sense of self, which then also opens one’s optimism, and compassion, and mind towards the long-termist view.

Sam Harris: That’s something that you get from reading Derek Parfit’s work. The paradoxes of identity that he so brilliantly framed and tried to reason through yield something like what you’re talking about. It’s not so important whether it’s you, because this notion of you is in fact, paradoxical to the point of being impossible to pin down. Whether the you that woke up in your bed this morning is the same person who went to sleep in it the night before, that is problematic. Yet there’s this fact of some degree of psychological continuity.

The basic fact experientially is just, there is consciousness and its contents. The only place for feelings, and perceptions, and moods, and expectations, and experience to show up is in consciousness, whatever it is and whatever its connection to the physics of things actually turns out to be. There’s just consciousness. The question of where it appears is a genuinely interesting one philosophically, and intellectually, and scientifically, and ultimately morally.

Because if we build conscious robots or conscious computers and build them in a way that causes them to suffer, we’ve just done something terrible. We might do that inadvertently if we don’t know how consciousness arises based on information processing, or whether it does. It’s all interesting terrain to think about. If the lights are still on a billion years from now, and the view of the universe is unimaginably bright, and interesting and beautiful, and all kinds of creative things are possible by virtue of the kinds of minds involved, that will be much better than any alternative. That’s certainly how it seems to me.

Lucas Perry: I agree. Some things here that ring true seem to be, you always talk about how there’s only consciousness and its contents. I really like the phrase, “Seeing from nowhere.” That usually is quite motivating for me, in terms of the arguments against the conventional conceptual and experiential notions of self. There just seems to be instantiations of consciousness intrinsically free of identity.

Sam Harris: Two things to distinguish here. There’s the philosophical, conceptual side of the conversation, which can show you that things like your concept of a self, or certainly your concept of a self that could have free will that, that doesn’t make a lot of sense. It doesn’t make sense when mapped onto physics. It doesn’t make sense when looked for neurologically. Any way you look at it, it begins to fall apart. That’s interesting, but again, it doesn’t necessarily change anyone’s experience.

It’s just a riddle that can’t be solved. Then there’s the experiential side which you encounter more in things like meditation, or psychedelics, or sheer good luck where you can experience consciousness without the sense that there’s a subject or a self in the center of it appropriating experiences. Just a continuum of experience that doesn’t have structure in the normal way. What’s more, that’s not a problem. In fact, it’s the solution to many problems.

A lot of the discomfort you have felt psychologically goes away when you punch through to a recognition that consciousness is just the space in which thoughts, sensations and emotions continually appear, change and vanish. There’s no thinker authoring the thoughts. There’s no experiencer in the middle of the experience. It’s not to say you don’t have a body. There’s every sign that you have a body is still appearing. There’s sensations of tension, warmth, pressure and movement.

There are sights, there are sounds but again, everything is simply an appearance in this condition, which I’m calling consciousness for lack of a better word. There’s no subject to whom it all refers. That can be immensely freeing to recognize, and that’s a matter of a direct change in one’s experience. It’s not a matter of banging your head against the riddles of Derek Parfit or any other way of undermining one’s belief in personal identity or the reification of a self.

Lucas Perry: A little bit earlier, we talked a little bit about the other side of the existential risk coin. Now, the other side of that is this existential hope, we like to call at The Future of Life Institute. We’re not just a doom and gloom society. It’s also about how the future can be unimaginably good if we can get our act together and apply the appropriate wisdom to manage and steward our technologies with wisdom and benevolence in mind.

Pivoting in here and reflecting a little bit on the implications of some of this no self conversation we’ve been having for global priorities, the effective altruism community has narrowed down on three of these global priorities as central issues of consideration, existential risk, global poverty and animal suffering. We talked a bunch about existential risk already. Global poverty is prolific, and many of us live in quite nice and abundant circumstances.

Then there’s animal suffering, which can be thought of as in two categories. One being factory farmed animals, where we have billions upon billions of animals being born into miserable conditions and being slaughtered for sustenance. Then we also have wild animal suffering, which is a bit more esoteric and seems like it’s harder to get any traction on helping to alleviate. Thinking about these last two points, global poverty and animal suffering, what is your perspective on these?

I find the lack of willingness for people to empathize and be compassionate towards animal suffering to be quite frustrating, as well as global poverty, of course. If you view the perspective of no self as potentially being informative or helpful for leveraging human compassion and motivation to help other people and to help animals. One quick argument here that comes from the conventional view of self, so isn’t strictly true or rational, but is motivating for me, is that I feel like I was just born as me and then I just woke up one day as Lucas.

I, referring to this conventional and experientially illusory notion that I have of myself, this convenient fiction that I have. Now, you’re going to die and you could wake up as a factory farmed animal. Surely there are those billions upon billions of instantiations of consciousness that are just going through misery. If the self is an illusion then there are selfless chicken and cow experiences of enduring suffering. Any thoughts or reactions you have to global poverty, animal suffering and what I mentioned here?

Sam Harris: I guess the first thing to observe is that again, we are badly set up to prioritize what should be prioritized and to have the emotional response commensurate with what we could rationally understand is so. We have a problem of motivation. We have a problem of making data real. This has been psychologically studied, but it’s just manifest in oneself and in the world. We care more about the salient narrative that has a single protagonist than we do about the data on, even human suffering.

The classic example here is one little girl falls down a well, and you get wall to wall news coverage. All the while there could be a genocide or a famine killing hundreds of thousands of people, and it doesn’t merit more than five minutes. One broadcast. That’s clearly a bug, not a feature morally speaking, but it’s something we have to figure out how to work with because I don’t think it’s going away. One of the things that the effective altruism philosophy has done, I think usefully, is that it has separated two projects which up until the emergence of effective altruism, I think were more or less always conflated.

They’re both valid projects, but one has much greater moral consequence. The fusion of the two is, the concern about giving and how it makes one feel. I want to feel good about being philanthropic. Therefore, I want to give to causes that give me these good feels. In fact, at the end of the day, the feeling I get from giving is what motivates me to give. If I’m giving in a way that doesn’t really produce that feeling, well, then I’m going to give less or give less reliably.

Even in a contemplative Buddhist context, there’s an explicit fusion of these two things. The reason to be moral and to be generous is not merely, or even principally, the effect on the world. The reason is because it makes you a better person. It gives you a better mind. You feel better in your own skin. It is in fact, more rewarding than being selfish. I think that’s true, but that doesn’t get at really, the important point here, which is we’re living in a world where the difference between having good and bad luck is so enormous.

The inequalities are so shocking and indefensible. The fact that I was born me and not born in some hell hole in the middle of a civil war soon to be orphaned, and impoverished and riddled by disease, I can take no responsibility for the difference in luck there. That difference is the difference that matters more than anything else in my life. What the effective altruist community has prioritized is, actually helping the most people, or the most sentient beings.

That is fully divorceable from how something makes you feel. Now, I think it shouldn’t be ultimately divorceable. I think we should recalibrate our feelings or struggle to, so that we do find doing the most good the most rewarding thing in the end, but it’s hard to do. My inability to do it personally, is something that I have just consciously corrected for. I’ve talked about this a few times on my podcast. When Will MacAskill came on my podcast and we spoke about these things, I was convinced at the end of the day, “Well, I should take this seriously.”

I recognize that fighting malaria by sending bed nets to people in sub-Saharan Africa is not a cause I find particularly sexy. I don’t find it that emotionally engaging. I don’t find it that rewarding to picture the outcome. Again, compared to other possible ways of intervening in human misery and producing some better outcome, it’s not the same thing as rescuing the little girl from the well. Yet, I was convinced that, as Will said on that podcast and as organizations like GiveWell attest, giving money to the Against Malaria Foundation was and remains one of the absolute best uses of every dollar to mitigate unnecessary death and suffering.

I just decided to automate my giving to the Against Malaria Foundation because I knew I couldn’t be trusted to wake up every day, or every month or every quarter, whatever it would be, and recommit to that project because some other project would have captured my attention in the meantime. I was either going to give less to it or not give at all, in the end. I’m convinced that we do have to get around ourselves and figure out how to prioritize what a rational analysis says we should prioritize and get the sentimentality out of it, in general.

It’s very hard to escape entirely. I think we do need to figure out creative ways to reformat our sense of reward. The reward we find in helping people has to begin to become more closely coupled to what is actually most helpful. Conversely, the disgust or horror we feel over bad outcomes should be more closely coupled to the worst things that happen. As opposed to just the most shocking, but at the end of the day, minor things. We’re just much more captivated by a sufficiently ghastly story involving three people than we are by the deaths of literally millions that happen some other way. These are bugs we have to figure out how to correct for.

Lucas Perry: I hear you. The person running in the burning building to save the child is sung as a hero, but if you are say, earning to give for example and write enough checks to save dozens of lives over your lifetime, that might not go recognized or felt in the same way.

Sam Harris: And also these are different people, too. It’s also true to say that someone who is psychologically and interpersonally not that inspiring, and certainly not a saint might wind up doing more good than any saint ever does or could. I don’t happen to know Bill Gates. He could be saint-like. I literally never met him, but I don’t get that sense that he is. I think he’s kind of a normal technologist and might be normally egocentric, concerned about his reputation and legacy.

He might be a prickly bastard behind closed doors. I don’t know, but he certainly stands a chance of doing more good than any person in human history at this point, just based on the checks he’s writing and his intelligent prioritization of his philanthropic efforts. There is an interesting uncoupling here where you could just imagine someone who might be a total asshole, but actually does more good than any army of Saints you could muster. That’s interesting. That just proves a point that a concern about real world outcomes is divorceable from the psychology that we tend to associate with doing good in the world. On the point of animal suffering, I share your intuitions there, although again, this is a little bit like climate change in that I think that the ultimate fix will be technological. It’ll be a matter of people producing the Impossible Burger squared that is just so good that no one’s tempted to eat a normal burger anymore, or something like Memphis Meats, which actually, I invested in.

I have no idea where it’s going as a company, but when I had its CEO on my podcast back in the day, Uma Valeti, I just thought, “This is fantastic to engineer actual meat without producing any animal suffering. I hope he can bring this to scale.” At the time, it was like an $18,000-meatball. I don’t know what it is now, but it’s that kind of thing that will close the door to the slaughterhouse more than just convincing billions of people about the ethics. It’s too difficult and the truth may not align with exactly what we want.

I’m going to reap the whirlwind of criticism from the vegan mafia here, but it’s just not clear to me that it’s easy to be a healthy vegan. Forget about yourself as an adult making a choice to be a vegan, raising vegan kids is a medical experiment on your kids of a certain sort and it’s definitely possible to screw it up. There’s just no question about it. If you’re not going to admit that, you’re not a responsible parent.

It is possible, it is by no means easier to raise healthy vegan kids than it is to raise kids who eat meat sometimes and that’s just a problem, right? Now, that’s a problem that has a technical solution, but there’s still diversity of opinion about what constitutes a healthy human diet even when all things are on the menu. We’re just not there yet. It’s unlikely to be just a matter of supplementing B12.

Then the final point you made does get us into a kind of, I would argue, a reductio ad absurdum of the whole project ethically when you’re talking about losing sleep over whether to protect the rabbits from the foxes out there in the wild. If you’re going to go down that path, and I will grant you, I wouldn’t want to trade places with a rabbit, and there’s a lot of suffering out there in the natural world, but if you’re going to try to figure out how to minimize the suffering of wild animals in relation to other wild animals then I think you are a kind of antinatalist with respect to the natural world. I mean, then it would be just better if these animals didn’t exist, right? Let’s just hit stop on the whole biosphere, if that’s the project.

Then there’s the argument that there are many more ways to suffer and to be happy as a sentient being. Whatever story you want to tell yourself about the promise of future humanity, it’s just so awful to be a rabbit or an insect that if an asteroid hit us and canceled everything, that would be a net positive.

Lucas Perry: Yeah. That’s an actual view that I hear around a bunch. I guess my quick response is as we move farther into the future, if we’re able to reach an existential situation which is secure and where there is flourishing and we’re trying to navigate the moral landscape to new peaks, it seems like we will have to do something about wild animal suffering. With AGI and aligned superintelligence, I’m sure there could be very creative solutions using genetic engineering or something. Our descendants will have to figure that out, whether they are just like, “Are wild spaces really necessary in the future and are wild animals actually necessary, or are we just going to use those resources in space to build more AI that would dream beautiful dreams?”

Sam Harris: I just think it may be, in fact, the case that nature is just a horror show. It is bad almost any place you could be born in the natural world, you’re unlucky to be a rabbit and you’re unlucky to be a fox. We’re lucky to be humans, sort of, and we can dimly imagine how much luckier we might get in the future if we don’t screw up.

I find it compelling to imagine that we could create a world where certainly most human lives are well worth living and better than most human lives ever were. Again, I follow Pinker in feeling that we’ve sort of done that already. It’s not to say that there aren’t profoundly unlucky people in this world, and it’s not to say that things couldn’t change in a minute for all of us, but life has gotten better and better for virtually everyone when you compare us to any point in the past.

If we get to the place you’re imagining where we have AGI that we have managed to align with our interests and we’re migrating into of spaces of experience that changes everything, it’s quite possible we will look back on the “natural world” and be totally unsentimental about it, which is to say, we could compassionately make the decision to either switch it off or no longer provide for its continuation. It’s like that’s just a bad software program that evolution designed and wolves and rabbits and bears and mice, they were all unlucky on some level.

We could be wrong about that, or we might discover something else. We might discover that intelligence is not all it’s cracked up to be, that it’s just this perturbation on something that’s far more rewarding. At the center of the moral landscape, there’s a peak higher than any other and it’s not one that’s elaborated by lots of ideas and lots of creativity and lots of distinctions, it’s just this great well of bliss that we actually want to fully merge with. We might find out that the cicadas were already there. I mean, who knows how weird this place is?

Lucas Perry: Yeah, that makes sense. I totally agree with you and I feel this is true. I also feel that there’s some price that is paid because there’s already some stigma around even thinking this. I think it’s a really early idea to have in terms of the history of human civilization, so people’s initial reaction is like, “Ah, what? Nature’s so beautiful and why would you do that to the animals?” Et cetera. We may come to find out that nature is just very net negative, but I could be wrong and maybe it would be around neutral or better than that, but that would require a more robust and advanced science of consciousness.

Just hitting on this next one fairly quickly, effective altruism is interested in finding new global priorities and causes. They call this “Cause X,” something that may be a subset of existential risk or something other than existential risk or global poverty or animal suffering probably still just has to do with the suffering of sentient beings. Do you think that a possible candidate for Cause X would be machine suffering or the suffering of other non-human conscious things that we’re completely unaware of?

Sam Harris: Yeah, well, I think it’s a totally valid concern. Again, it’s one of these concerns that’s hard to get your moral intuitions tuned up to respond to. People have a default intuition that a conscious machine is impossible, that substrate independence, on some level, is impossible, they’re making an assumption without ever doing it explicitly… In fact, I think most people would explicitly deny thinking this, but it is implicit in what they then go on to think when you pose the question of the possibility of suffering machines and suffering computers.

That just seems like something that never needs to be worried about and yet the only way to close the door to worrying about it is to assume that consciousness is totally substrate-dependent and that we would never build a machine that could suffer because we’re building machines out of some other material. If we built a machine out of biological neurons, well, then, then we might be up for condemnation morally because we’ve taken an intolerable risk analogous to create some human-chimp hybrid or whatever. It’s like obviously, that thing’s going to suffer. It’s an ape of some sort and now it’s in a lab and what sort of monster would do that, right? We would expect the lights to come on in a system of that sort.

If consciousness is the result of information processing on some level, and again, that’s an “if,” we’re not sure that’s the case, and if information processing is truly substrate-independent, and that seems like more than an “if” at this point, we know that’s true, then we could inadvertently build conscious machines. And then the question is: What is it like to be those machines and are they suffering? There’s no way to prevent that on some level.

Certainly, if there’s any relationship between consciousness and intelligence, if building more and more intelligent machines is synonymous with increasing the likelihood that the lights will come on experientially, well, then we’re clearly on that path. It’s totally worth worrying about, but it’s again, judging from what my own mind is like and what my conversations with other people suggest, it seems very hard to care about for people. That’s just another one of these wrinkles.

Lucas Perry: Yeah. I think a good way of framing this is that humanity has a history of committing moral catastrophes because of bad incentives and they don’t even realize how bad the thing is that they’re doing, or they just don’t really care or they rationalize it, like subjugation of women and slavery. We’re in the context of human history and we look back at these people and see them as morally abhorrent.

Now, the question is: What is it today that we’re doing that’s morally abhorrent? Well, I think factory farming is easily one contender and perhaps human selfishness that leads to global poverty and millions of people drowning in shallow ponds is another one that we’ll look back on. With just some foresight towards the future, I agree that machine suffering is intuitively and emotionally difficult to empathize with if your sci-fi gene isn’t turned on. It could be the next thing.

Sam Harris: Yeah.

Lucas Perry: I’d also like to pivot here into AI alignment and AGI. In terms of existential risk from AGI or transformative AI systems, do you have thoughts on public intellectuals who are skeptical of existential risk from AGI or superintelligence? You had a talk about AI risk and I believe you got some flak from the AI community about that. Elon Musk was just skirmishing with the head of AI at Facebook, I think. What is your perspective about the disagreement and confusion here?

Sam Harris: It comes down to a failure of imagination on the one hand and also just bad argumentation. No sane person who’s concerned about this is concerned because they think it’s going to happen this year or next year. It’s not a bet on how soon this is going to happen. For me, it certainly isn’t a bet on how soon it’s going to happen. It’s just a matter of the implications of continually making progress in building more and more intelligent machines. Any progress, it doesn’t have to be Moore’s law, it just has to be continued progress, will ultimately deliver us into relationship with something more intelligent than ourselves.

To think that that is farfetched or is not likely to happen or can’t happen is to assume some things that we just can’t assume. It’s to assume that substrate independence is not in the cards for intelligence. Forget about consciousness. I mean, consciousness is orthogonal to this question. I’m not suggesting that AGI need be conscious, it just needs to be more competent than we are. We already know that our phones are more competent as calculators than we are, they’re more competent chess players than we are. You just have to keep stacking cognitive-information-processing abilities on that and making progress, however incremental.

I don’t see how anyone can be assuming substrate dependence for really any of the features of our mind apart from, perhaps, consciousness. Take the top 200 things we do cognitively, consciousness aside, just as a matter of sheer information-processing and behavioral control and power to make decisions and you start checking those off, those have to be substrate independent: facial recognition, voice recognition, we can already do that in silico. It’s just not something you need meat to do.

We’re going to build machines that get better and better at all of these things and ultimately, they will pass the Turing test and ultimately, it will be like chess or now Go as far as the eye can see, where it will be in relationship to something that is better than we are at everything that we have prioritized, every human competence we have put enough priority in that we took the time to build it into our machines in the first place: theorem-proving in mathematics, engineering software programs. There is no reason why a computer will ultimately not be the best programmer in the end, again, unless you’re assuming that there’s something magical about doing this in meat. I don’t know anyone who’s assuming that.

Arguing about the time horizon is a non sequitur, right? No one is saying that this need happen soon to ultimately be worth thinking about. We know that whatever the time horizon is, it can happen suddenly. We have historically been very bad at predicting when there will be a breakthrough. This is a point that Stuart Russell makes all the time. If you look at what Rutherford said about the nuclear chain reaction being a pipe dream, it wasn’t even 24 hours before Leo Szilard committed the chain reaction to paper and had the relevant breakthrough. We know we can make bad estimates about the time horizon, so at some point, we could be ambushed by a real breakthrough, which suddenly delivers exponential growth in intelligence.

Then there’s a question of just how quickly that could unfold and whether this something like an intelligence explosion. That’s possible. We can’t know for sure, but you need to find some foothold to doubt whether these things are possible and the footholds that people tend to reach for are either nonexistent or they’re non sequiturs.

Again, the time horizon is irrelevant and yet the time horizon is the first thing you hear from people who are skeptics about this: “It’s not going to happen for a very long time.” Well, I mean, Stuart Russell’s point here, which is, again, it’s just a reframing, but in the persuasion business, reframing is everything. The people who are consoled by this idea that this is not going to happen for 50 years wouldn’t be so consoled if we receive a message from an alien civilization which said, “People of Earth, we will arrive on your humble planet in 50 years. Get ready.”

If that happened, we would be prioritizing our response to that moment differently than the people who think it’s going to take 50 years for us to build AGI are prioritizing their response to what’s coming. We would recognize a relationship with something more powerful than ourselves is in the often. It’s only reasonable to do that on the assumption that we will continue to make progress.

The point I made in my TED Talk is that the only way to assume we’re not going to continue to make progress is to be convinced of a very depressing thesis. The only way we wouldn’t continue to make progress is if we open the wrong door of the sort that you and I have been talking about in this conversation, if we invoke some really bad roll of the dice in terms of existential risk or catastrophic civilizational failure, and we just find ourselves unable to build better and better computers. I mean, that’s the only thing that would cause us to be unable to do that. Given the power and value of intelligent machines, we will build more and more intelligent machines at almost any cost at this point, so a failure to do it would be a sign that something truly awful has happened.

Lucas Perry: Yeah. From my perspective, the people that are skeptical of substrate independence, I wouldn’t say that those are necessarily AI researchers. Those are regular persons or laypersons who are not computer scientists. I think that’s motivated by mind-body dualism, where one has a conventional and experiential sense of the mind as being non-physical, which may be motivated by popular religious beliefs, but when we get into the area of actual AI researchers, for them, it seems to either be like they’re attacking some naive version of the argument or a straw man or something

Sam Harris: Like robots becoming spontaneously malevolent?

Lucas Perry: Yeah. It’s either that, or they think that the alignment problem isn’t as hard as it is. They have some intuition, like why the hell would we even release systems that weren’t safe? Why would we not make technology that served us or something? To me, it seems that when there are people from like the mainstream machine-learning community attacking AI alignment and existential risk considerations from AI, it seems like they just don’t understand how hard the alignment problem is.

Sam Harris: Well, they’re not taking seriously the proposition that what we will have built are truly independent minds more powerful than our own. If you actually drill down on what that description means, it doesn’t mean something that is perfectly enslaved by us for all time, I mean, because that is by definition something that couldn’t be more intelligent across the board than we are.

The analogy I use is imagine if dogs had invented us to protect their interests. Well, so far, it seems to be going really well. We’re clearly more intelligent than dogs, they have no idea what we’re doing or thinking about or talking about most of the time, and they see us making elaborate sacrifices for their wellbeing, which we do. I mean, the people who own dogs care a lot about them and make, you could argue, irrational sacrifices to make sure they’re happy and healthy.

But again, back to the pandemic, if we recognize that we had a pandemic that was going to kill the better part of humanity and it was jumping from dogs to people and the only way to stop this is to kill all the dogs, we would kill all the dogs on a Thursday. There’d be some holdouts, but they would lose. The dog project would be over and the dogs would never understand what happened.

Lucas Perry: But that’s because humans aren’t perfectly aligned with dog values.

Sam Harris: But that’s the thing: Maybe it’s a solvable problem, but it’s clearly not a trivial problem because what we’re imagining are minds that continue to grow in power and grow in ways that by definition we can’t anticipate. Dogs can’t possibly anticipate where we will go next, what we will become interested in next, what we will discover next, what we’ll prioritize next. If you’re not imagining minds so vast that we can’t capture their contents ourselves, you’re not talking about the AGI that the people who are worried about alignment are talking about.

Lucas Perry: Maybe this is like a little bit of a nuanced distinction between you or I, but I think that that story that you’re developing there seems to assume that the utility function or the value learning or the objective function of the systems that we’re trying to align with human values is dynamic. It may be the case that you can build a really smart alien mind and it might become super-intelligent, but there are arguments that maybe you could make its alignment stable.

Sam Harris: That’s the thing we have to hope for, right? I’m not a computer scientist, so as far as the doability of this, that’s something I don’t have good intuitions about, but Stuart Russell’s argument that we would need a system whose ultimate value is to more and more closely approximate our current values that would continually, no matter how much its intelligence escapes our own, it would continually remain available to the conversation with us where we say, “Oh, no, no. Stop doing that. That’s not what we want.” That would be the most important message from its point of view, no matter how vast its mind got.

Maybe that’s doable, right, but that’s the kind of thing that would have to be true for the thing to remain completely aligned to us because the truth is we don’t want it aligned to who we used to be and we don’t want it aligned to the values of the Taliban. We want to grow in moral wisdom as well and we want to be able to revise our own ethical codes and this thing that’s smarter than us presumably could help us do that, provided it doesn’t just have its own epiphanies which cancel the value of our own or subvert our own in a way that we didn’t foresee.

If it really has our best interest at heart, but our best interests are best conserved by it deciding to pull the plug on everything, well, then we might not see the wisdom of that. I mean, it might even be the right answer. Now, this is assuming it’s conscious. We could be building something that is actually morally more important than we are.

Lucas Perry: Yeah, that makes sense. Certainly, eventually, we would want it to be aligned with some form of idealized human values and idealized human meta preferences over how value should change and evolve into the deep future. This is known, I think, as “ambitious value learning” and it is the hardest form of value learning. Maybe we can make something safe without doing this level of ambitious value learning, but something like that may be deeper in the future.

Now, as we’ve made moral progress throughout history, we’ve been expanding our moral circle of consideration. In particular, we’ve been doing this farther into space, deeper into time, across species, and potentially soon, across substrates. What do you see as the central way of continuing to expand our moral circle of consideration and compassion?

Sam Harris: Well, I just think we have to recognize that things like distance in time and space and superficial characteristics, like whether something has a face, much less a face that can make appropriate expressions or a voice that we can relate to, none of these things have moral significance. The fact that another person is far away from you in space right now shouldn’t fundamentally affect how much you care whether or not they’re being tortured or whether they’re starving to death.

Now, it does. We know it does. People are much more concerned about what’s happening on their doorstep, but I think proximity, if it has any weight at all, it has less and less weight the more our decisions obviously affect people regardless of separation and space, but the more it becomes truly easy to help someone on another continent because you can just push a button in your browser, then you’re caring less about them is clearly a bug. And so it’s just noticing that the things that attenuate our compassion tend to be things that for evolutionary reasons we’re designed to discount in this way, but at the level of actual moral reasoning about a global civilization it doesn’t make any sense and it prevents us from solving the biggest problems.

Lucas Perry: Pivoting into ethics more so now. I’m not sure if this is the formal label that you would use but your work on the moral landscape lands you pretty much it seems in the moral realism category.

Sam Harris: Mm-hmm (affirmative).

Lucas Perry: You’ve said something like, “Put your hand in fire to know what bad is.” That seems to disclose or seems to argue about the self intimating nature of suffering about how it’s clearly bad. If you don’t believe me, go and do the suffering things. From other moral realists who I’ve talked to and who argued for moral realism, like Peter Singer, they make similar arguments. What view or theory of consciousness are you most partial to? And how does this inform this perspective about the self intimating nature of suffering as being a bad thing?

Sam Harris: Well, I’m a realist with respect to morality and consciousness in the sense that I think it’s possible not to know what you’re missing. So if you’re a realist, the property that makes the most sense to me is that there are facts about the world that are facts whether or not anyone knows them. It is possible for everyone to be wrong about something. We could all agree about X and be wrong. That’s the realist position as opposed to pragmatism or some other variant, where it’s all just a matter, it’s all a language game, and the truth value of a statement is just the measure of the work it does in conversation. So with respect to consciousness, I’m a realist in the sense that if a system is conscious, if a cricket is conscious, if a sea cucumber is conscious, they’re conscious whether we know it or not. For the purposes of this conversation, let’s just decide that they’re not conscious, the lights are not on in those systems.

Well, that’s a claim that we could believe, we could all believe it, but we could be wrong about it. And so the facts exceed our experience at any given moment. And so it is with morally salient facts, like the existence of suffering. If a system can be conscious whether I know it or not a system can be suffering whether I know it or not. And that system could be me in the future or in some counterfactual state. I could think I’m doing the right thing by doing X. But the truth is I would have been much happier had I done Y and I’ll never know that. I was just wrong about the consequences of living in a certain way. That’s what realism on my view entails. So the way this relates to questions of morality and good and evil and right and wrong, this is back to my analogy of the moral landscape, I think morality really is a navigation problem. There are possibilities of experience in this universe and we don’t even need the concept of morality, we don’t need the concept of right and wrong and good and evil really.

That’s shorthand for, in my view, the way we should talk about the burden that’s on us in each moment to figure out what we should do next. Where should we point ourselves across this landscape of mind and possible minds? And knowing that it’s possible to move in the wrong direction, and what does it mean to be moving in the wrong direction? Well, it’s moving in a direction where everything is getting worse and worse and everything that was good a moment ago is breaking down to no good end. You could conceive of moving down a slope on the moral landscape only to ascend some higher peak. That’s intelligible to me that we might have to all move in the direction that seems to be making things worse but it is a sacrifice worth making because it’s the only way to get to something more beautiful and more stable.

I’m not saying that’s the world we’re living in, but it certainly seems like a possible world. But this just doesn’t seem open to doubt. There’s a range of experience on offer. And, on the one end, it’s horrific and painful and all the misery is without any silver lining, right? It’s not like we learn a lot from this ordeal. No, it just gets worse and worse and worse and worse and then we die, and I call that the worst possible misery for everyone. Alright so, the worst possible misery for everyone is bad if anything is bad, if the word bad is going to mean anything, it has to apply to the worst possible misery for everyone. But now some people come in and think they’re doing philosophy when they say things like, “Well, who’s to say the worst possible misery for everyone is bad?” Or, “Should we avoid the worst possible misery for everyone? Can you prove that we should avoid it?” And I actually think those are unintelligible noises that they’re making.

You can say those words, I don’t think you can actually mean those words. I have no idea what that person actually thinks they’re saying. You can play a language game like that but when you actually look at what the words mean, “the worst possible misery for everyone,” to then say, “Well, should we avoid it?” In a world where you should do anything, where the word should make sense, there’s nothing that you should do more than avoid the worst possible misery for everyone. By definition, it’s more fundamental than the concept of should. What I would argue is if you’re hung up on the concept of should, and you’re taken in by Hume’s flippant and ultimately misleading paragraph on, “You can’t get an ought from an is,” you don’t need oughts then. There is just this condition of is. There’s a range of experience on offer, and the one end it is horrible, on the other end, it is unimaginably beautiful.

And we clearly have a preference for one over the other, if we have a preference for anything. There is no preference more fundamental than escaping the worst possible misery for everyone. If you doubt that, you’re just not thinking about how bad things can get. It’s incredibly frustrating. In this conversation, you’re hearing the legacy of the frustration I’ve felt in talking to otherwise smart and well educated people who think they’re on interesting philosophical ground in doubting whether we should avoid the worst possible misery for everyone. Or that it would be good to avoid it, or perhaps it’s intelligible to have other priorities. And, again, I just think that they’re not understanding the words “worst possible misery and everyone”, they’re not letting those words and land in language cortex. And if they do, they’ll see that there is no other place to stand where you could have other priorities.

Lucas Perry: Yeah. And my brief reaction to that is, I still honestly feel confused about this. So maybe I’m in the camp of frustrating people. I can imagine other evolutionary timelines where there are minds that just optimize for the worst possible misery for everyone, just because in mind space those minds are physically possible.

Sam Harris: Well, that’s possible. We can certainly create a paperclip maximizer that is just essentially designed to make every conscious being suffer as much as it can. And that would be especially easy to do provided that intelligence wasn’t conscious. If it’s not a matter of its suffering, then yeah, we could use AGI to make things awful for everyone else. You could create a sadistic AGI that wanted everyone else to suffer and it derived immense pleasure from that.

Lucas Perry: Or immense suffering. I don’t see anything intrinsically motivating about suffering as navigating a mind necessarily away from it. Computationally, I can imagine a mind just suffering as much as possible and spreads that as much as possible. And maybe the suffering is bad in some objective sense, given consciousness realism, and that that was disclosing the intrinsic valence of consciousness in the universe. But the is-ought distinction there still seems confusing to me. Yes, suffering is bad and maybe the worst possible misery for everyone is bad, but that’s not universally motivating for all possible minds.

Sam Harris: The usual problem here is, it’s easy for me to care about my own suffering, but why should I care about the suffering of others? That seems to be the ethical stalemate that people worry about. My response there is that it doesn’t matter. You can take the view from above there and you can just say, “The universe would be better if all the sentient beings suffered less and it would be worse if they suffered more.” And if you’re unconvinced by that, you just have to keep turning the dial to separate those two more and more and more and more so that you get to the extremes. If any given sentient being can’t be moved to care about the experience of others, well, that’s one sort of world, that’s not a peak on the moral landscape. That will be a world where beings are more callous than they would otherwise be in some other corner of the universe. And they’ll bump into each other more and they’ll be more conflict and they’ll fail to cooperate in certain ways that would have opened doors to positive experiences that they now can’t have.

And you can try to use moralizing language about all of this and say, “Well, you still can’t convince me that I should care about people starving to death in Somalia.” But the reality is an inability to care about that has predictable consequences. If enough people can’t care about that then certain things become impossible and those things, if they were possible, lead to good outcomes that if you had a different sort of mind, you would enjoy. So all of this bites its own tail in an interesting way when you imagine being able to change a person’s moral intuitions. And then the question is, well, should you change those intuitions? Would it be good to change your sense of what is good? That question has an answer on the moral landscape. It has an answer when viewed as a navigation problem.

Lucas Perry: Right. But isn’t the assumption there that if something leads to a good world, then you should do it?

Sam Harris: Yes. You can even drop your notion of should. I’m sure it’s finite, but a functionally infinite number of worlds on offer and there’s ways to navigate into those spaces. And there are ways to fail to navigate into those spaces. There are ways to try and fail, and worse still, there are ways to not know what you’re missing, to not even know where you should be pointed on this landscape, which is to say, you need to be a realist here. There are experiences that are better than any experience that you are going to have and you are never going to know about them, possible experiences. And granting that, you don’t need a concept of should, should is just shorthand for how we speak with one another and try to admonish one another to be better in the future in order to cooperate better or to realize different outcomes. But it’s not a deep principle of reality.

What is a deep principle of reality is consciousness and its possibilities. Consciousness is the one thing that can’t be an illusion. Even if we’re in a simulation, even if we’re brains in vats, even if we’re confused about everything, something seems to be happening, and that seeming is the fact of consciousness. And almost as rudimentary as that is the fact that within this space of seemings, again, we don’t know what the base layer of reality is, we don’t know if our physics is the real physics, we could be confused, this could be a dream, we could be confused about literally everything except that in this space of seemings there appears to be a difference between things getting truly awful to no apparent good end and things getting more and more sublime.

And there’s potentially even a place to stand where that difference isn’t so captivating anymore. Certainly, there are Buddhists who would tell you that you can step off that wheel of opposites, ultimately. But even if you buy that, that is some version of a peak on my moral landscape. That is a contemplative peak where the difference between agony and ecstasy is no longer distinguishable because what you are then aware of is just that consciousness is intrinsically free of its content and no matter what its possible content could be. If someone can stabilize that intuition, more power to them, but then that’s the thing you should do, just to bring it back to the conventional moral framing.

Lucas Perry: Yeah. I agree with you. I’m generally a realist about consciousness and still do feel very confused, not just because of reasons in this conversation, but just generally about how causality fits in there and how it might influence our understanding of the worst possible misery for everyone being a bad thing. I’m also willing to go that far to accept that as objectively a bad thing, if bad means anything. But then I still get really confused about how that necessarily fits in with, say, decision theory or “shoulds” in the space of possible minds and what is compelling to who and why?

Sam Harris: Perhaps this is just semantic. Imagine all these different minds that have different utility functions. The paperclip maximizer wants nothing more than paperclips. And anything that reduces paperclips is perceived as a source of suffering. It has a disutility. If you have any utility function, you have this liking and not liking component provided your sentient. That’s what it is to be motivated consciously. For me, the worst possible misery for everyone is a condition where, whatever the character of your mind, every sentient mind is put in the position of maximal suffering for it. So some things like paperclips and some things hate paperclips. If you hate paperclips, we give you a lot of paperclips. If you like paperclips, we take away all your paperclips. If that’s your mind, we tune your corner of the universe to that torture chamber. You can be agnostic as to what the actual things are that make something suffer. It’s just suffering is by definition the ultimate frustration of that mind’s utility function.

Lucas Perry: Okay. I think that’s a really, really important crux and crucial consideration between us and a general point of confusion here. Because that’s the definition of what suffering is or what it means. I suspect that those things may be able to come apart. So, for you, maximum disutility and suffering are identical, but I guess I could imagine a utility function being separate or inverse from the hedonics of a mind. Maybe the utility function, which is purely a computational thing, is getting maximally satisfied, maximizing suffering everywhere, and the mind that is implementing that suffering is just completely immiserated while doing it. But the utility function, which is different and inverse from the experience of the thing, is just getting satiated and so the machine keeps driving towards maximum-suffering-world.

Sam Harris: Right, but there’s either something that is liked to be satiated in that way or there isn’t right now. If we’re talking about real conscious society, we’re talking about some higher order satisfaction or pleasure that is not suffering by my definition. We have this utility function ourselves. I mean when you take somebody who decides to climb to the summit of Mount Everest where the process almost every moment along the way is synonymous with physical pain and intermittent fear of death, torture by another name. But the whole project is something that they’re willing to train for, sacrifice for, dream about, and then talk about for the rest of their lives, and at the end of the day might be in terms of their conscious sense of what it was like to be them, the best thing they ever did in their lives.

That is this sort of bilayered utility function you’re imagining, whereas if you could just experience sample what it’s like to be in the death zone on Everest, it really sucks and if imposed on you for any other reason, it would be torture. But given the framing, given what this person believes about what they’re doing, given the view out their goggles, given their identity as a mountain climber, this is the best thing they’ve ever done. You’re imagining some version of that, but that fits in my view on the moral landscape. That’s not the worst possible misery for anyone. The source of satisfaction that is deeper than just bodily, sensory pleasure every moment of the day, or at least it seems to be for that person at that point in time. They could be wrong about that. There could be something better. They don’t know what they’re missing. It’s actually much better to not care about mountain climbing.

The truth is, your aunt is a hell of a lot happier than Sir Edmund Hillary was and Edmund Hillary was never in a position to know it because he was just so into climbing mountains. That’s where the realism comes in, in terms of you not knowing what you’re missing. But I just see any ultimate utility function, if it’s accompanied by consciousness, it can’t define itself as the ultimate frustration of its aims if its aims are being satisfied.

Lucas Perry: I see. Yeah. So this just seems to be a really important point around hedonics and computation and utility function and what drives what. So, wrapping up here, I think I would feel defeated if I let you escape without maybe giving a yes or no answer to this last question. Do you think that bliss and wellbeing can be mathematically defined?

Sam Harris: That is something I have no intuitions about it. I’m not enough of a math head to think in those terms. If we mathematically understood what it meant for us neurophysiologically in our own substrate, well then, I’m sure we can characterize it for creatures just like us. I think substrate independence makes it something that’s hard to functionally understand in new systems and it’ll just pose problems of our just knowing what it’s like to be something that on the outside seems to be functioning much like we do but is organized in a very different way. But yeah, I don’t have any intuitions around that one way or the other.

Lucas Perry: All right. And so pointing towards your social media or the best places to follow you, where should we do that?

Sam Harris: My website is just samharris.org and I’m SamHarrisorg without the dot on Twitter, and you can find anything you want about me on my website, certainly.

Lucas Perry: All right, Sam. Thanks so much for coming on and speaking about this wide range of issues. You’ve been deeply impactful in my life since I guess about high school. I think you probably partly at least motivated my trip to Nepal, where I overlooked the Pokhara Lake and reflected on your terrifying acid trip there.

Sam Harris: That’s hilarious. That’s in my book Waking Up, but it’s also on my website and it’s also I think I read it on the Waking Up App and it’s in a podcast. It’s also on Tim Ferriss’ podcast. But anyway, that acid trip was detailed in this piece called Drugs and The Meaning of Life. That’s hilarious. I haven’t been back to Pokhara since, so you’ve seen that lake more recently than I have.

Lucas Perry: So yeah, you’ve contributed much to my intellectual and ethical development and thinking, and for that, I have tons of gratitude and appreciation. And thank you so much for taking the time to speak with me about these issues today.

Sam Harris: Nice. Well, it’s been a pleasure, Lucas. And all I can say is keep going. You’re working on very interesting problems and you’re very early to the game, so it’s great to see you doing it.

Lucas Perry: Thanks so much, Sam.

FLI Podcast: On the Future of Computation, Synthetic Biology, and Life with George Church

Progress in synthetic biology and genetic engineering promise to bring advancements in human health sciences by curing disease, augmenting human capabilities, and even reversing aging. At the same time, such technology could be used to unleash novel diseases and biological agents which could pose global catastrophic and existential risks to life on Earth. George Church, a titan of synthetic biology, joins us on this episode of the FLI Podcast to discuss the benefits and risks of our growing knowledge of synthetic biology, its role in the future of life, and what we can do to make sure it remains beneficial. Will our wisdom keep pace with our expanding capabilities?

Topics discussed in this episode include:

  • Existential risk
  • Computational substrates and AGI
  • Genetics and aging
  • Risks of synthetic biology
  • Obstacles to space colonization
  • Great Filters, consciousness, and eliminating suffering

You can take a survey about the podcast here

Submit a nominee for the Future of Life Award here

 

Timestamps: 

0:00 Intro

3:58 What are the most important issues in the world?

12:20 Collective intelligence, AI, and the evolution of computational systems

33:06 Where we are with genetics

38:20 Timeline on progress for anti-aging technology

39:29 Synthetic biology risk

46:19 George’s thoughts on COVID-19

49:44 Obstacles to overcome for space colonization

56:36 Possibilities for “Great Filters”

59:57 Genetic engineering for combating climate change

01:02:00 George’s thoughts on the topic of “consciousness”

01:08:40 Using genetic engineering to phase out voluntary suffering

01:12:17 Where to find and follow George

 

Citations: 

George Church’s Twitter and website

 

This podcast is possible because of the support of listeners like you. If you found this conversation to be meaningful or valuable consider supporting it directly by donating at futureoflife.org/donate. Contributions like yours make these conversations possible.

All of our podcasts are also now on Spotify and iHeartRadio! Or find us on SoundCloudiTunesGoogle Play and Stitcher.

You can listen to the podcast above or read the transcript below. 

Lucas Perry: Welcome to the Future of Life Institute Podcast. I’m Lucas Perry. Today we have a conversation with Professor George Church on existential risk, the evolution of computational systems, synthetic-bio risk, aging, space colonization, and more. We’re skipping the AI Alignment Podcast episode this month, but I intend to have it resume again on the 15th of June. Some quick announcements for those unaware, there is currently a live survey that you can take about the FLI and AI Alignment Podcasts. And that’s a great way to voice your opinion about the podcast, help direct its evolution, and provide feedback for me. You can find a link for that survey on the page for this podcast or in the description section of wherever you might be listening. 

The Future of Life Institute is also in the middle of its search for the 2020 winner of the Future of Life Award. The Future of Life Award is a $50,000 prize that we give out to an individual who, without having received much recognition at the time of their actions, has helped to make today dramatically better than it may have been otherwise. The first two recipients of the Future of Life Institute award were Vasili Arkhipov and Stanislav Petrov, two heroes of the nuclear age. Both took actions at great personal risk to possibly prevent an all-out nuclear war. The third recipient was Dr. Matthew Meselson, who spearheaded the international ban on bioweapons. Right now, we’re not sure who to give the 2020 Future of Life Award to. That’s where you come in. If you know of an unsung hero who has helped to avoid global catastrophic disaster, or who has done incredible work to ensure a beneficial future of life, please head over to the Future of Life Award page and submit a candidate for consideration. The link for that page is on the page for this podcast or in the description of wherever you might be listening. If your candidate is chosen, you will receive $3,000 as a token of our appreciation. We’re also incentivizing the search via MIT’s successful red balloon strategy, where the first to nominate the winner gets $3,000 as mentioned, but there are also tiered pay outs to the person who invited the nomination winner, and so on. You can find details about that on the page. 

George Church is Professor of Genetics at Harvard Medical School and Professor of Health Sciences and Technology at Harvard and MIT. He is Director of the U.S. Department of Energy Technology Center and Director of the National Institutes of Health Center of Excellence in Genomic Science. George leads Synthetic Biology at the Wyss Institute, where he oversees the directed evolution of molecules, polymers, and whole genomes to create new tools with applications in regenerative medicine and bio-production of chemicals. He helped initiate the Human Genome Project in 1984 and the Personal Genome Project in 2005. George invented the broadly applied concepts of molecular multiplexing and tags, homologous recombination methods, and array DNA synthesizers. His many innovations have been the basis for a number of companies including Editas, focused on gene therapy, Gen9bio, focused on Synthetic DNA, and Veritas Genetics, which is focused on full human genome sequencing. And with that, let’s get into our conversation with George Church.

So I just want to start off here with a little bit of a bigger picture about what you care about most and see as the most important issues today.

George Church: Well, there’s two categories of importance. One are things that are very common and so affect many people. And then there are things that are very rare but very impactful nevertheless. Those are my two top categories. They weren’t when I was younger. I didn’t consider either of them that seriously. So examples of very common things are age-related diseases, infectious diseases. They can affect all 7.7 billion of us. Then on the rare end would be things that could wipe out all humans or all civilization or all living things, asteroids, supervolcanoes, solar flares, and engineered or costly natural pandemics. So those are things that I think are very important problems. Then we have had the research to enhance wellness and minimize those catastrophes. The third category or somewhat related to those two which is things we can do to say get us off the planet, so things would be highly preventative from total failure.

Lucas Perry: So in terms of these three categories, how do you see the current allocation of resources worldwide and how would you prioritize spending resources on these issues?

George Church: Well the current allocation of resources is very different from the allocations that I would set for my own research goals and what I would set for the world if I were in charge, in that there’s a tendency to be reactive rather than preventative. And this applies to both therapeutics versus preventatives and the same thing for environmental and social issues. All of those, we feel like it somehow makes sense or is more cost-effective, but I think it’s an illusion. It’s far more cost-effective to do many things preventatively. So, for example, if we had preventatively had a system of extensive testing for pathogens, we could probably save the world trillions of dollars on one disease alone with COVID-19. I think the same thing is true for global warming. A little bit of preventative environmental engineering for example in the Arctic where relatively few people would be directly engaged, could save us disastrous outcomes down the road.

So I think we’re prioritizing a very tiny fraction for these things. Aging and preventative medicine is maybe a percent of the NIH budget, and each institute sets aside about a percent to 5% on preventative measures. Gene therapy is another one. Orphan drugs, very expensive therapies, millions of dollars per dose versus genetic counseling which is now in the low hundreds, soon will be double digit dollars per lifetime.

Lucas Perry: So in this first category of very common widespread issues, do you have any other things there that you would add on besides aging? Like aging seems to be the kind of thing in culture where it’s recognized as an inevitability so it’s not put on the list of top 10 causes of death. But lots of people who care about longevity and science and technology and are avant-garde on these things would put aging at the top because they’re ambitious about reducing it or solving aging. So are there other things that you would add to that very common widespread list, or would it just be things from the top 10 causes of mortality?

George Church: Well infection was the other one that I included in the original list in common diseases. Infectious diseases are not so common in the wealthiest parts of the world, but they are still quite common worldwide, HIV, TB, malaria are still quite common, millions of people dying per year. Nutrition is another one that tends to be more common in the four parts of the world that still results in death. So the top three would be aging-related.

And even if you’re not interested in longevity and even if you believe that aging is natural, in fact some people think that infectious diseases and nutritional deficiencies are natural. But putting that aside, if we’re attacking age-related diseases, we can use preventative medicine and aging insights into reducing those. So even if you want to neglect longevity that’s unnatural, if you want to address heart disease, strokes, lung disease, falling down, infectious disease, all of those things might be more easily addressed by aging studies and therapies and preventions than by a frontal assault on each micro disease one at a time.

Lucas Perry: And in terms of the second category, existential risk, if you were to rank order the likelihood and importance of these existential and global catastrophic risks, how would you do so?

George Church: Well you can rank their probability based on past records. So, we have some records of supervolcanoes, solar activity, and asteroids. So that’s one way of calculating probability. And then you can also calculate the impact. So it’s a product, the probability and impact for the various kinds of recorded events. I mean I think they’re similar enough that I’m not sure I would rank order those three.

And then pandemics, whether natural or human-influenced, probably a little more common than those first three. And then climate change. There are historic records but it’s not clear that they’re predictive. The probability of an asteroid hitting probably is not influenced by human presence, but climate change probably is and so you’d need a different model for that. But I would say that that is maybe the most likely of the lot for having an impact.

Lucas Perry: Okay. The Future of Life Institute, the things that we’re primarily concerned about in terms of this existential risk category would be the risks from artificial general intelligence and superintelligence, also synthetic bio-risk coming up in the 21st century more and more, and then accidental nuclear war would also be very bad, maybe not an existential risk. That’s arguable. Those are sort of our central concerns in terms of the existential risk category.

Relatedly the Future of Life Institute sees itself as a part of the effective altruism community which when ranking global priorities, they have four areas of essential consideration for impact. The first is global poverty. The second is animal suffering. And the third is long-term future and existential risk issues, having to do mainly with anthropogenic existential risks. The fourth one is meta-effective altruism. So I don’t want to include that. They also tend to make the same ranking, being that mainly the long-term risks of advanced artificial intelligence are basically the key issues that they’re worried about.

How do you feel about these perspectives or would you change anything?

George Church: My feeling is that natural intelligence is ahead of artificial intelligence and will stay there for quite a while, partly because synthetic biology has a steeper slope and I’m including the enhanced natural intelligence in the synthetic biology. That has a steeper upward slope than totally inorganic computing now. But we can lump those together. We can say artificial intelligence writ large to include anything that our ancestors didn’t have in terms of intelligence, which could include enhancing our own intelligence. And I think especially should include corporate behavior. Corporate behavior is a kind of intelligence which is not natural, is wide spread, and it is likely to change, mutate, evolve very rapidly, faster than human generation times, probably faster than machine generation times.

Nukes I think are aging and maybe are less attractive as a defense mechanism. I think they’re being replaced by intelligence, artificial or otherwise, or collective and synthetic biology. I mean I think that if you wanted to have mutually assured destruction, it would be more cost-effective to do that with syn-bio. But I would still keep it on the list.

So I agree with that list. I’d just like nuanced changes to where the puck is likely to be going.

Lucas Perry: I see. So taking into account and reflecting on how technological change in the short to medium term will influence how one might want to rank these risks.

George Church: Yeah. I mean I just think that a collective human enhanced intelligence is going to be much more disruptive potentially than AI is. That’s just a guess. And I think that nukes will just be part of a collection of threatening things that people do. Probably it’s more threatening to cause collapse of a electric grid or a pandemic or some other economic crash than nukes.

Lucas Perry: That’s quite interesting and is very different than the story that I have in my head, and I think will also be very different than the story that many listeners have in their heads. Could you expand and unpack your timelines and beliefs about why you think the\at collective organic intelligence will be ahead of AI? Could you say, I guess, when you would expect AI to surpass collective bio intelligence and some of the reasons again for why?

George Church: Well, I don’t actually expect silicon-based intelligence to ever bypass in every category. I think it’s already super good at storage retrieval and math. But that’s subject to change. And I think part of the assumptions have been that we’ve been looking at a Moore’s law projection while most people haven’t been looking at the synthetic biology equivalent and haven’t noticed that the Moore’s law might finally be plateauing, at least as it was originally defined. So that’s part of the reason I think for the excessive optimism, if you will, about artificial intelligence.

Lucas Perry: The Moore’s law thing has to do with hardware and computation, right?

George Church: Yeah.

Lucas Perry: That doesn’t say anything about how algorithmic efficiency and techniques and tools are changing, and the access to big data. Something we’ve talked about on this podcast before is that many of the biggest insights and jumps in deep learning and neural nets haven’t come from new techniques but have come from more massive and massive amounts of compute on data.

George Church: Agree, but those data are also available to humans as big data. I think maybe the compromise here is that it’s some hybrid system. I’m just saying that humans plus big data plus silicon-based computers, even if they stay flat in hardware is going to win over either one of them separately. So maybe what I’m advocating is hybrid systems. Just like in your brain you have different parts of your brain that have different capabilities and functionality. In a hybrid system we would have the wisdom of crowds, plus compute engines, plus big data, but available to all the parts of the collective brain.

Lucas Perry: I see. So it’s kind of like, I don’t know if this is still true, but I think at least at some point it was true, that the best teams at chess were AIs plus humans?

George Church: Correct, yeah. I think that’s still true. But I think it will become even more true if we start altering human brains, which we have a tendency to try to do already via education and caffeine and things like that. But there’s really no particular limit to that.

Lucas Perry: I think one of the things that you said was that you don’t think that AI alone will ever be better than biological intelligence in all ways.

George Church: Partly because biological intelligence is a moving target. The first assumption was that the hardware would keep improving on Moore’s law, which it isn’t. The second assumption was that we would not alter biological intelligence. There’s one moving target which was silicon and biology was not moving, when in fact biology is moving at a steeper slope both in terms of hardware and algorithms and everything else and we’re just beginning to see that. So I think that when you consider both of those, it at least sows the seed of uncertainty as to whether AI is inevitably better than a hybrid system.

Lucas Perry: Okay. So let me just share the kind of story that I have in my head and then you can say why it might be wrong. AI researchers have been super wrong about predicting how easy it would be to make progress on AI in the past. So taking predictions with many grains of salt, if you interview say the top 100 AI researchers in the world, they’ll give a 50% probability of there being artificial general intelligence by 2050. That could be very wrong. But they gave like a 90% probability of there being artificial general intelligence by the end of the century.

And the story in my head says that I expect there to be bioengineering and genetic engineering continuing. I expect there to be designer babies. I expect there to be enhancements to human beings further and further on as we get into the century in increasing capacity and quality. But there are computational and substrate differences between computers and biological intelligence like the clock speed of computers can be much higher. They can compute much faster. And then also there’s this idea about the computational architectures in biological intelligences not being privileged or only uniquely available to biological organisms such that whatever the things that we think are really good or skillful or they give biological intelligences a big edge on computers could simply be replicated in computers.

And then there is an ease of mass manufacturing compute and then emulating those systems on computers such that the dominant and preferable form of computation in the future will not be on biological wetware but will be on silicon. And for that reason at some point there’ll just be a really big competitive advantage for the dominant form of compute and intelligence and life on the planet to be silicon based rather than biological based. What is your reaction to that?

George Church: You very nicely summarized what I think is a dominant worldview of people that are thinking about the future, and I’m happy to give a counterpoint. I’m not super opinionated but I think it’s worthy of considering both because the reason we’re thinking about the future is we don’t want to be blind sighted by it. And this could be happening very quickly by the way because both revolutions are ongoing as is the merger.

Now clock speed, my guess is that clock speed may not be quite as important as energy economy. And that’s not to say that both systems, let’s call them bio and non-bio, can’t optimize energy. But if you look back at sort of the history of evolution on earth, the fastest clock speeds, like bacteria and fruit flies, aren’t necessarily more successful in any sense than humans. They might have more bio mass, but I think humans are the only species with our slow clock speed relative to bacteria that are capable of protecting all of the species by taking us to a new planet.

And clock speed is only important if you’re in a direct competition in a fairly stable environment where the fastest bacteria win. But worldwide most of the bacteria are actually very slow growers. If you look at energy consumption right now, which both of them can improve, there are biological compute systems that are arguably a million times more energy-efficient at even tasks where the biological system wasn’t designed or evolved for that task, but it can kind of match. Now there are other things where it’s hard to compare, either because of the intrinsic advantage that either the bio or the non-bio system has, but where they are sort of on the same framework, it takes 100 kilowatts of power to run say Jeopardy! and Go on a computer and the humans that are competing are using considerably less than that, depending on how you calculate all the things that is required to support the 20 watt brain.

Lucas Perry: What do you think the order of efficiency difference is?

George Church: I think it’s a million fold right now. And this largely a hardware thing. I mean there is algorithmic components that will be important. But I think that one of the advantages that bio chemical systems have is that they are intrinsically atomically precise. While Moore’s law seem to be plateauing somewhere around 3 nanometer fabrication resolution, that’s off by maybe a thousand fold from atomic resolution. So that’s one thing, that as you go out many years, they will either be converging on or merging in some ways so that you get the advantages of atomic precision, the advantages of low energy and so forth. So that’s why I think that we’re moving towards a slightly more molecular future. It may not be recognizable as either our silicon von Neumann or other computers, nor totally recognizable as a society of humans.

Lucas Perry: So is your view that we won’t reach artificial general intelligence like the kind of thing which can reason about as well as about humans across all the domains that humans are able to reason? We won’t reach that on non-bio methods of computation first?

George Church: No, I think that we will have AGI in a number of different substrates, mechanical, silicon, quantum computing. Various substrates will be able of doing artificial general intelligence. It’s just that the ones that do it in a most economic way will be the ones that we will tend to use. There’ll be some cute museum that will have a collection of all the different ways, like the tinker toy computer that did Tic Tac Toe. Well, that’s in a museum somewhere next to Danny Hillis, but we’re not going to be using that for AGI. And I think there’ll be a series of artifacts like that, that in practice it will be very pragmatic collection of things that make economic sense.

So just for example, its easier to make a copy of a biological brain. Now that’s one thing that appears to be an advantage to non-bio computers right now, is you can make a copy of even large data sets for a fairly small expenditure of time, cost, and energy. While, to educate a child takes decades and in the end you don’t have anything totally resembling the parents and teachers. I think that’s subject to change. For example, we have now storage of data in DNA form, which is about a million times denser than any comprable non-chemical, non-biological system, and you can make a copy of it for hundreds of joules of energy and pennies. So you can hold an exabyte of data in the palm of your hand and you can make a copy of it relatively easy.

Now that’s not a mature technology, but it shows where we’re going. If we’re talking 100 years, there’s no particular reason why you couldn’t have that embedded in your brain and input and output to it. And by the way, the cost of copying that is very close to the thermodynamic limit for making copies of bits, while computers are nowhere near that. They’re off by a factor of a million.

Lucas Perry: Let’s see if I get this right. Your view is that there is this computational energy economy benefit. There is this precisional element which is of benefit, and that because there are advantages to biological computation, we will want to merge the best aspects of biological computation with non-biological in order to sort of get best of both worlds. So while there may be many different AGIs on offer on different substrates, the future looks like hybrids.

George Church: Correct. And it’s even possible that silicon is not in the mix. I’m not predicting that it’s not in the mix. I’m just saying it’s possible. It’s possible that an atomically precise computer is better at quantum computing or is better at clock time or energy.

Lucas Perry: All right. So I do have a question later about this kind of thing and space exploration and reducing existential risk via further colonization which I do want to get into later. I guess I don’t have too much more to say about our different stories around here. I think that what you’re saying is super interesting and challenging in very interesting ways. I guess the only thing I would have to say is I guess I don’t know enough, but you said that the computation energy economy is like a million fold more efficient.

George Church: That’s for copying bits, for DNA. For doing complex tasks for example, Go, Jeopardy! or Einstein’s Mirabilis, those kinds of things were typically competing a 20 watt brain plus support structure with a 100 kilowatt computer. And I would say at least in the case of Einstein’s 1905 we win, even though we lose at Go and Jeopardy!, which is another interesting thing, is that humans have a great deal more of variability. And if you take the extreme values like one person in one year, Einstein in 1905 as the representative rather than the average person and the average year for that person, well, if you make two computers, they are going to likely be nearly identical, which is both a plus and a minus in this case. Now if you make Einstein in 1905 the average for humans, then you have a completely different set of goalpost for the AGI than just being able to pass a basic Turing test where you’re simulating someone of average human interest and intelligence.

Lucas Perry: Okay. So two things from my end then. First is, do you expect AGI to first come from purely non-biological silicon-based systems? And then the second thing is no matter what the system is, do you still see the AI alignment problem as the central risk from artificial general intelligence and superintelligence, which is just aligning AIs with human values and goals and intentions?

George Church: I think the further we get from human intelligence, the harder it is to convince ourselves that we can educate, and whereas the better they will be at fooling us. It doesn’t mean they’re more intelligent than us. It’s just they’re alien. It’s like a wolf can fool us when we’re out in the woods.

Lucas Perry: Yeah.

George Church: So I think that exceptional humans are as hard to guarantee that we really understand their ethics. So if you have someone who is a sociopath or high functioning autistic, we don’t really know after 20 years of ethics education whether they actually are thinking about it the same way we are, or even in compatible way to the way that we are. We being in this case neurotypicals, although I’m not sure I am one. But anyway.

I think that this becomes a big problem with AGI, and it may actually put a damper on it. Part of the assumption so far is we won’t change humans because we have to get ethics approval for changing humans. But we’re increasingly getting ethics approval for changing humans. I mean gene therapies are now approved and increasing rapidly, all kinds of neuro-interfaces and so forth. So I think that that will change.

Meanwhile, the silicon-based AGI as we approached it, it will change in the opposite direction. It will be harder and harder to get approval to do manipulations in those systems, partly because there’s risk, and partly because there’s sympathy for the systems. Right now there’s very little sympathy for them. But as you got to the point where computers haven an AGI level of say IQ of 70 or something like that for a severely mentally disabled person so it can pass the Turing test, then they should start getting the rights of a disabled person. And once they have the rights of a disabled person, that would include the right to not be unplugged and the right to vote. And then that creates a whole bunch of problems that we won’t want to address, except as academic exercises or museum specimens that we can say, hey, 50 years ago we created this artificial general intelligence, just like we went to the Moon once. They’d be stunts more than practical demonstrations because they will have rights and because it will represent risks that will not be true for enhanced human societies.

So I think more and more we’re going to be investing in enhanced human societies and less and less in the uncertain silicon-based. That’s just a guess. It’s based not on technology but on social criteria.

Lucas Perry: I think that it depends what kind of ethics and wisdom that we’ll have at that point in time. Generally I think that we may not want to take conventional human notions of personhood and apply them to things where it might not make sense. Like if you have a system that doesn’t mind being shut off, but it can be restarted, why is it so unethical to shut it off? Or if the shutting off of it doesn’t make it suffer, suffering may be some sort of high level criteria.

George Church: By the same token you can make human beings that don’t mind being shut off. That won’t change our ethics much I don’t think. And you could also make computers that do mind being shut off, so you’ll have this continuum on both sides. And I think we will have sympathetic rules, but combined with the risk, which is the risk that they can hurt you, the risk that if you don’t treat them with respect, they will be more likely to hurt you, the risk that you’re hurting them without knowing it. For example, if you have somebody with locked-in syndrome, you could say, “Oh, they’re just a vegetable,” or you could say, “They’re actually feeling more pain than I am because they have no agency, they have no ability to control their situation.”

So I think creating computers that could have the moral equivalent of locked-in syndrome or some other pain without the ability to announce their pain could be very troubling to us. And we would only overcome it if that were a solution to an existential problem or had some gigantic economic benefit. I’ve already called that into question.

Lucas Perry: So then, in terms of the first AGI, do you have a particular substrate that you imagine that coming online on?

George Church: My guess is it will probably be very close to what we have right now. As you said, it’s going to be algorithms and databases and things like that. And it will be probably at first a stunt, in the same sense that Go and Jeopardy! are stunts. It’s not clear that those are economically important. A computer that could pass the Turing test, it will make a nice chat bots and phone answering machines and things like that. But beyond that it may not change our world, unless we solve energy issues and so. So I think to answer your question, we’re so close to it now that it might be based on an extrapolation of current systems.

Quantum computing I think is maybe a more special case thing. Just because it’s good at encryption, encryption is very societal utility. I haven’t yet seen encryption described as something that’s mission critical for space flight or curing diseases, other than the social components of those. And quantum simulation may be beaten by building actual quantum systems. So for example, atomically precise systems that you can build with synthetic biology are quantum systems that are extraordinarily hard to predict, but they’re very easy to synthesize and measure.

Lucas Perry: Is your view here that if the first AGI is on the economic and computational scale of a supercomputer such that we imagine that we’re still just leveraging really, really big amounts of data and we haven’t made extremely efficient advancements and algorithms such that the efficiency jumps a lot but rather the current trends continue and it’s just more and more data and maybe some algorithmic improvements, that the first system is just really big and clunky and expensive, and then that thing can self-recursively try to make itself cheaper, and then that the direction that that would move in would be increasingly creating hardware which has synthetic bio components.

George Church: Yeah, I’d think that that already exists in a certain sense. We have a hybrid system that is self-correcting, self-improving at an alarming rate. But it is a hybrid system. In fact, it’s such a complex hybrid system that you can’t point to a room where it can make a copy of itself. You can’t even point to a building, possibly not even a state where you can make a copy of this self-modifying system because it involves humans, it involves all kinds of fab labs scattered around the globe.

We could set a goal to be able to do that, but I would argue we’re much closer to achieving that goal with a human being. You can have a room where you only can make a copy of a human, and if that is augmentable, that human can also make computers. Admittedly it would be a very primitive computer if you restricted that human to primitive supplies and a single room. But anyway, I think that’s the direction we’re going. And we’re going to have to get good at doing things in confined spaces because we’re not going to be able to easily duplicate planet Earth, probably going to have to make a smaller version of it and send it off and how big that is we can discuss later.

Lucas Perry: All right. Cool. This is quite perspective shifting and interesting, and I will want to think about this more in general going forward. I want to spend just a few minutes on this next question. I think it’ll just help give listeners a bit of overview. You’ve talked about it in other places. But I’m generally interested in getting a sense of where we currently stand with the science of genetics in terms of reading and interpreting human genomes, and what we can expect on the short to medium term horizon in human genetic and biological sciences for health and longevity?

George Church: Right. The short version is that we have gotten many factors of 10 improvement in speed, cost, accuracy, and interpretability, 10 million fold reduction in price from $3 billion for a poor quality genomic non-clinical quality sort of half a genome in that each of us have two genomes, one from each parent. So we’ve gone from $3 billion to $300. It will probably be $100 by the middle of year, and then will keep dropping. There’s no particular second law of thermodynamics or Heisenberg stopping us, at least for another million fold. That’s where we are in terms of technically being able to read and for that matter write DNA.

But the interpretation certainly there are genes that we don’t know what they do, there are disease that we don’t know what causes them. There’s a great vast amount of ignorance. But that ignorance may not be as impactful as sometimes we think. It’s often said that common diseases or so called complex multi-genic diseases are off in the future. But I would reframe that slightly for everyone’s consideration, that many of these common diseases are diseases of aging. Not all of them but many, many of them that we care about. And it could be that attacking aging as a specific research program may be more effective than trying to list all the millions of small genetic changes that has small phenotypic effects on these complex diseases.

So that’s another aspect of the interpretation where we don’t necessarily have to get super good at so called polygenic risk scores. We will. We are getting better at it, but it could be in the end a lot of the things that we got so excited about precision medicine, and I’ve been one of the champions of precision medicine since before it was called that. But precision medicine has a potential flaw in it, which is it’s the tendency to work on the reactive cures for specific cancers and inherited diseases and so forth when the preventative form of it which could be quite generic and less personalized might be more cost-effective and humane.

So for example, taking inherited diseases, we have a million to multi-million dollars spent on people having inherited diseases per individual, while a $100 genetic diagnosis could be used to prevent that. And generic solutions like aging reversal or aging prevention might stop cancer more effectively than trying to stop it once it gets to metastatic stage, which there is a great deal of resources put into that. That’s my update on where genomics is. There’s a lot more that could be said.

Lucas Perry:

Yeah. As a complete lay person in terms of biological sciences, stopping aging to me sounds like repairing and cleaning up human DNA and the human genome such that information that is lost over time is repaired. Correct me if I’m wrong or explain a little bit about what the solution to aging might look like.

George Church: I think there’s two kind of closer related schools of thought which one is that there’s damage that you need to go in there and fix the way you would fix a pothole. And the other is that there’s regulation that informs the system how to fix itself. I believe in both. I tend to focus on the second one.

If you take a very young cell, say a fetal cell. It has a tendency to repair much better than an 80-year-old adult cell. The immune system of a toddler is much more capable than that of a 90-year-old. This isn’t necessarily due to damage. This is due to the epigenetic so called regulation of the system. So one cell is convinced that it’s young. I’m going to use some anthropomorphic terms here. So you can take an 80-year-old cell, actually up to 100 years is now done, reprogram it into an embryo like state through for example Yamanaka factors named after Shinya Yamanaka. And that reprogramming resets many, not all, of the features such that it now behaves like a young non-senescent cell. While you might have taken it from a 100-year-old fibroblast that would only replicate a few times before it senesced and died.

Things like that seem to convince us that aging is reversible and you don’t have to micromanage it. You don’t have to go in there and sequence the genome and find every bit of damage and repair it. The cell will repair itself.

Now there are some things like if you delete a gene it’s gone unless you have a copy of it, in which case you could copy it over. But those cells will probably die off. And the same thing happens in the germline when you’re passing from parent to kid, those sorts of things that can happen and the process of weeding them out is not terribly humane right now.

Lucas Perry: Do you have a sense or timelines on progress of aging throughout the century?

George Church: There’s been a lot of wishful thinking for centuries on this topic. But I think we have a wildly different scenario now, partly because this exponential improvement in technologies, reading and writing DNA and the list goes on and on in cell biology and so forth. So I think we suddenly have a great deal of knowledge of causes of aging and ways to manipulate those to reverse it. And I think these are all exponentials and we’re going to act on them very shortly.

We already are seeing some aging drugs, small molecules that are in clinical trials. My lab just published a combination gene therapy that will hit five different diseases of aging in mice and now it’s in clinical trials in dogs and then hopefully in a couple of years it will be in clinical trials in humans.

We’re not talking about centuries here. We’re talking about the sort of time that it takes to get things through clinical trails, which is about a decade. And a lot of stuff going on in parallel which then after one decade of parallel trials would be merging into combined trials. So a couple of decades.

Lucas Perry: All right. So I’m going to get in trouble in here if I don’t talk to you about synthetic bio risk. So, let’s pivot into that. What are your views and perspectives on the dangers to human civilization that an increasingly widespread and more advanced science of synthetic biology will pose?

George Church: I think it’s a significant risk. Getting back to the very beginning of our conversation, I think it’s probably one of the most significant existential risks. And I think that preventing it is not as easy as nukes. Not that nukes are easy, but it’s harder. Partly because it’s becoming cheaper and the information is becoming more widespread.

But it is possible. Part of it depends on having many more positive societally altruistic do gooders than do bad. It would be helpful if we could also make a big impact on poverty and diseases associated poverty and psychiatric disorders. The kind of thing that causes unrest and causes dissatisfaction is what tips the balance where one rare individual or a small team will do something that otherwise it would be unthinkable for even them. But if they’re sociopaths or they are representing a disadvantaged category of people then they feel justified.

So we have to get at some of those core things. It would also be helpful if we were more isolated. Right now we are very well mixed pot, which puts us both at risk for natural, as well as engineered diseases. So if some of us lived in sealed environments on Earth that are very similar to the sealed environments that we would need in space, that would both prepare us for going into space. And some of them would actually be in space. And so the further we are away from the mayhem of our wonderful current society, the better. If we had a significant fraction of population that was isolated, either on earth or elsewhere, it would lower the risk of all of us dying.

Lucas Perry: That makes sense. What are your intuitions about the offense/defense balance on synthetic bio risk? Like if we have 95% to 98% synthetic bio do gooders and a small percentage of malevolent actors or actors who want more power, how do you see the relative strength and weakness of offense versus defense?

George Church: I think as usual it’s a little easier to do offense. It can go back and forth. Certainly it seems easier to defend yourself from a ICBM than from something that could be spread in a cough. And we’re seeing that in spades right now. I think the fraction of white hats versus black hats is much better than 98% and it has to be. It has to be more like a billion to one. And even then it’s very risky. But yeah, it’s not easy to protect.

Now you can do surveillance so that you can restrict research as best you can, but it’s a numbers game. It’s combination of removing incentives, adding strong surveillance, whistleblowers that are not fearful of false positives. The suspicious package in the airport should be something you look at, even though most of them are not actually bombs. We should tolerate a very high rate of false positives. But yes, surveillance is not something we’re super good at it. It falls in the category of preventative medicine. And we would far prefer to do reactive, is to wait until somebody releases some pathogen and then say, “Oh, yeah, yeah, we can prevent that from happening again in the future.”

Lucas Perry: Is there a opportunity for boosting or beefing a human immune system or a public early warning detection systems of powerful and deadly synthetic bio agents?

George Church: Well so, yes is the simple answer. If we boost our immune systems in a public way — which it almost would have to be, there’d be much discussion about how to do that — then pathogens that get around those boosts might become more common. In terms of surveillance, I proposed in 2004 that we had an opportunity and still do of doing surveillance on all synthetic DNA. I think that really should be 100% worldwide. Right now it’s 80% or so. That is relatively inexpensive to fully implement. I mean the fact that we’ve done 80% already closer to this.

Lucas Perry: Yeah. So, funny enough I was actually just about to ask you about that paper that I think you’re referencing. So in 2004 you wrote A Synthetic Biohazard Non-proliferation Proposal, in anticipation of a growing dual use risk of synthetic biology, which proposed in part the sale and registry of certain synthesis machines to verified researchers. If you were to write a similar proposal today, are there some base elements of it you would consider including, especially since the ability to conduct synthetic biology research has vastly proliferated since then? And just generally, are you comfortable with the current governance of dual use research?

George Church: I probably would not change that 2004 white paper very much. Amazingly the world has not changed that much. There still are a very limited number of chemistries and devices and companies, so that’s a bottleneck which you can regulate and is being regulated by the International Gene Synthesis Consortium, IGSC. I did advocate back then and I’m still advocating that we get closer to an international agreement. Two sectors generally in the United Nations have said casually that they would be in favor of that, but we need essentially every level from the UN all the way down to local governments.

There’s really very little pushback today. There was some pushback back in 2004 where the company’s lawyers felt that they would be responsible or there would be an invasion of privacy of their customers. But I think eventually the rationale of high risk avoidance won out, so now it’s just a matter of getting full compliance.

One of these unfortunate things that the better you are at avoiding an existential risk, the less people know about it. In fact, we did so well on Y2K makes it uncertain as to whether we needed to do anything about Y2K at all, and I think hopefully the same thing will be true for a number of disasters that we avoid without most of the population even knowing how close we were.

Lucas Perry: So the main surveillance intervention here would be heavy monitoring and regulation and tracking of the synthesis machines? And then also a watch dog organization which would inspect the products of said machines?

George Church: Correct.

Lucas Perry: Okay.

George Church: Right now most of the DNA is ordered. You’ll send on the internet your order. They’ll send back the DNA. Those same principles have to apply to desktop devices. It has to get some kind of approval to show that you are qualified to make a particular DNA before the machine will make that DNA. And it has to be protected against hardware and software hacking which is a challenge. But again, it’s a numbers game.

Lucas Perry: So on the topic of biological risk, we’re currently in the context of the COVID-19 pandemic. What do you think humanity should take as lessons from COVID-19?

George Church: Well, I think the big one is testing. Testing is probably the fastest way out of it right now. The geographical locations that have pulled out of it fastest were the ones that were best at testing and isolation. If your testing is good enough, you don’t even have to have very good contact tracing, but that’s also valuable. The longer shots are cures and vaccines and those are not entirely necessary and they are long-term and uncertain. There’s no guarantee that we will come up with a cure or a vaccine. For example, HIV, TB and malaria do not have great vaccines, and most of them don’t have great stable cures. HIV is a full series of cures over time. But not even cures. They’re more maintenance, management.

I sincerely hope that coronavirus is not in that category of HIV, TB, and malaria. But we can’t do public health based on hopes alone. So testing. I’ve been requesting a bio weather map and working towards improving the technology to do so since around 2002, which was before the SARS 2003, as part of the inspiration for the personal genome project, was this bold idea of bio weather map. We should be at least as interested in what biology is doing geographically as we are about what the low pressure fronts are doing geographically. It could be extremely inexpensive, certainly relative to the multi-trillion dollar cost for one disease.

Lucas Perry: So given the ongoing pandemic, what has COVID-19 demonstrated about human global systems in relation to existential and global catastrophic risk?

George Church: I think it’s a dramatic demonstration that we’re more fragile than we would like to believe. It’s a demonstration that we tend to be more reactive than proactive or preventative. And it’s a demonstration that we’re heterogeneous. That there are geographical reasons and political systems that are better prepared. And I would say at this point the United States is probably among the least prepared, and that was predictable by people who thought about this in advance. Hopefully we will be adequately prepared that we will not emerge from this as a third world nation. But that is still a possibility.

I think it’s extremely important to make our human systems, especially global systems more resilient. It would be nice to take as examples the countries that did the best or even towns that did the best. For example, the towns of Vo, Italy and I think Bolinas, California, and try to spread that out to the regions that did the worst. Just by isolation and testing, you can eliminate it. That sort of thing is something that we should have worldwide. To make the human systems more resilient we can alter our bodies, but I think very effective is altering our social structures so that we are testing more frequently, we’re constantly monitoring both zoonotic sources and testing bushmeat and all the places where we’re getting too close to the animals. But also testing our cities and all the environments that humans are in so that we have a higher probability of seeing patient zero before they become a patient.

Lucas Perry: The last category that you brought up at the very beginning of this podcast was preventative measures and part of that was not having all of our eggs in the same basket. That has to do with say Mars colonization or colonization of other moons which are perhaps more habitable and then eventually to Alpha Centauri and beyond. So with advanced biology and advanced artificial intelligence, we’ll have better tools and information for successful space colonization. What do you see as the main obstacles to overcome for colonizing the solar system and beyond?

George Church: So we’ll start with the solar system. Most of the solar system is not pleasant compared to Earth. It’s a vacuum and it’s cold, including Mars and many of the moons. There are moons that have more water, more liquid water than Earth, but it requires some drilling to get down to it typically. There’s radiation. There’s low gravity. And we’re not adaptive.

So we might have to do some biological changes. They aren’t necessarily germline but they’ll be the equivalent. There are things that you could do. You can simulate gravity with centrifuges and you can simulate the radiation protection we have on earth with magnetic fields and thick shielding, equivalent of 10 meters of water or dirt. But there will be a tendency to try to solve those problems. There’ll be issues of infectious disease, which ones we want to bring with us and which ones we want to quarantine away from. That’s an opportunity more than a uniquely space related problem.

A lot of the barriers I think are biological. We need to practice building colonies. Right now we have never had a completely recycled human system. We have completely recycled plant and animal systems but none that are humans, and that is partly having to do with social issues, hygiene and eating practices and so forth. I think that can be done, but it should be tested on Earth because the consequences of failure on a moon or non-earth planet is much more severe than if you test it out on Earth. We should have thousands, possibly millions of little space colonies on Earth since one of my pet projects is making that so that it’s economically feasible on Earth. Only by heavy testing at that scale will we find the real gotchas and failure modes.

And then final barrier, which is more in the category that people think about is the economies of, if you do the physics calculation how much energy it takes to raise a kilogram into orbit or out of orbit, it’s much, much less than the cost per kilogram, orders of magnitude than what we currently do. So there’s some opportunity for improvement there. So that’s in the solar system.

Outside of the solar system let’s say Proxima B, Alpha Centauri and things of that range, there’s nothing particularly interesting between here and there, although there’s nothing to stop us from occupying the vacuum of space. To get to four and a half light years either requires a revolution in propulsion and sustainability in a very small container, or a revolution in the size of the container that we’re sending.

So, one pet project that I’m working on is trying to make a nanogram size object that would contain the information sufficiently for building a civilization or at least building a communication device that’s much easier to accelerate and decelerate a nanogram than it is to do any of the scale of space probes we currently use.

Lucas Perry: Many of the issues that human beings will face within the solar system and beyond machines or synthetic computation that exist today seems more robust towards. Again, there are the things which you’ve already talked about like the computational efficiency and precision for self-repair and other kinds of things that modern computers may not have. So I think just a little bit of perspective on that would be useful, like why we might not expect that machines would take the place of humans in many of these endeavors.

George Church: Well, so for example, we would be hard pressed to even estimate, I haven’t seen a good estimate yet, of a self-contained device that could make a copy of itself from dirt or whatever, the chemicals that are available to it on a new planet. But we do know how to do that with humans or hybrid systems.

Here’s a perfect example of a hybrid system. Is a human can’t just go out into space. It needs a spaceship. A spaceship can’t go out into space either. It needs a human. So making a replicating system seems like a good idea, both because we are replicating systems and it lowers the size of the package you need to send. So if you want to have a million people in the Alpha Centauri system, it might be easier just to send a few people and a bunch of frozen embryos or something like that.

Sending a artificial general intelligence is not sufficient. It has to also be able to make a copy of itself, which I think is a much higher hurdle than just AGI. I think AGI, we will achieve before we achieve AGI plus replication. It may not be much before, it will be probably be before.

In principle, a lot of organisms, including humans, start from single cells and mammals tend to need more support structure than most other vertebrates. But in principle if you land a vertebrate fertilized egg in an aquatic environment, it will develop and make copies of itself and maybe even structures.

So my speculation is that there exist a nanogram cell that’s about the size of a lot of vertebrate eggs. There exists a design for a nanogram that would be capable of dealing with a wide variety of harsh environments. We have organisms that thrive everywhere between the freezing point of water and the boiling point or 100 plus degrees at high pressure. So you have this nanogram that is adapted to a variety of different environments and can reproduce, make copies of itself, and built into it is a great deal of know-how about building things. The same way that building a nest is built into a bird’s DNA, you could have programmed into an ability to build computers or a radio or laser transmitters so it could communicate and get more information.

So a nanogram could travel at close the speed of light and then communicate at close the speed of light once it replicates. I think that illustrates the value of hybrid systems, within this particular case a high emphasis on the biochemical, biological components that’s capable of replicating as the core thing that you need for efficient transport.

Lucas Perry: If your claim about hybrid systems is true, then if we extrapolate it to say the deep future, then if there’s any other civilizations out there, then the form in which we will meet them will likely also be hybrid systems.

And this point brings me to reflect on something that Nick Bostrom talks about, the great filters which are supposed points in the evolution and genesis of life throughout the cosmos that are very difficult for life to make it through those evolutionary leaps, so almost all things don’t make it through the filter. And this is hypothesized to be a way of explaining the Fermi paradox, why is it that there are hundreds of billions of galaxies and we don’t see any alien superstructures or we haven’t met anyone yet?

So, I’m curious to know if you have any thoughts or opinions on what the main great filters to reaching interstellar civilization might be?

George Church: Of all the questions you’ve asked, this is the one where i’m most uncertain. I study among other things how life originated, in particular how we make complex biopolymers, so ribosomes making proteins for example, the genetic code. That strikes me as a pretty difficult thing to have arisen. That’s one filter. Maybe much earlier than many people would think.

Another one might be lack of interest that once you get to a certain level of sophistication, you’re happy with your life, your civilization, and then typically you’re overrun by someone or something that is more primitive from your perspective. And then they become complacent, and the cycle repeats itself.

Or the misunderstanding of resources. I mean we’ve seen a number of island civilizations that have gone extinct because they didn’t have a sustainable ecosystem, or they might turn inward. You know, like Easter Island, they got very interested in making statutes and tearing down trees in order to do that. And so they ended up with an island that didn’t have any trees. They didn’t use those trees to build ships so they could populate the rest of the planet. They just miscalculated.

So all of those could be barriers. I don’t know which of them it is. There probably are many planets and moons where if we transplanted life, it would thrive there. But it could be that just making life in the first place is hard and then making intelligence and civilizations that care to grow outside of their planet. It might be hard to detect them if they’re growing in a subtle way.

Lucas Perry: I think the first thing you brought up might be earlier than some people expect, but I think for many people thinking about great filters it is not like abiogenesis, if that’s the right word, seems really hard getting the first self-replicating things in the ancient oceans going. There seemed to be loss of potential filters from there to multi-cellular organisms and then general intelligences like people and beyond.

George Church: But many empires have just become complacent and they’ve been overtaken by perfectly obvious technology that they could’ve at least kept up with by spying, if not by invention. But they became complacent. They seem to plateau at roughly the same place. We’re plateauing more or less the same place the Easter Islanders and the Roman Empire plateaued. Today I mean the slight differences that we are maybe space faring civilization now.

Lucas Perry: Barely.

George Church: Yeah.

Lucas Perry: So, climate change has been something that you’ve been thinking about a bunch it seems. You have the Woolly Mammoth Project which we don’t need to necessarily get into here. But are you considering or are you optimistic about other methods of using genetic engineering for combating climate change?

George Church: Yeah, I think genetic engineering has potential. Most of the other things we talk about putting in LEDs or slightly more efficient car engines, solar power and so forth. And these are slowing down the inevitable rather than reversing it. To reverse it we need to take carbon out of the air, and a really, great way to do that is with photosynthesis, partly because it builds itself. So if we just allow the Arctic to do the photosynthesis the way it used to, we could get a net loss of carbon dioxide from the atmosphere and put it into the ground rather than releasing a lot.

That’s part of the reason that I’m obsessed with Arctic solutions and the Arctic Ocean is also similar. It’s the place where you get upwelling of nutrients, and so you get a natural, very high rate of carbon fixation. It’s just you also have a high rate of carbon consumption back into carbon dioxide. So if you could change that cycle a little bit. So that I think both Arctic land and ocean is a very good place to reverse carbon and accumulation in the atmosphere, and I think that that is best done with synthetic biology.

Now the barriers have historically been release of recombinant DNA into the wild. We now have salmon which are essentially in the wild, the humans that are engineered that are in the wild, and we have golden rice is now finally after more than a decade of tussle being used in the Philippines.

So I think we’re going to see more and more of that. To some extent even the plants of agriculture are in the wild. This is one of the things that was controversial, was that the pollen was going all over the place. But I think there’s essentially zero examples of recombinant DNA causing human damage. And so we just need to be cautious about our environmental decision making.

Lucas Perry: All right. Now taking kind of a sharp pivot here. In the philosophy of consciousness there is a distinction between the hard problem of consciousness and the easy problem. The hard problem is why is it that computational systems have something that it is like to be that system? Why is there a first person phenomenal perspective and experiential perspective filled with what one might call qualia. Some people reject the hard problem as being an actual thing and prefer to say that consciousness is an illusion or is not real. Other people are realists about consciousness and they believe phenomenal consciousness is substantially real and is on the same ontological or metaphysical footing as other fundamental forces of nature, or that perhaps consciousness discloses the intrinsic nature of the physical.

And then the easy problems are how is that we see, how is that light enters the eyes and gets computed, how is it that certain things are computationally related to consciousness?

David Chalmers calls another problem here, the meta problem of consciousness, which is why is it that we make reports about consciousness? Why is that we even talk about consciousness? Particularly if it’s an illusion? Maybe it’s performing some kind of weird computational efficiency. And if it is real, there seems to be some tension between the standard model of physics, being pretty complete feeling, and then how is it that we would be making reports about something that doesn’t have real causal efficacy if there’s nothing real to add to the standard model?

Now you have the Human Connectome Project which would seem to help a lot with the easy problems of consciousness and maybe might have something to say about the meta problem. So I’m curious to know if you have particular views on consciousness or how the Human Connectome Project might relate to that interest?

George Church: Okay. So I think that consciousness is real and it has selective advantage. Part of reality to a biologist is evolution, and I think it’s somewhat coupled to free will. I think of them as even though they are real and hard to think about, they may be easier than we often lay on, and this is when you think of it from an evolutionary standpoint or also from a simulation standpoint.

I can really only evaluate consciousness and the qualia by observations. I can only imagine that you have something similar to what I feel by what you do. And from that standpoint it wouldn’t be that hard to make a synthetic system that displayed consciousness that would be nearly impossible to refute. And as that system replicated and took on a life of its own, let’s say it’s some hybrid biological, non-biological system that displays consciousness, to really convincingly display consciousness it would also have to have some general intelligence or at least pass the Turing test.

But it would have evolutionary advantage in that it could think or could reason about itself. It recognizes the difference between itself and something else. And this has been demonstrated already in robots. There are admittedly kind of proof of concept demos. Like you have robots that can tell themselves in a reflection in a mirror from other people to operate upon their own body by removing dirt from their face, which is only demonstrated in a handful of animal species and recognize their own voice.

So you can see how these would have evolutionary advantages and they could be simulated to whatever level of significance is necessarily to convince an objective observer that they are conscious as far as you know, to the same extent that I know that you are.

So I think the hard problem is a worthy one. I think it is real. It has evolutionary consequences. And free will is related in that free will I think is a game theory which is if you behave in a completely deterministic predictable way, all the organisms around you have an advantage over you. They know that you are going to do a certain thing and so they can anticipate that, they can steal your food, they can bite you, they can do whatever they want. But if you’re unpredictable, which is essentially free will, in this case it can be a random number generator or dice, you now have a selective advantage. And to some extent you could have more free will than the average human, though the average human is constrained by all sorts of social mores and rules and laws and things like that, that something with more free will might not be.

Lucas Perry: I guess I would just want to tease a part self-consciousness from consciousness in general. I think that one can have a first person perspective without having a sense of self or being able to reflect on one’s own existence as a subject in the world. I also feel a little bit confused about why consciousness would provide an evolutionary advantage, where consciousness is the ability to experience things, I guess I have some intuitions about it not being causal like having causal efficacy because the standard model doesn’t seem to be missing anything essentially.

And then your point on free will makes sense. I think that people mean very different things here. I think within common discourse, there is a much more spooky version of free will which we can call libertarian free will, which says that you could’ve done otherwise and it’s more closely related to religion and spirituality, which I reject and I think most people listening to this would reject. I just wanted to point that out. Your take on free will makes sense and is the more scientific and rational version.

George Church: Well actually, I could say they could’ve done otherwise. If you consider that religious, that is totally compatible with flipping the coin. That helps you do otherwise. If you could take the same scenario, you could do something differently. And that ability to do otherwise is of selective advantage. As indeed religions can be of a great selective advantage in certain circumstances.

So back to consciousness versus self-consciousness, I think they’re much more intertwined. I’d be cautious about trying to disentangle them too much. I think your ability to reason about your own existence as being separate from other beings is very helpful for say self-grooming, for self-protection, so forth. And I think that maybe consciousness that is not about oneself may be a byproduct of that.

The greater your ability to reason about yourself versus others, your hand versus the piece of wood in your hands makes you more successful. Even if you’re not super intelligent, just the fact that you’re aware that you’re different from the entity that you’re competing with is a advantage. So I find it not terribly useful to make a giant rift between consciousness and self-consciousness.

Lucas Perry: Okay. So I’m becoming increasingly mindful of your time. We have five minutes left here so I’ve just got one last question for you and I need just a little bit to set it up. You’re vegan as far as I understand.

George Church: Yes.

Lucas Perry: And the effective altruism movement is particularly concerned with animal suffering. We’ve talked a lot about genetic engineering and its possibilities. David Pearce has written something called The Hedonistic Imperative which outlines a methodology and philosophy for using genetic engineering for voluntarily editing out suffering. So that can be done both for wild animals and it could be done for the human species and our descendants.

So I’m curious to know what your view is on animal suffering generally in the world, and do you think about or have thoughts on genetic engineering for wild animal suffering in places outside of human civilization? And then finally, do you view a role for genetic engineering and phasing out human suffering, making it biologically impossible by re-engineering people to operate on gradients of intelligent bliss?

George Church: So I think this kind of difficult problem, a technique that I employ is I imagine what this would be like on another planet and in the future, and whether given that imagined future, we would be willing to come back to where we are now. Rather than saying whether we’re willing to go forward, they ask whether you’re willing to come back. Because there’s a great deal of appropriate respect for inertia and the way things have been. Sometimes it’s called natural, but I think natural includes the future and everything that’s manmade, as well, we’re all part of nature. So I think it’s more of the way things were. So if you go to the future and ask whether we’d be willing to come back is a different way of looking.

I think in going to another planet, we might want to take a limited set of organisms with us, and we might be tempted to make them so that they don’t suffer, including humans. There is a certain amount of let’s say pain which could be a little red light going off on your dashboard. But the point of pain is to get your attention. And you could reframe that. People are born with chronic insensitivity to pain, CIPA, genetically, and they tend to get into problems because they will chew their lips and other body parts and get infected, or they will jump from high places because it doesn’t hurt and break things they shouldn’t break.

So you need some kind of alarm system that gets your attention that cannot be ignored. But I think it could be something that people would complain about less. It might even be more effective because you could prioritize it.

I think there’s a lot of potential there. By studying people that have chronic insensitivity to pain, you could even make that something you could turn on and off. SCNA9 for example is a channel in human neuro system that doesn’t cause the dopey effects of opioids. You can be pain-free without being compromised intellectually. So I think that’s a very promising direction to think about this problem.

Lucas Perry: Just summing that up. You do feel that it is technically feasible to replace pain with some other kind of informationally sensitive thing that could have the same function for reducing and mitigating risk and signaling damage?

George Church: We can even do better. Right now we’re unaware of certain physiological states can be quite hazardous and we’re blind to for example all the pathogens in the air around us. These could be new signaling. It wouldn’t occur to me to make every one of those painful. It would be better just to see the pathogens and have little alarms that go off. It’s much more intelligent.

Lucas Perry: That makes sense. So wrapping up here, if people want to follow your work, or follow you on say Twitter or other social media, where is the best place to check out your work and to follow what you do?

George Church: My Twitter is @geochurch. And my website is easy to find just by google, but it’s arep.med.harvard.edu. Those are two best places.

Lucas Perry: All right. Thank you so much for this. I think that a lot of the information you provided about the skillfulness and advantages of biology and synthetic computation will challenge many of the intuitions of our usual listeners and people in general. I found this very interesting and valuable, and yeah, thanks so much for coming on.

George Church: Okay. Great. Thank you.

FLI Podcast: On Superforecasting with Robert de Neufville

Essential to our assessment of risk and ability to plan for the future is our understanding of the probability of certain events occurring. If we can estimate the likelihood of risks, then we can evaluate their relative importance and apply our risk mitigation resources effectively. Predicting the future is, obviously, far from easy — and yet a community of “superforecasters” are attempting to do just that. Not only are they trying, but these superforecasters are also reliably outperforming subject matter experts at making predictions in their own fields. Robert de Neufville joins us on this episode of the FLI Podcast to explain what superforecasting is, how it’s done, and the ways it can help us with crucial decision making. 

Topics discussed in this episode include:

  • What superforecasting is and what the community looks like
  • How superforecasting is done and its potential use in decision making
  • The challenges of making predictions
  • Predictions about and lessons from COVID-19

You can take a survey about the podcast here

Submit a nominee for the Future of Life Award here

 

Timestamps: 

0:00 Intro

5:00 What is superforecasting?

7:22 Who are superforecasters and where did they come from?

10:43 How is superforecasting done and what are the relevant skills?

15:12 Developing a better understanding of probabilities

18:42 How is it that superforecasters are better at making predictions than subject matter experts?

21:43 COVID-19 and a failure to understand exponentials

24:27 What organizations and platforms exist in the space of superforecasting?

27:31 Whats up for consideration in an actual forecast

28:55 How are forecasts aggregated? Are they used?

31:37 How accurate are superforecasters?

34:34 How is superforecasting complementary to global catastrophic risk research and efforts?

39:15 The kinds of superforecasting platforms that exist

43:00 How accurate can we get around global catastrophic and existential risks?

46:20 How to deal with extremely rare risk and how to evaluate your prediction after the fact

53:33 Superforecasting, expected value calculations, and their use in decision making

56:46 Failure to prepare for COVID-19 and if superforecasting will be increasingly applied to critical decision making

01:01:55 What can we do to improve the use of superforecasting?

01:02:54 Forecasts about COVID-19

01:11:43 How do you convince others of your ability as a superforecaster?

01:13:55 Expanding the kinds of questions we do forecasting on

01:15:49 How to utilize subject experts and superforecasters

01:17:54 Where to find and follow Robert

 

Citations: 

The Global Catastrophic Risk Institute

NonProphets podcast

Robert’s Twitter and his blog Anthropocene

If you want to try making predictions, you can try Good Judgement Open or Metaculus

 

This podcast is possible because of the support of listeners like you. If you found this conversation to be meaningful or valuable consider supporting it directly by donating at futureoflife.org/donate. Contributions like yours make these conversations possible.

All of our podcasts are also now on Spotify and iHeartRadio! Or find us on SoundCloudiTunesGoogle Play and Stitcher.

You can listen to the podcast above or read the transcript below. 

Lucas Perry: Welcome to the Future of Life Institute Podcast. I’m Lucas Perry. Today we have a conversation with Robert de Neufville about superforecasting. But, before I get more into the episode I have two items I’d like to discuss. The first is that the Future of Life Institute is looking for the 2020 recipient of the Future of Life Award. For those not familiar, the Future of Life Award is a $50,000 prize that we give out to an individual who, without having received much recognition at the time of their actions, has helped to make today dramatically better than it may have been otherwise. The first two recipients were Vasili Arkhipov and Stanislav Petrov, two heroes of the nuclear age. Both took actions at great personal risk to possibly prevent an all-out nuclear war. The third recipient was Dr. Matthew Meselson, who spearheaded the international ban on bioweapons. Right now, we’re not sure who to give the 2020 Future of Life Award to. That’s where you come in. If you know of an unsung hero who has helped to avoid global catastrophic disaster, or who have done incredible work to ensure a beneficial future of life, please head over to the Future of Life Award page and submit a candidate for consideration. The link for that page is on the page for this podcast or the description of wherever you might be listening. You can also just search for it directly. If your candidate is chosen, you will receive $3,000 as a token of our appreciation. We’re also incentivizing the search via MIT’s successful red balloon strategy, where the first to nominate the winner gets $3,000 as mentioned, but there are also tiered pay outs to the person who invited the nomination winner, and so on. You can find details about that on the page. 

The second item is that there is a new survey that I wrote about the Future of Life Institute and AI Alignment Podcasts. It’s been a year since our last survey and that one was super helpful for me understanding what’s going well, what’s not, and how to improve. I have some new questions this time around and would love to hear from everyone about possible changes to the introductions, editing, content, and topics covered. So, if you have any feedback, good or bad, you can head over to the SurveyMonkey poll in the description of wherever you might find this podcast or on the page for this podcast. You can answer as many or as little of the questions as you’d like and it goes a long way for helping me to gain perspective about the podcast, which is often hard to do from my end because I’m so close to it. 

And if you find the content and subject matter of this podcast to be important and beneficial, consider sharing it with friends, subscribing on Apple Podcasts, Spotify, or whatever your preferred listening platform, and leaving us a review. It’s really helpful for getting information on technological risk and the future of life to more people.

Regarding today’s episode, I just want to provide a little bit of context. The foundation of risk analysis has to do with probabilities. We use these probabilities and the predicted value lost if certain risks occur to calculate or estimate expected value. This in turn helps us to prioritize risk mitigation efforts to where it’s truly needed. So, it’s important that we’re able to make accurate predictions about the likelihood of future events and risk so that we can take the appropriate action to mitigate them. This is where superforecasting comes in.

Robert de Neufville is a researcher, forecaster, and futurist with degrees in government and political science from Harvard and Berkeley. He works particularly on the risk of catastrophes that might threaten human civilization. He is also a “superforecaster”, since he was among the top 2% of participants in IARPA’s Good Judgment forecasting tournament. He has taught international relations, comparative politics, and political theory at Berkeley and San Francisco State. He has written about politics for The Economist, The New Republic, The Washington Monthly, and Big Think. 

And with that, here’s my conversation with Robert de Neufville on superforecasting. 

All right. Robert, thanks so much for coming on the podcast.

Robert de Neufville: It’s great to be here.

Lucas Perry: Let’s just start off real simply here. What is superforecasting? Say if you meet someone, a friend or family member of yours asks you what you do for work. How do you explain what superforecasting is?

Robert de Neufville: I just say that I do some forecasting. People understand what forecasting is. They may not understand specifically the way I do it. I don’t love using “superforecasting” as a noun. There’s the book Superforecasting. It’s a good book and it’s kind of great branding for Good Judgment, the company, but it’s just forecasting, right, and hopefully I’m good at it and there are other people that are good at it. We have used different techniques, but it’s a little bit like an NBA player saying that they play super basketball. It’s still basketball.

But what I tell people for background is that the US intelligence community had this forecasting competition basically just to see if anyone could meaningfully forecast the future because it turns out one of the things that we’ve seen in the past is that people who supposedly have expertise in subjects don’t tend to be very good at estimating probabilities that things will happen.

So the question was, can anyone do that? And it turns out that for the most part people can’t, but a small subset of people in the tournament were consistently more accurate than the rest of the people. And just using open source information, we were able to decisively beat subject matter experts who actually that’s not a high bar. They don’t do very well. And we were also able to beat intelligence community analysts. We didn’t originally know we were going up against them, but we’re talking about forecasters in the intelligence community who had access to classified information we didn’t have access to. We were basically just using Google.

And one of the stats that we got later was that as a group we were more accurate 300 days ahead of a question being resolved than others were just a hundred days ahead. As far as what makes the technique of superforecasting sort of fundamentally distinct, I think one of the things is that we have a system for scoring our accuracy. A lot of times when people think about forecasting, people just make pronouncements. This thing will happen or it won’t happen. And then there’s no real great way of checking whether they were right. And they can also often after the fact explain away their forecast. But we make probabilistic predictions and then we use a mathematical formula that weather forecasters have used to score them. And then we can see whether we’re doing well or not well. We can evaluate and say, “Hey look, we actually outperformed these other people in this way.” And we can also then try to improve our forecasting when we don’t do well, ask ourselves why and try to improve it. So that’s basically how I explain it.

Lucas Perry: All right, so can you give me a better understanding here about who “we” is? You’re saying that the key point and where this started was this military competition basically attempting to make predictions about the future or the outcome of certain events. What are the academic and intellectual foundations of superforecasting? What subject areas would one study or did superforecasters come from? How was this all germinated and seeded prior to this competition?

Robert de Neufville: It actually was the intelligence community, although though I think military intelligence participated in this. But I mean I didn’t study to be a forecaster and I think most of us didn’t. I don’t know if there really has been a formal study that would lead you to be a forecaster. People just learn subject matter and then apply that in some way. There must be some training that people had gotten in the past, but I don’t know about it.

There was a famous study by Phil Tetlock. I think in the 90s it came out as a book called Expert Political Judgment, and he found essentially that experts were not good at this. But what he did find, he made a distinction between foxes and hedgehogs you might’ve heard. Hedgehogs are people that have one way of thinking about things, one system, one ideology, and they apply it to every question, just like the hedgehog has one trick and it’s its spines. Hedgehogs didn’t do well. If you were a Marxist or equally a dyed in the wool Milton Friedman capitalist and you applied that way of thinking to every problem, you tended not to do as well at forecasting.

But there’s this other group of people that he found did a little bit better and he called him foxes, and foxes are tricky. They have all sorts of different approaches. They don’t just come in with some dogmatic ideology. They look at things from a lot of different angles. So that was sort of the initial research that inspired him. And there’s other people that were talking about this, but it was ultimately Phil Tetlock and Barb Miller’s group that outperformed everyone else, had looked for people that were good at forecasting and they put them together in teams, and they aggregated their scores with algorithmic magic.

We had a variety of different backgrounds. If you saw any of the press initially, the big story that came out in the press was that we were just regular people. There was a lot of talk about so-and-so was a housewife and that’s true. We weren’t people that had a reputation for being great pundits or anything. That’s totally true. I think that was a little bit overblown though because it made it sound like so and so was a housewife and no one knew that she had this skill. Otherwise she was completely unremarkable. In fact, superforecasters as a group tended to be highly educated with advanced degrees. They tended to have backgrounds and they lived in a bunch of different countries.

The thing that correlates most with forecasting ability seems to be basically intelligence, performing well on measures of intelligence tests, and also I should say that a lot of very smart people aren’t good forecasters. Just being smart isn’t enough, but that’s one of the strongest predictors of forecasting ability and that’s not as good a story for journalists.

Lucas Perry: So it wasn’t crystals.

Robert de Neufville: If you do surveys of the way superforecasters think about the world, they tend not to do what you would call magical thinking. Some of us are religious. I’m not. But for the most part the divine isn’t an explanation in their forecast. They don’t use God to explain it. They don’t use things that you might consider a superstition. Maybe that seems obvious, but it’s a very rational group.

Lucas Perry: How’s superforecasting done and what kinds of models are generated and brought to bear?

Robert de Neufville: As a group, we tend to be very numeric. That’s one thing that correlates pretty well with forecasting ability. And when I say they come from a lot of backgrounds, I mean there are doctors, pharmacists, engineers. I’m a political scientist. There are actually a fair number of political scientists. Some people who are in finance or economics, but they all tend to be people who could make at least a simple spreadsheet model. We’re not all statisticians, but have at least a intuitive familiarity with statistical thinking and intuitive concept of Bayesian updating.

As far as what the approach is, we make a lot of simple models, often not very complicated models I think because often when you make a complicated model, you end up over fitting the data and drawing falsely precise conclusions, at least when we’re talking about complex, real-world political science-y kind of situations. But I would say the best guide for predicting the future, and this probably sounds obvious, best guide for what’s going to happen is what’s happened in similar situations in the past. One of the key things you do, if somebody asks you, “Will so and so when an election?” you would look back and say, “Well, what’s happened in similar elections in the past? What’s the base rate of the incumbent, for example, maybe from this party or that party winning an election, given this economy and so on?”

Now it is often very hard to beat simple algorithms that try to do the same thing, but that’s not a thing that you can just do by rote. It requires an element of judgment about what situations in the past count as similar to the situation you’re trying to ask a question about. In some ways that’s a big part of the trick is to figure out what’s relevant to the situation, trying to understand what past events are relevant, and that’s something that’s hard to teach I think because you could make a case for all sorts of things being relevant and there’s an intuitive feel that’s hard to explain to someone else.

Lucas Perry: The things that seem to be brought to bear here would be like these formal mathematical models and then the other thing would be what I think comes from Daniel Kahneman and is borrowed by the rationalist community, this idea of system one and system two thinking.

Robert de Neufville: Right.

Lucas Perry: Where system one’s, the intuitive, the emotional. We catch balls using system one. System one says the sun will come out tomorrow.

Robert de Neufville: Well hopefully the system two does too.

Lucas Perry: Yeah. System two does too. So I imagine some questions are just limited to sort of pen and paper system one, system two thinking, and some are questions that are more suitable for mathematical modeling.

Robert de Neufville: Yeah, I mean some questions are more suitable for mathematical modeling for sure. I would say though the main system we use is system two. And this is, as you say, we catch balls with some sort of intuitive reflex. It’s sort of maybe not in our prefrontal cortex. If I were trying to calculate the trajectory of a ball and tried to catch it, that would work very well. But I think most of what we’re doing when we forecast is trying to calculate something else. Often the models are really simple. It might be as simple as saying, “This thing has happened seven times in the last 50 years, so let’s start from the idea there’s a 14% chance of that thing happening again.” It’s analytical. We don’t necessarily just go with the gut and say this feels like a one in three chance.

Now that said, I think that it helps a lot and this is a problem with applying the results of our work. It helps a lot to have a good intuitive feel of probability like what one in three feels like, just a sense of how often that is. And superforecasters tend to be people who they are able to distinguish between smaller gradations of probability.

I think in general people that don’t think about this stuff very much, they have kind of three probabilities: definitely going to happen, might happen, and will never have. And there’s no finer grain distinction there. Whereas, I think superforecasters often feel like they can distinguish between 1% or 2% probabilities, the difference between 50% and 52%.

The sense of what that means I think is a big thing. If we’re going to tell a policymaker there’s a 52% chance of something happening, a big part of the problem is that policymakers have no idea what that means. They’re like, “Well, will it happen or won’t it? Oh, what do I do at number?” Right? How is that different from 50%? And I

Lucas Perry: All right, so a few things I’m interested in here. The first is I’m interested in what you have to say about what it means and how one learns how probabilities work. If you were to explain to policymakers or other persons who are interested who are not familiar with working with probabilities a ton, how one can get a better understanding of them and what that looks like. I feel like that would be interesting and helpful. And then the other thing that I’m sort of interested in getting a better understanding of is most of what is going on here seems like a lot of system two thinking, but I also would suspect and guess that many of the top superforecasters have very excellent, finely tuned system ones.

Robert de Neufville: Yeah.

Lucas Perry: Curious if you have any thoughts about these two things.

Robert de Neufville: I think that’s true. I mean, I don’t know exactly what counts as system one in the cognitive psych sense, but I do think that there is a feel that you get. It’s like practicing a jump shot or something. I’m sure Steph Curry, not that I’m Steph Curry in forecasting, but sure, Steph Curry, when he takes a shot, isn’t thinking about it at the time. He’s just practiced a lot. And by the same token, if you’ve done a lot of forecasting and thought about it and have a good feel for it, you may be able to look at something and think, “Oh, here’s a reasonable forecast. Here’s not a reasonable forecast.” I had that sense recently. When looking at FiveThirtyEight tracking COVID predictions for a bunch of subject matter experts, and they’re honestly kind of doing terribly. And part of it is that some of the probabilities are just not plausible. And that’s immediately obvious to me. And I think to other forecasters spent a lot of time thinking about it.

So I do think that without even having to do a lot of calculations or a lot of analysis, often I have a sense of what’s plausible, what’s in the right range just because of practice. When I’m watching a sporting event and I’m stressed about my team winning, for years before I started doing this, I would habitually calculate the probability of winning. It’s a neurotic thing. It’s like imposing some kind of control. I think I’m doing the same thing with COVID, right? I’m calculating probabilities all the time to make myself feel more in control. But that actually was pretty good practice for getting a sense of it.

I don’t really have the answer to how to teach that to other people except potentially the practice of trying to forecast and seeing what happens and when you’re right and when you’re wrong. Good Judgment does have some training materials that improved forecasting for people validated by research. They involve things about thinking about the base rate of things happening in the past and essentially going through sort of system two approaches, and I think that kind of thing can also really help people get a sense for it. But like anything else, there’s an element of practice. You can get better or worse at it. Well hopefully you get better.

Lucas Perry: So a risk that is 2% likely is two times more likely than a 1% chance risk. How do those feel differently to you than to me or a policymaker who doesn’t work with probabilities a ton?

Robert de Neufville: Well I don’t entirely know. I don’t entirely know what they feel like to someone else. I think I do a lot of one time in 50 that’s what 2% is and one time in a hundred that’s what 1% is. The forecasting platform we use, we only work in integer probabilities. So if it goes below half a percent chance, I’d round down to zero. And honestly I think it’s tricky to get accurate forecasting with low probability events for a bunch of reasons or even to know if you’re doing a good job because you have to do so many of them. I think about fractions often and have a sense of what something happening two times in seven might feel like in a way.

Lucas Perry: So you’ve made this point here that superforecasters are often better at making predictions than subject matter expertise. Can you unpack this a little bit more and explain how big the difference is? You recently just mentioned the COVID-19 virologists.

Robert de Neufville: Virologists, infectious disease experts, I don’t know all of them, but people whose expertise I really admire, who know the most about what’s going on and to whom I would turn in trying to make a forecast about some of these questions. And it’s not really fair because these are people often who have talked to FiveThirtyEight for 10 minutes and produced a forecast. They’re very busy doing other things, although some of them are doing modeling and you would think that they would have thought about some of these probabilities in advance. But one thing that really stands out when you look at those is they’ll give a 5% or 10% chance of something happening, which to me is virtually impossible. And I don’t think it’s their better knowledge of virology that makes them think it’s more likely. I think it’s having thought about what 5% or 10% means a lot. Well, they think it’s not very likely and they assign it, which sounds like a low number. That’s my guess. I don’t really know what they’re doing.

Lucas Perry: What’s an example of that?

Robert de Neufville: Recently there were questions about how many tests would be positive by a certain date, and they assigned a real chance, like a 5% or 10%, I don’t remember exactly the numbers, but way higher than I thought it would be for there being below a certain number of tests. And the problem with that was it would have meant essentially that all of a sudden the number of tests that were happening positive every day would drop off the cliff. Go from, I don’t know how many positive tests are a day, 27,000 in the US all of a sudden that would drop to like 2000 or 3000. And this we’re talking about forecasting like a week ahead. So really a short timeline. It just was never plausible to me that all of a sudden tests would stop turning positive. There’s no indication that that’s about to happen. There’s no reason why that would suddenly shift.

I mean maybe I can always say maybe there’s something that a virologist knows that I don’t, but I have been reading what they’re saying. So how would they think that it would go from 25,000 a day to 2000 a day over the next six days? I’m going to assign that basically a 0% chance.

Another thing that’s really striking, and I think this is generally true and it’s true to some extent of superforecasts, so we’ve had a little bit of an argument on our superforecasting platform, people are terrible at thinking about exponential growth. They really are. They really under predicted the number of cases and deaths even again like a week or two in advance because it was orders of magnitude higher than the number at the beginning of the week. But a computer, they’ve had like an algorithm to fit an exponential curve, would have had no problem doing it. Basically, I think that’s what the good forecasters did is we fit an exponential curve and said, “I don’t even need to know many of the details over the course of a week. My outside knowledge is the progression of the disease and vaccines or whatever isn’t going to make much difference.”

And like I said it’s often hard to beat a simple algorithm, but the virologists and infectious disease experts weren’t applying that simple algorithm, and it’s fair to say, well maybe some public health intervention will change the curve or something like that. But I think they were assigning way too high a probability to the exponential trends stopping. I just think it’s a failure to imagine. You know maybe the Trump administration is motivated reasoning on this score. They kept saying it’s fine. There aren’t very many deaths yet. But it’s easy for someone to project the trajectory a little bit further in the future and say, “Wow, there are going to be.” So I think that’s actually been a major policy issue too is people can’t believe the exponential growth.

Lucas Perry: There’s this tension between not trying to panic everyone in the country or you’re unsure if this is the kind of thing that’s an exponential or you just don’t really intuit how exponentials work. For the longest time, our federal government were like, “Oh, it’s just a person. There’s just like one or two people. They’re just going to get better and that will let go away or something.” What’s your perspective on that? Is that just trying to assuage the populace while they try to figure out what to do or do you think that they actually just don’t understand how exponentials work?

Robert de Neufville: I’m not confident with my theory of mind with people in power. I think one element is this idea that we need to avoid panic and I think that’s probably, they believe in good faith, that’s a thing that we need to do. I am not necessarily an expert on the role of panic in crises, but I think that that’s overblown personally. We have this image of, hey, in the movies, if there’s a disaster, all of a sudden everyone’s looting and killing each other and stuff, and we think that’s what’s going to happen. But actually often in disasters people really pull together and if anything have a stronger sense of community and help their neighbors rather than immediately go and try to steal their supplies. We did see some people fighting over toilet paper on news rolls and there are always people like that, but even this idea that people were hoarding toilet paper, I don’t even think that’s the explanation for why it was out of the stores.

If you tell everyone in the country they need two to three weeks and toilet paper right now today, yeah, of course they’re going to buy it off the shelf. That’s actually just what they need to buy. I haven’t seen a lot of panic. And I honestly am someone, if I had been an advisor to the administrations, I would have said something along the lines of “It’s better to give people accurate information so we can face it squarely than to try to sugarcoat it.”

But I also think that there was a hope that if we pretended things weren’t about to happen or that maybe they would just go away, I think that that was misguided. There seems to be some idea that you could reopen the economy and people would just die but the economy would end up being fine. I don’t think that would be worth it any way. Even if you don’t shut down, the economy’s going to be disrupted by what’s happening. So I think there are a bunch of different motivations for why governments weren’t honest or weren’t dealing squarely with this. It’s hard to know what’s not honesty and what is just genuine confusion.

Lucas Perry: So what organizations exist that are focused on superforecasting? Where or what are the community hubs and prediction aggregation mechanisms for superforecasters?

Robert de Neufville: So originally in the IARPA Forecasting Tournament, there were a bunch of different competing teams, and one of them was run by a group called Good Judgment. And that team ended up doing so well. They ended up basically taking over the later years of the tournament and it became the Good Judgment project. There was then a spinoff. Phil Tetlock and others who were involved with that spun off into something called Good Judgment Incorporated. That is the group that I work with and a lot of the superforecasters that were identified in that original tournament continue to work with Good Judgment.

We do some public forecasting and I try to find private clients interested in our forecasts. It’s really a side gig for me and part of the reason I do it is that it’s really interesting. It gives me an opportunity to think about things in a way and I feel like I’m much better up on certain issues because I’ve thought about them as forecasting questions. So there’s Good Judgment Inc. and they also have something called the Good Judgment Open. They have an open platform where you can forecast the kinds of questions we do. I should say that we have a forecasting platform. They come up with forecastable questions, but forecastable means that they’re a relatively clear resolution criteria.

But also you would be interested in knowing the answer. It wouldn’t be just some picky trivial answer. They’ll have a set resolution date so you know that if you’re forecasting something happening, it has to happen by a certain date. So it’s all very well-defined. And coming up with those questions is a little bit of its own skill. It’s pretty hard to do. So Good Judgment will do that. And they put it on a platform where then as a group we discuss the questions and give our probability estimates.

We operate to some extent in teams and they found there’s some evidence that teams of forecasters, at least good forecasters, can do a little bit better than people on their own. I find it very valuable because other forecasters do a lot of research and they critique my own ideas. There’s concerns about group think, but I think that we’re able to avoid those. I can talk about why if you want. Then there’s also this public platform called Good Judgment Open where they use the same kind of questions and anyone can participate. And they’ve actually identified some new superforecasters who participated on this public platform, people who did exceptionally well, and then they invited them to work with the company as well. There are others. I know a couple of superforecasters who are spinning off their own group. They made an app. I think it’s called Maybe, where you can do your own forecasting and maybe come up with your own questions. And that’s a neat app. There is Metaculus, which certainly tries to apply the same principles. And I know some superforecasters who forecast on Metaculus. I’ve looked at it a little bit, but I just haven’t had time because forecasting takes a fair amount of time. And then there are always prediction markets and things like that. There are a number of other things, I think, that try to apply the same principles. I don’t know enough about the space to know of all of the other platforms and markets that exist.

Lucas Perry: For some more information on the actual act of forecasting that will be put onto these websites, can you take us through something which you have forecasted recently that ended up being true? And tell us how much time it took you to think about it? And what your actual thinking was on it? And how many variables and things you considered?

Robert de Neufville: Yeah, I mean it varies widely. And to some extent it varies widely on the basis of how many times have I forecasted something similar. So sometimes we’ll forecast the change in interest rates, the fed moves. That’s something that’s obviously a lot of interest to people in finance. And at this point, I’ve looked at that kind of thing enough times that I have set ideas about what would make that likely or not likely to happen.

But some questions are much harder. We’ve had questions about mortality in certain age groups in different districts in England and I didn’t know anything about that. And all sorts of things come into play. Is the flu season likely to be bad? What’s the chance of flu season will be bad? Is there a general trend among people who are dying of complications from diabetes? Does poverty matter? How much would Brexit affect mortality chances? Although a lot of what I did was just look at past data and project trends, just basically projecting trends you can get a long way towards an accurate forecast in a lot of circumstances.

Lucas Perry: When such a forecast is made and added to these websites and the question for the thing which is being predicted resolves, what are the ways in which the websites aggregate these predictions? Or are we at the stage of them often being put to use? Or is the utility of these websites currently primarily honing the epistemic acuity of the forecasters?

Robert de Neufville: There are a couple of things. Like I hope that my own personal forecasts are potentially pretty accurate. But when we work together on a platform, we will essentially produce an aggregate, which is, roughly speaking, the median prediction. There’s some proprietary elements to it. They extremize it a little bit, I think, because once you aggregate it kind of blurs things towards the middle. They maybe weight certain forecasts and more recent forecasts differently. I don’t know the details of it. But you can improve accuracy not just by taking the median of our forecast or in a prediction market, but doing a little algorithmic tweaking they found they can improve accuracy a little bit. That’s sort of what happens with our output.

And then as far as how people use it, I’m afraid not very well. There are people who are interested in Good Judgement’s forecasts and who pay them to produce forecasts. But it’s not clear to me what decision makers do with it or if they know what to do.

I think a big problem selling forecasting is that people don’t know what to do with a 78% chance of this, or let’s say a 2% chance of a pandemic in a given year, I’m just making that up. But somewhere in that ballpark, what does that mean about how you should prepare? I think that people don’t know how to work with that. So it’s not clear to me that our forecasts are necessarily affecting policy. Although it’s the kind of thing that gets written up in the news and who knows how much that affects people’s opinions, or they talk about it at Davos and maybe those people go back and they change what they’re doing.

Certain areas, I think people in finance know how to work with probabilities a little bit better. But they also have models that are fairly good at projecting certain types of things, so they’re already doing a reasonable job, I think.

I wish it were used better. If I were the advisor to a president, I would say you should create a predictive intelligence unit using superforecasters. Maybe give them access to some classified information, but even using open source information, have them predict probabilities of certain kinds of things and then develop a system for using that in your decision making. But I think we’re a fair ways away from that. I don’t know any interest in that in the current administration.

Lucas Perry: One obvious leverage point for that would be if you really trusted this group of superforecasters. And the key point for that is just simply how accurate they are. So just generally, how accurate is superforecasting currently? If we took the top 100 superforecasters in the world, how accurate are they over history?

Robert de Neufville: We do keep score, right? But it depends a lot on the difficulty of the question that you’re asking. If you ask me whether the sun will come up tomorrow, yeah, I’m very accurate. If you asked me to predict a random number generator, but you want a 100, I’m not very accurate. And it’s hard often to know with a given question how hard it is to forecast.

I have what’s called a Brier score. Essentially a mathematical way of correlating your forecast, the probabilities you give with the outcomes. A lower Brier score essentially is a better fit. I can tell you what my Brier score was on the questions I forecasted in the last year. And I can tell you that it’s better than a lot of other people’s Brier scores. And that’s the way you know I’m doing a good job. But it’s hard to say how accurate that is in some absolute sense.

It’s like saying how good are NBA players and taking jump shots. It depends where they’re shooting from. That said, I think broadly speaking, we are the most accurate. So far, superforecasters had a number of challenges. And I mean I’m proud of this. We pretty much crushed all comers. They’ve tried to bring artificial intelligence into it. We’re still, I think as far as I know, the gold standard of forecasting. But we’re not prophets by any means. Accuracy for us is saying there’s a 15% chance of this thing in politics happening. And then when we do that over a bunch of things, yeah, 15% of them end up happening. It is not saying this specific scenario will definitely come to pass. We’re not prophets. Getting the well calibrated probabilities over a large number of forecasts is the best that we can do, I think, right now and probably in the near future for these complex political social questions.

Lucas Perry: Would it be skillful to have some sort of standardized group of expert forecasters rank the difficulty of questions, which then you would be able to better evaluate and construct a Brier score for persons?

Robert de Neufville: It’s an interesting question. I think I could probably tell you, I’m sure other forecasters could tell you which questions are relatively easier or harder to predict. Things where there’s a clear trend and there’s no good reason for it changing are relatively easy to predict. Things where small differences could make it tip into a lot of different end states are hard to predict. And I can sort of have a sense initially what those would be.

I don’t know what the advantage of ranking questions like that and then trying to do some weighted adjustment. I mean maybe you could. But the best way that I know of to really evaluate forecasting scale is to compare it with other forecasters. I’d say it’s kind of a baseline. What do you know other good forecasters come up with and what do average forecasters come up with? And can you beat prediction markets? I think that’s the best way of evaluating relative forecasting ability. But I’m not sure it’s possible that some kind of weighting would be useful in some context. I hadn’t really thought about it.

Lucas Perry: All right, so you work both as a superforecaster, as we’ve been talking about, but you also have a position at the Global Catastrophic Risk Institute. Can you provide a little bit of explanation for how superforecasting and existential and global catastrophic risk analysis are complimentary?

Robert de Neufville: What we produce at GCRI, a big part of our product is academic research. And there are a lot of differences. If I say there’s a 10% chance of something happening on a forecasting platform, I have an argument for that. I can try to convince you that my rationale is good. But it’s not the kind of argument that you would make in an academic paper. It wouldn’t convince people it was 100% right. My warrant for saying that on the forecasting platform is I have a track record. I’m good at figuring out what the correct argument is or have been in the past, but producing an academic paper is a whole different thing.

There’s some of the same skills, but we’re trying to produce a somewhat different output. What superforecasters say is an input in writing papers about catastrophic risk or existential risk. We’ll use what superforecasters think as a piece of data. That said, superforecasters are validated at doing well at certain category of political, social economic questions. And over a certain timeline, we know that we outperform others up to like maybe two years.

We don’t really know if we can do meaningful forecasting 10 years out. That hasn’t been validated. You can see why that would be difficult to do. You would have to have a long experiment to even figure that out. And it’s often hard to figure out what the right questions to ask about 2030 would be. I generally think that the same techniques we use would be useful for forecasting 10 years out, but we don’t even know that. And so a lot of the things that I would look at in terms of global catastrophic risk would be things that might happen at some distant point in the future. Now what’s the risk that there will be a nuclear war in 2020, but also over the next 50 years? It’s a somewhat different thing to do.

They’re complementary. They both involve some estimation of risk and they use some of the same techniques. But the longer term aspect … The fact that as I think I said, one of the best ways superforecasters do well is that they use the past as a guide to the future. A good rule of thumb is that the status quo is likely to be the same. There’s a certain inertia. Things are likely to be similar in a lot of ways to the past. I don’t know if that’s necessarily very useful for predicting rare and unprecedented events. There is no precedent for an artificial intelligence catastrophe, so what’s the base rate of that happening? It’s never happened. I can use some of the same techniques, but it’s a little bit of a different kind of thing.

Lucas Perry: Two people are coming to my mind of late. One is Ray Kurzweil, who has made a lot of longterm technological predictions about things that have not happened in the past. And then also curious to know if you’ve read The Precipice: Existential Risk and the Future of Humanity by Toby Ord. Toby makes specific predictions about the likelihood of existential and global catastrophic risks in that book. I’m curious if you have any perspective or opinion or anything to add on either of these two predictors or their predictions?

Robert de Neufville: Yeah, I’ve read some good papers by Toby Ord. I haven’t had a chance to read the book yet, so I can’t really comment on that. I really appreciate Ray Kurzweil. And one of the things he does that I like is that he holds himself accountable. He’s looked back and said, how accurate are my predictions? Did this come true or did that not come true? I think that is a basic hygiene point of forecasting. You have to hold yourself accountable and you can’t just go back and say, “Look, I was right,” and not rationalize whatever somewhat off forecasts you’ve made.

That said, when I read Kurzweil, I’m skeptical, maybe that’s my own inability to handle exponential change. When I look at his predictions for certain years, I think he does a different set of predictions for seven year periods. I thought, “Well, he’s actually seven years ahead.” That’s pretty good actually, if you’re predicting what things are going to be like in 2020, but you just think it’s going to be 2013. Maybe they get some credit for that. But I think that he is too aggressive and optimistic about the pace of change. Obviously exponential change can happen quickly.

But I also think another rule of thumb is that things take a long time to go through beta. There’s the planning fallacy. People always think that projects are going to take less time than they actually do. And even when you try to compensate for the planning fallacy and double the amount of time, it still takes twice as much time as you come up with. I tend to think Kurzweil sees things happening sooner than they will. He’s a little bit of a techno optimist, obviously. But I haven’t gone back and looked at all of his self evaluation. He scores himself pretty well.

Lucas Perry: So we’ve spoken a bit about the different websites. And what are they technically called, what is the difference between a prediction market and … I think Metaculus calls itself a massive online prediction solicitation and aggregation engine, which is not a prediction market. What are the differences here and how’s the language around these platforms used?

Robert de Neufville: Yeah, so I don’t necessarily know all the different distinction categories someone would make. I think a prediction market particularly is where you have some set of funds, some kind of real or fantasy money. We used one market in the Good Judgement project. Our money was called Inkles and we could spend that money. And essentially, they traded probabilities like you would trade a share. So if there was a 30% chance of something happening on the market, that’s like a price of 30 cents. And you would buy that for 30 cents and then if people’s opinions about how likely that was changed and a lot of people bought it, then we could bid up to 50% chance of happening and that would be worth 50 cents.

So if I correctly realize that something … that the market says is a 30% chance of happening, if I correctly realized that, that’s more likely, I would buy shares of that. And then eventually either other people would realize it, too, or it would happen. I should say that when things happened, then you’d get a dollar, then it’s suddenly it’s 100% chance of happening.

So if you recognize that something had a higher percent chance of happening than the market was valuing at, you could buy a share of that and then you would make money. That basically functions like a stock market, except literally what you’re trading is directly the probability of a question will answer yes or no.

The stock market’s supposed to be really efficient, and I think in some ways it is. I think prediction markets are somewhat useful. Big problem with prediction markets is that they’re not liquid enough, which is to say that a stock market, there’s so much money going around and people are really just on it to make money, that it’s hard to manipulate the prices.

There’s plenty of liquidity on the prediction markets that I’ve been a part of. Like for the one on the Good Judgement project, for example, sometimes there’d be something that would say there was like a 95% chance of it happening on the prediction market. In fact, there would be like a 99.9% chance of it happening. But I wouldn’t buy that share, even though I knew it was undervalued, because the return on investment wasn’t as high as it was on some other questions. So it would languish at this inaccurate probability, because there just wasn’t enough money to chase all the good investments.

So that’s one problem you can have in a prediction market. Another problem you can have … I see it happen with PredictIt, I think. They used to be the IO Exchange predicting market. People would try to manipulate the market for some advertising reason, basically.

Say you were working on a candidate’s campaign and you wanted to make it look like they were a serious contender, it was a cheap investment and you put a lot of money in the prediction market and you boost their chances, but that’s not really boosting their chances. That’s just market manipulation. You can’t really do that with the whole stock market, but prediction markets aren’t well capitalized, you can do that.

And then I really enjoy PredictIt. PredictIt’s one of the prediction markets that exists for political questions. They have some dispensation so that it doesn’t count as gambling in the U.S. Add it’s research purposes: is there some research involved with PredictIt. But they have a lot of fees and they use their fees to pay for the people who run the market. And it’s expensive. But the fees mean that the prices are very sticky and it’s actually pretty hard to make money. Probabilities have to be really out of whack before you can make enough money to cover your fees.

So things like that make these markets not as accurate. I also think that although we’ve all heard about the wisdom of the crowds, and broadly speaking, crowds might do better than just a random person. They can also do a lot of herding behavior that good forecasters wouldn’t do. And sometimes the crowds overreact to things. And I don’t always think the probabilities that prediction markets come up with are very good.

Lucas Perry: All right. Moving along here a bit. Continuing the relationship of superforecasting with global catastrophic and existential risk. How narrowly do you think that we can reduce the error range for superforecasts on low probability events like global catastrophic risks and existential risks? If a group of forecasters settled on a point estimate of 2% chance for some kind of global catastrophic for existential risk, but with an error range of like 1%, that dramatically changes how useful the prediction is, because of its major effects on risk. How accurate do you think we can get and how much do you think we can squish the probability range?

Robert de Neufville: That’s a really hard question. When we produce forecasts, I don’t think there’s necessarily clear error bars built in. One thing that Good Judgement will do, is it will show where forecasters all agreed the probability is 2% and then it will show if there’s actually a wide variation. I’m thinking 0%, some think it’s 4% or something like that. And that maybe tells you something. And if we had a lot of very similar forecasts, maybe you could look back and say, we tend to have an error of this much. But for the kinds of questions we look at with catastrophic risk, it might really be hard to have a large enough “n”. Hopefully it’s hard to have a large “n” where you could really compute an error range. If our aggregate spits out a probability of 2%, it’s difficult to know in advance for a somewhat unique question how far off we could be.

I don’t spend a lot of time thinking about frequentist or Bayesian interpretations or probability or counterfactuals or whatever. But at some point, if I say it has a 2% probability of something and then it happens, I mean it’s hard to know what my probability meant. Maybe we live in a deterministic universe and that was 100% going to happen and I simply failed to see the signs of it. I think that to some extent, what kind of probabilities you assign things depend on the amount of information you get.

Often we might say that was a reasonable probability to assign to something because we couldn’t get much better information. Given the information we had, that was our best estimate of the probability. But it might always be possible to know with more confidence if we got better information. So I guess one thing I would say is if you want to reduce the error on our forecasts, it would help to have better information about the world.

And that’s some extent where what I do with GCRI comes in. We’re trying to figure out how to produce better estimates. And that requires research. It requires thinking about these problems in a systematic way to try to decompose them into different parts and figure out what we can look at the past and use to inform our probabilities. You can always get better information and produce more accurate probabilities, I think.

The best thing to do would be to think about these issues more carefully. Obviously, it’s a field. Catastrophic risk is something that people study, but it’s not the most mainstream field. There’s a lot of research that needs to be done. There’s a lot of low hanging fruit, work that could easily be done applying research done in other fields, to catastrophic risk issues. But they’re just aren’t enough researchers and there isn’t enough funding to do all the work that we should do.

So my answer would be, we need to do better research. We need to study these questions more closely. That’s how we get to better probability estimates.

Lucas Perry: So if we have something like a global catastrophic or existential risk, and say a forecaster says that there’s a less than 1% chance that, that thing is likely to occur. And if this less than 1% likely thing happens in the world, how does that update our thinking about what the actual likelihood of that risk was? Given this more meta point that you glossed over about how if the universe is deterministic, then the probability of that thing was actually more like 100%. And the information existed somewhere, we just didn’t have access to that information or something. Can you add a little bit of commentary here about what these risks mean?

Robert de Neufville: I guess I don’t think it’s that important when forecasting, if I have a strong opinion about whether or not we live in a single deterministic universe where outcomes are in some sense in the future, all sort of baked in. And if only we could know everything, then we would know with a 100% chance everything that was going to happen. Or whether there are some fundamental randomness, or maybe we live in a multiverse where all these different outcomes are happening, you could say that in 30% of the universes in this multiverse, this outcome comes true. I don’t think that really matters for the most part. I do think as a practical question, we may make forecast on the basis of the best information we have, that’s all you can do. But there are some times you look back and say, “Well, I missed this. I should’ve seen this thing.” I didn’t think that Donald Trump would win the 2016 election. That’s literally my worst Brier score ever. I’m not alone in that. And I comfort myself by saying there was actually genuinely small differences made a huge impact.

But there are other forecasters who saw it better than I did. Nate Silver didn’t think that Trump was a lock, but he thought it was more likely and he thought it was more likely for the right reasons. That you would get this correlated polling error in a certain set of states that would hand Trump the electoral college. So in retrospect, I think, in that case I should’ve seen something like what Nate Silver did. Now I don’t think in practice it’s possible to know enough about an election to get in advance who’s going to win.

I think we still have to use the tools that we have, which are things like polling. In complex situations, there’s always stuff that I missed when I make a mistake and I can look back and say I should have done a better job figuring that stuff out. I do think though, with the kinds of questions we forecast, there’s a certain irreducible, I don’t want to say randomness because I’m not making a position on whether the university is deterministic, but irreducible uncertainty about what we’re realistically able to know and we have to base our forecasts on the information that’s possible to get. I don’t think metaphysical interpretation is that important to figuring out these questions. Maybe it comes up a little bit more with unprecedented one-off events. Even then I think you’re still trying to use the same information to estimate probabilities.

Lucas Perry: Yeah, that makes sense. There’s only the set of information that you have access to.

Robert de Neufville: Something actually occurs to me. One of the things that superforecaster are proud of is that we beat these intelligence analysts that had access to classified information and I think that if we had access to more information, I mean we’re doing our research on Google, right? Or maybe occasionally we’ll write a government official and get a FOIA request or something, but we’re using open source intelligence and it, I think it would probably help if we had access to more information that would inform our forecasts, but sometimes more information actually hurts you.

People have talked about a classified information bias that if you have secret information that other people don’t have, you are likely to think that is more valuable and useful than it actually is and you overweight the classified information. But if you had that secret information, I don’t know if it’s an ego thing, you want to have a different forecast than other people don’t have access to. It makes you special. You have to be a little bit careful. More information isn’t always better. Sometimes the easy to find information is actually really dispositive and is enough. And if you search for more information, you can find stuff that is irrelevant to your forecast, but think that it is relevant.

Lucas Perry: So if there’s some sort of risk and the risk occurs, after the fact how does one update what the probability was more like?

Robert de Neufville: It depends a little bit of the context. If you want to evaluate my prediction. If I say I thought there was a 30% chance of the original Brexit vote would be to leave England. That actually was more accurate than some other people, but I didn’t think it was likely. Now in hindsight, should I have said 100%. Somebody might argue that I should have, that if you’d really been paying attention, you would have known 100%.

Lucas Perry: But like how do we know it wasn’t 5% and we live in a rare world?

Robert de Neufville: We don’t. You basically can infer almost nothing from an n of 1. Like if I say there’s a 1% chance of something happening and it happens, you can be suspicious that I don’t know what I’m talking about. Even from that n of 1, but there’s also a chance that there was a 1% chance that it happened and that was the 1 time in a 100. To some extent that could be my defense of my prediction that Hillary was going to win. I should talk about my failures. The night before, I thought there was a 97% chance that Hillary would win the election and that’s terrible. And I think that that was a bad forecast in hindsight. But I will say that typically when I’ve said there’s a 97% chance of something happening, they have happened.

I’ve made more than 30-some predictions that things are going to be 97% percent likely and that’s the only one that’s been wrong. So maybe I’m actually well calibrated. Maybe that was the 3% thing that happened. You can only really judge over a body of predictions and if somebody is always saying there’s a 1% chance of things happening and they always happen, then that’s not a good forecaster. But that’s a little bit of a problem when you’re looking at really rare, unprecedented events. It’s hard to know how well someone does at that because you don’t have an n of hopefully more than 1. It is difficult to assess those things.

Now we’re in the middle of a pandemic and I think that the fact that this pandemic happened maybe should update our beliefs about how likely pandemics will be in the future. There was the Spanish flu and the Asian flu and this. And so now we have a little bit more information about the base rate, which these things happen. It’s a little bit difficult because 1918 is very different from 2020. The background rate of risk, may be very different from what it was in 1918 so you want to try to take those factors into account, but each event does give us some information that we can use for estimating the risk in the future. You can do other things. A lot of what we do as a good forecaster is inductive, right? But you can use deductive reasoning. You can, for example, with rare risks, decompose them into the steps that would have to happen for them to happen.

What systems have to fail for a nuclear war to start? Or what are the steps along the way to potentially an artificial intelligence catastrophe. And I might be able to estimate the probability of some of those steps more accurately than I estimate the whole thing. So that gives us some kind of analytic methods to estimate probabilities even without real base rate of the thing itself happening.

Lucas Perry: So related to actual policy work and doing things in the world. The thing that becomes skillful here seems to be to use these probabilities to do expected value calculations to try and estimate how much resources should be fed into mitigating certain kinds of risks.

Robert de Neufville: Yeah.

Lucas Perry: The probability of the thing happening requires a kind of forecasting and then also the value that is lost requires another kind of forecasting. What are your perspectives or opinions on superforecasting and expected value calculations and their use in decision making and hopefully someday more substantially in government decision making around risk?

Robert de Neufville: We were talking earlier about the inability of policymakers to understand probabilities. I think one issue is that a lot of times when people make decisions, they want to just say, “What’s going to happen? I’m going to plan for the single thing that’s going to happen.” But as a forecaster, I don’t know what’s going to happen. I might if I’m doing a good job, know there’s a certain percent chance that this will happen, a certain percent chance that that will happen. And in general, I think that policymakers need to make decisions over sort of the space of possible outcomes with the planning for contingencies. And I think that is a more complicated exercise than a lot of policymakers want to do. I mean I think it does happen, but it requires being able to hold in your mind all these contingencies and plan for them simultaneously. And I think that with expected value calculations to some extent, that’s what you have to do.

That gets very complicated very quickly. When we forecast questions, we might forecast some discrete fact about the world and how many COVID deaths will there be by a certain date. And it’s neat that I’m good at that, but there’s a lot that that doesn’t tell you about the state of the world at that time. There’s a lot of information that would be valuable making decisions. I don’t want to say infinite because it may be sort of technically wrong, but there is essentially uncountable amount of things you might want to know and you might not even know what the relevant questions to ask about a certain space. So it’s always going to be somewhat difficult to get an expected value calculation because you can sort of not possibly forecast all the things that might determine the value of something.

I mean, this is a little bit of a philosophical critique of consequentialist kind of analyses of things too. Like if you ask if something is good or bad, it may have an endless chain of consequences rippling throughout future history and maybe it’s really a disaster now, but maybe it means that future Hitler isn’t born. How do you evaluate that? It might seem like a silly trivial point, but the fact is it may be really difficult to know enough about the consequences of your action to an expected value calculation. So your expected value calculation may have to be kind of a approximation in a certain sense, given broad things we know these are things that are likely to happen. I still think expected value calculations are good. I just think there’s a lot of uncertainty in them and to some extent it’s probably irreducible. I think it’s always better to think about things clearly if you can. It’s not the only approach. You have to get buy-in from people and that makes a difference. But the more you can do accurate analysis about things, I think the better your decisions are likely to be.

Lucas Perry: How much faith or confidence do you have that the benefits of superforecasting and this kind of thought will increasingly be applied to critical government or non-governmental decision-making processes around risk?

Robert de Neufville: Not as much as I’d like. I think now that we know that people can do a better or worse job of predicting the future, we can use that information and it will eventually begin to be integrated into our governance. I think that that will help. But in general, you know my background’s in political science and political science is, I want to say, kind of discouraging. You learn that even under the best circumstances, outcomes of political struggles over decisions are not optimal. And you could imagine some kind of technocratic decision-making system, but even that ends up having its problems or the technocrats end up just lining their own pockets without even realizing they’re doing it or something. So I’m a little bit skeptical about it and right now what we’re seeing with the pandemic, I think we systematically underprepare for certain kinds of things, that there are reasons why it doesn’t help leaders very much to prepare for things that will never happen.

And with something like a public health crisis, the deliverable is for nothing to happen and if you succeed, it looks like all your money was wasted, but in fact you’ve actually prevented anything from happening and that’s great. The problem is that that creates an underincentive for leaders. They don’t get credit for preventing the pandemic that no one even knew could have happened and they don’t necessarily win the next election or business leaders may not improve their quarterly profits much by preparing for rare risks for that and other reasons too. I think that we’re probably… have a hard time believing cognitively that certain kinds of things that seem crazy like this could happen. I’m somewhat skeptical about that. Now I think in this case we had institutions who did prepare for this, but for whatever reason a lot of governments fail to do what was necessary.

Failed to respond quickly enough or minimize that what was happening. There are worse actors than others, right, but this isn’t a problem that’s just about the US government. This is a problem in Italy, in China, and it’s disheartening because COVID-19 is pretty much exactly one of the major scenarios that infectious disease experts have been warning about. The novel coronavirus that jumps from animals to humans that spread through some kind of respiratory pathway that’s highly infectious, that spreads asymptomatically. This is something that people worried about and knew about and in a sense it was probably only a matter of time that this was going to happen and there might be a small risk in any given year and yet we weren’t ready for it, didn’t take the steps, we lost time. It could have been used saving lives. That’s really disheartening.

I would like to see us learn a lesson from this and I think to some extent, once this is all over, whenever that is, we will probably create some institutional structures, but then we have to maintain them. We tend to forget a generation later about these kinds of things. We need to create governance systems that have more incentive to prepare for rare risks. It’s not the only thing we should be doing necessarily, but we are underprepared. That’s my view.

Lucas Perry: Yeah, and I mean the sample size of historic pandemics is quite good, right?

Robert de Neufville: Yeah. It’s not like we were invaded by aliens. Something like this happens in just about every person’s lifetime. It’s historically not that rare and this is a really bad one, but the Spanish flu and the Asian flu were also pretty bad. We should have known this was coming.

Lucas Perry: What I’m also reminded here of and some of these biases you’re talking about, we have climate change on the other hand, which is destabilizing and kind of global catastrophic risky, depending on your definition and for people who are against climate change, there seems to be A) lack in trust of science and B) then not wanting to invest in expensive technologies or something that seemed wasteful. I’m just reflecting here on all of the biases that fed into our inability to prepare for COVID.

Robert de Neufville: Well, I don’t think the distrust of science is sort of a thing that’s out there. I mean, maybe to some extent it is, but it’s also a deliberate strategy that people with interests in continuing, for example, the fossil fuel economy, have deliberately tried to cloud the issue to create distrust in science to create phony studies that make it seem that climate change isn’t real. We thought a little bit about this at GCRI about how this might happen with artificial intelligence. You can imagine that somebody with a financial interest might try to discredit the risks and make it seem safer than it is, and maybe they even believe that to some extent, nobody really wants to believe that the thing that’s getting them a lot of money is actually evil. So I think distrust in science really isn’t an accident and it’s a deliberate strategy and it’s difficult to know how to combat it. There are strategies you can take, but it’s a struggle, right? There are people who have an interest in keeping scientific results quiet.

Lucas Perry: Yeah. Do you have any thoughts then about how we could increase the uptake of using forecasting methodologies for all manner of decision making? It seems like generally you’re pessimistic about it right now.

Robert de Neufville: Yeah. I am a little pessimistic about it. I mean one thing is that I think that we’ve tried to get people interested in our forecasts and a lot of people just don’t know what to do with them. Now one thing I think is interesting is that often people, they’re not interested in my saying, “There’s a 78% chance of something happening.” What they want to know is, how did I get there? What is my arguments? That’s not unreasonable. I really like thinking in terms of probabilities, but I think it often helps people understand what the mechanism is because it tells them something about the world that might help them make a decision. So I think one thing that maybe can be done is not to treat it as a black box probability, but to have some kind of algorithmic transparency about our thinking because that actually helps people, might be more useful in terms of making decisions than just a number.

Lucas Perry: So is there anything else here that you want to add about COVID-19 in particular? General information or intuitions that you have about how things will go? What the next year will look like? There is tension in the federal government about reopening. There’s an eagerness to do that, to restart the economy. The US federal government and the state governments seem totally unequipped to do the kind of testing and contact tracing that is being done in successful areas like South Korea. Sometime in the short to medium term we’ll be open and there might be the second wave and it’s going to take a year or so for a vaccine. What are your intuitions and feelings or forecasts about what the next year will look like?

Robert de Neufville: Again, with the caveat that I’m not a virologist or not an expert in vaccine development and things like that, I have thought about this a lot. I think there was a fantasy, still is a fantasy that we’re going to have what they call a V-shape recovery that… you know everything crashed really quickly. Everyone started filing for unemployment as all the businesses shut down. Very different than other types of financial crises, this virus economics. But there was this fantasy that we would sort of put everything on pause, put the economy into some cryogenic freeze, and somehow keep people able to pay their bills for a certain amount of time. And then after a few months, we’d get some kind of therapy or vaccine or it would die down and suppress the disease somehow. And then we would just give it a jolt of adrenaline and we’d be back and everyone would be back in their old jobs and things would go back to normal. I really don’t think that is what’s going to happen. I think it is almost thermodynamically harder to put things back together than it is to break them. That there are things about the US economy in particular, the fact that in order to keep getting paid, you actually need to lose your job and go on unemployment, in many cases. It’s not seamless. It’s hard to even get through on the phone lines or to get the funding.

I think that even after a few months, the US economy is going to look like a town that’s been hit by a hurricane and we’re going to have to rebuild a lot of things. And maybe unemployment will go down faster than it did in previous recessions where it was more about a bubble popping or something, but I just don’t think that we go back to normal.

I also just don’t think we go back to normal in a broader sense. This idea that we’re going to have some kind of cure. Again, I’m not a virologist, but I don’t think we typically have a therapy that cures viruses the way you know antibiotics might be super efficacious against bacteria. Typically, viral diseases, I think are things we have to try to mitigate and some cocktail may improve treatments and we may figure out better things to do with ventilators. Well, you might get the fatality rate down, but it’s still going to be pretty bad.

And then there is this idea maybe we’ll have a vaccine. I’ve heard people who know more than I do say maybe it’s possible to get a vaccine by November. But, the problem is until you can simulate with a supercomputer what happens in the human body, you can’t really speed up biological trials. You have to culture things in people and that takes time.

You might say, well, let’s don’t do all the trials, this is an emergency. But the fact is, if you don’t demonstrate that a vaccine is safe and efficacious, you could end up giving something to people that has serious adverse effects, or even makes you more susceptible to disease. That was problem one of the SARS vaccines they tried to come up with. Originally, is it made people more susceptible. So you don’t want to hand out millions and millions of doses of something that’s going to actually hurt people, and that’s the danger if you skip these clinical trials. So it’s really hard to imagine a vaccine in the near future.

I don’t want to sell short human ingenuity because we’re really adaptable, smart creatures, and we’re throwing all our resources at this. But, there is a chance that there is really no great vaccine for this virus. We haven’t had great luck with finding vaccines for coronaviruses. It seems to do weird things to the human immune system and maybe there is evidence that immunity doesn’t stick around that long. It’s possible that we come up with a vaccine that only provides partial immunity and doesn’t last that long. And I think there is a good chance that essentially we have to keep social distancing well into 2021 and that this could be a disease that remains dangerous and we have to continue to keep fighting for years potentially.

I think that we’re going to open up and it is important to open up as soon as we can because what’s happening with the economy will literally kill people and cause famines. But on the other hand, we’re going to get outbreaks that come back up again. You know it’s going to be a like fanning coals if we open up too quickly and in some places we’re not going to get it right and that doesn’t save anyone’s life. I mean, if it starts up again and the virus disrupts the economy again. So I think this is going to be a thing we are struggling to find a balance to mitigate and that we’re not going to go back to December 2019 for a while, not this year. Literally, it may be years.

And I think that although humans have amazing capacity to forget things and go back to normal life. I think that we’re going to see permanent changes. I don’t know exactly what they are. But, I think we’re going to see permanent changes in the way we live. And I don’t know if I’m ever shaking anyone’s hands again. We’ll see about that. A whole generation of people are going to be much better at washing their hands.

Lucas Perry: Yeah. I’ve already gotten a lot better at washing my hands watching tutorials.

Robert de Neufville: I was terrible at it. I had no idea how bad I was.

Lucas Perry: Yeah, same. I hope people who have shaken my hand in the past aren’t listening. So the things that will stop this are sufficient herd immunity to some extent or a vaccine that is efficacious. Those seem like the, okay, it’s about time to go back to normal points, right?

Robert de Neufville: Yeah.

Lucas Perry: A vaccine is not a given thing given the class of coronavirus diseases and how they behave?

Robert de Neufville: Yeah. Eventually now this is where I really feel like I’m not a virologist, but eventually diseases evolve and we co-evolve with them. Whatever the Spanish Flu was, it didn’t continue to kill as many people years down the line. I think that’s because people did develop immunity.

But also, viruses don’t get any evolutionary advantage from killing their hosts. They want to use us to reproduce. Well, they don’t want anything, but that advantages them. If they kill us and make us use mitigation strategies, that hurts their ability to reproduce. So in the long run, and I don’t know how long that run is, but eventually we co-evolve with it and it becomes endemic instead of epidemic and it’s presumably not as lethal. But, I think that it is something that we could be fighting for a while.

There is chances of additional disasters happening on top of it. We could get another disease popping out of some animal population while our immune systems are weak or something like that. So we should probably be rethinking the way we interact with caves full of bats and live pangolins.

Lucas Perry: All right. We just need to be prepared for the long haul here.

Robert de Neufville: Yeah, I think so.

Lucas Perry: I’m not sure that most people understand that.

Robert de Neufville: I don’t think they do. I mean, I guess I don’t have my finger on the pulse and I’m not interacting with people anymore, but I don’t think people want to understand it. It’s hard. I had plans. I did not intend to be staying in my apartment. Having your health is more important and the health of others, but it’s hard to face that we may be dealing with a very different new reality.

This thing, the opening up in Georgia, it’s just completely insane to me. Their cases have been slowing, but if it’s shrinking, it seems to be only a little bit. To me, when they talk about opening up, it sounds like they’re saying, well, we reduced the extent of this forest fire by 15%, so we can stop fighting it now. Well, it’s just going to keep growing. But, you have to actually stamp it out or get really close to it before you can stop fighting it. I think people want to stop fighting the disease sooner than we should because it sucks. I don’t want to be doing this.

Lucas Perry: Yeah, it’s a new sad fact and there is a lot of suffering going on right now.

Robert de Neufville: Yeah. I feel really lucky to be in a place where there aren’t a lot of cases, but I worry about family members in other places and I can’t imagine what it’s like in places where it’s bad.

I mean, in Hawaii, people in the hospitality industry and tourism industry have all lost their jobs all at once and they still have to pay our super expensive rent. Maybe that’ll be waived and they won’t be evicted. But, that doesn’t mean they can necessarily get medications and feed their family. And all of these are super challenging for a lot of people.

Nevermind that other people are in the position of, they’re lucky to have jobs, but they’re maybe risking getting an infection going to work, so they have to make this horrible choice. And maybe they have someone with comorbidities or who is elderly living at home. This is awful. So I understand why people really want to get past this part of it soon.

Was it Dr. Fauci that said, “The virus has its own timeline?”

One of the things I think that this may be teaching us, it’s certainly reminding me that humans are not in charge of nature, not the way we think we are. We really dominate the planet in a lot of ways, but it’s still bigger than us. It’s like the ocean or something. You know? You may think you’re a good swimmer, but if you get a big wave, you’re not in control anymore and this is a big wave.

Lucas Perry: Yeah. So back to the point of general superforecasting. Suppose you’re a really good superforecaster and you’re finding well-defined things to make predictions about, which is, as you said, sort of hard to do and you have carefully and honestly compared your predictions to reality and you feel like you’re doing really well.

How do you convince other people that you’re a great predictor when almost everyone else is making lots of vague predictions and cherry picking their successes or their interests groups that are biasing and obscuring things to try to have a seat at the table? Or for example, if you want to compare yourself to someone else who has been keeping a careful track as well, how do you do that technically?

Robert de Neufville: I wish I knew the answer to that question. I think it is probably a long process of building confidence and communicating reasonable forecasts and having people see that they were pretty accurate. People trust something like FiveThirthyEight, Nate Silvers’, or Nick Cohen, or someone like that because they have been communicating for a while and people can now see it. They have this track record and they also are explaining how it happens, how they get to those answers. And at least a lot of people started to trust what Nate Silver says. So I think something like that really is the longterm strategy.

But, I think it’s hard because a lot of times there is always someone who is saying every different thing at any given time. And if somebody says there is definitely a pandemic going to happen, and they do it in November 2019, then a lot of people may think, “Wow, that person’s a prophet and we should listen to them.”

To my mind, if you were saying that in November of 2019, that wasn’t a great prediction. I mean, you turned out to be right, but you didn’t have good reasons for it. At that point, it was still really uncertain unless you had access to way more information than as far as I know anyone had access to.

But, you know sometimes those magic tricks where somebody throws a dart at something and happens to hit the bullseye might be more convincing than an accurate probabilistic forecast. I think that in order to sell the accurate probabilistic forecasts, you really need to build a track record of communication and build confidence slowly.

Lucas Perry: All right, that makes sense.

So on prediction markets and prediction aggregators, they’re pretty well set up to treat questions like will X happen by Y date where X is some super well-defined thing. But lots of things we’d like to know are not really of this form. So what are other useful forms of question about the future that you come across in your work and what do you think are the prospects for training and aggregating skilled human predictors to tackle them?

Robert de Neufville: What are the other forms of questions? There is always a trade off with designing question between sort of the rigor of the question, how easy it is to say whether it turned out to be true or not and how relevant it is to things you might actually want to know. Now, that’s often difficult to balance.

I think that in general we need to be thinking more about questions, so I wouldn’t say here is the different type of question that we should be answering. But rather, let’s really try to spend a lot of time thinking about the questions. What questions could be useful to answer? I think just that exercise is important.

I think things like science fiction are important where they brainstorm a possible scenario and they often fill it out with a lot of detail. But, I often think in forecasting, coming up with very specific scenarios is kind of the enemy. If you come up with a lot of things that could plausibly happen and you build it into one scenario and you think this is the thing that’s going to happen, well the more specific you’ve made that scenario, the less likely it is to actually be the exact right one.

We need to do more thinking about spaces of possible things that could happen, ranges of things, different alternatives rather than just coming up with scenarios and anchoring on them as the thing that happens. So I guess I’d say more questions and realize that at least as far as we’re able to know, I don’t know if the universe is deterministic, but at least as far as we are able to know, a lot of different things are possible and we need to think about those possibilities and potentially plan for them.

Lucas Perry: All right. And so, let’s say you had 100 professors with deep subject matter expertise in say, 10 different subjects and you had 10 superforecasters, how would you make use of all of them and on what sorts of topics would you consult, what group or combination of groups?

Robert de Neufville: That’s a good question. I think we bash on subject matter experts because they’re bad at producing probabilistic forecasts. But the fact is that I completely depend on subject matter experts. When I try to forecast what’s going to happen on the pandemic, I am reading all the virologists and infectious disease experts because I don’t know anything about this. I mean, I know I get some stuff wrong. Although, I’m in a position where I can actually ask people, hey what is this, and get their explanations for it.

But, I would like to see them working together. To some extent, having some of the subject matter experts recognize that we may know some things about estimating probabilities that they don’t. But also, the more I can communicate with people that know specific facts about things, the better the forecasts I can produce are. I don’t know what the best system for that is. I’d like to see more communication. But, I also think you could get some kind of a thing where you put them in a room or on a team together to produce forecasts.

When I’m forecasting, typically, I come up with my own forecast and then I see what other people have said. But, I do that so as not to anchor on somebody else’s opinion and to avoid groupthink. You’re more likely to get groupthink if you have a leader and a team that everyone defers to and then they all anchor on whatever the leader’s opinion is. So, I try to form my own independent opinion.

But, I think some kind of a Delphi technique where people will come up with their own ideas and then share them and then revise their ideas could be useful and you could involve subject matter experts in that. I would love to be able to just sit and talk with epidemiologist about this stuff. I don’t know if they would love it as much to talk to me and I don’t know. But I think that, that would help us collectively produce better forecasts.

Lucas Perry: I am excited and hopeful for the top few percentage of superforecasters being integrated into more decision making about key issues. All right, so you have your own podcast.

Robert de Neufville: Yeah.

Lucas Perry: If people are interested in following you or looking into more of your work at the Global Catastrophic Riss Institute, for example, or following your podcast or following you on social media, where can they do that?

Robert de Neufville: Go to the Global Catastrophic Risk Institute’s website, it’s gcrinstitute.org, so you can see and read about our work. It’s super interesting and I believe super important. We’re doing a lot of work now on artificial intelligence risk. There has been a lot of interest in that. But, we also talk about nuclear war risk and there is going to be I think a new interest in pandemic risk. So these are things that we think about. I also do have a podcast. I co-host it with two other superforecasters, which sometimes becomes sort of like a forecasting politics variety hour. But we have a good time and we do some interviews with other superforecasters and we’ve also talked to people about existential risk and artificial intelligence. That’s called NonProphets. We have a blog, nonprophetspod.wordpress.org. But Nonprophets, it’s N-O-N-P-R-O-P-H-E-T-S like prophet like someone who sees the future, because we are not prophets. However, there is also another podcast, which I’ve never listened to and feel like I should, which also has the same name. There is an atheist podcast out of Texas and atheist comedians. I apologize for taking their name, but we’re not them, so if there is any confusion. One of the things about forecasting is it’s super interesting and it’s a lot of fun, at least for people like me to think about things in this way, and there are ways like Good Judgment Open you can do it too. So we talk about that. It’s fun. And I recommend everyone get into forecasting.

Lucas Perry: All right. Thanks so much for coming on and I hope that more people take up forecasting. And it’s a pretty interesting lifelong thing that you can participate in and see how well you do over time and keep resolving over actual real world stuff. I hope that more people take this up and that it gets further and more deeply integrated into communities of decision makers on important issues.

Robert de Neufville: Yeah. Well, thanks for having me on. It’s a super interesting conversation. I really appreciate talking about this stuff.

AI Alignment Podcast: An Overview of Technical AI Alignment in 2018 and 2019 with Buck Shlegeris and Rohin Shah

 Topics discussed in this episode include:

  • Rohin’s and Buck’s optimism and pessimism about different approaches to aligned AI
  • Traditional arguments for AI as an x-risk
  • Modeling agents as expected utility maximizers
  • Ambitious value learning and specification learning/narrow value learning
  • Agency and optimization
  • Robustness
  • Scaling to superhuman abilities
  • Universality
  • Impact regularization
  • Causal models, oracles, and decision theory
  • Discontinuous and continuous takeoff scenarios
  • Probability of AI-induced existential risk
  • Timelines for AGI
  • Information hazards

Timestamps: 

0:00 Intro

3:48 Traditional arguments for AI as an existential risk

5:40 What is AI alignment?

7:30 Back to a basic analysis of AI as an existential risk

18:25 Can we model agents in ways other than as expected utility maximizers?

19:34 Is it skillful to try and model human preferences as a utility function?

27:09 Suggestions for alternatives to modeling humans with utility functions

40:30 Agency and optimization

45:55 Embedded decision theory

48:30 More on value learning

49:58 What is robustness and why does it matter?

01:13:00 Scaling to superhuman abilities

01:26:13 Universality

01:33:40 Impact regularization

01:40:34 Causal models, oracles, and decision theory

01:43:05 Forecasting as well as discontinuous and continuous takeoff scenarios

01:53:18 What is the probability of AI-induced existential risk?

02:00:53 Likelihood of continuous and discontinuous take off scenarios

02:08:08 What would you both do if you had more power and resources?

02:12:38 AI timelines

02:14:00 Information hazards

02:19:19 Where to follow Buck and Rohin and learn more

 

Works referenced: 

AI Alignment 2018-19 Review

Takeoff Speeds by Paul Christiano

Discontinuous progress investigation by AI Impacts

An Overview of Technical AI Alignment with Rohin Shah (Part 1)

An Overview of Technical AI Alignment with Rohin Shah (Part 2)

Alignment Newsletter

Intelligence Explosion Microeconomics

AI Alignment: Why It’s Hard and Where to Start

AI Risk for Computer Scientists

 

We hope that you will continue to join in the conversations by following us or subscribing to our podcasts on Youtube, Spotify, SoundCloud, iTunes, Google Play, StitcheriHeartRadio, or your preferred podcast site/application. You can find all the AI Alignment Podcasts here.

You can listen to the podcast above or read the transcript below. 

Note: The following transcript has been edited for style and clarity.

 

Lucas Perry: Welcome to the AI Alignment Podcast. I’m Lucas Perry. Today we have a special episode with Buck Shlegeris and Rohin Shah that serves as a review of progress in technical AI alignment over 2018 and 2019. This episode serves as an awesome birds eye view of the varying focus areas of technical AI alignment research and also helps to develop a sense of the field. I found this conversation to be super valuable for helping me to better understand the state and current trajectory of technical AI alignment research. This podcast covers traditional arguments for AI as an x-risk, what AI alignment is, the modeling of agents as expected utility maximizers, iterated distillation and amplification, AI safety via debate, agency and optimization, value learning, robustness, scaling to superhuman abilities, and more. The structure of this podcast is based on Rohin’s AI Alignment Forum post titled AI Alignment 2018-19 Review. That post is an excellent resource to take a look at in addition to this podcast. Rohin also had a conversation with us about just a year ago titled An Overview of Technical AI Alignment with Rohin shah. This episode serves as a follow up to that overview and as an update to what’s been going on in the field. You can find a link for it on the page for this episode.  

Buck Shlegeris is a researcher at the Machine Intelligence Research Institute. He tries to work to make the future good for sentient beings and currently believes that working on existential risk from artificial intelligence is the best way of doing this. Buck worked as a software engineer at PayPal before joining MIRI, and was the first employee at Triplebyte. He previously studied at the Australian National University, majoring in CS and minoring in math and physics, and he has presented work on data structure synthesis at industry conferences.

Rohin Shah is a 6th year PhD student in Computer Science at the Center for Human-Compatible AI at UC Berkeley. He is involved in Effective Altruism and was the co-president of EA UC Berkeley for 2015-16 and ran EA UW during 2016-2017. Out of concern for animal welfare, Rohin is almost vegan because of the intense suffering on factory farms. He is interested in AI, machine learning, programming languages, complexity theory, algorithms, security, and quantum computing to name a few. Rohin’s research focuses on building safe and aligned AI systems that pursue the objectives their users intend them to pursue, rather than the objectives that were literally specified. He also publishes the Alignment Newsletter, which summarizes work relevant to AI alignment. The Alignment Newsletter is something I highly recommend that you follow in addition to this podcast.  

And with that, let’s get into our review of AI alignment with Rohin Shah and Buck Shlegeris.

To get things started here, the plan is to go through Rohin’s post on the Alignment Forum about AI Alignment 2018 and 2019 In Review. We’ll be using this as a way of structuring this conversation and as a way of moving methodically through things that have changed or updated in 2018 and 2019, and to use those as a place for conversation. So then, Rohin, you can start us off by going through this document. Let’s start at the beginning, and we’ll move through sequentially and jump in where necessary or where there is interest.

Rohin Shah: Sure, that sounds good. I think I started this out by talking about this basic analysis of AI risk that’s been happening for the last couple of years. In particular, you have these traditional arguments, so maybe I’ll just talk about the traditional argument first, which basically says that the AI systems that we’re going to build are going to be powerful optimizers. When you optimize something, you tend to get these sort of edge case outcomes, these extreme outcomes that are a little hard to predict ahead of time.

You can’t just rely on tests with less powerful systems in order to predict what will happen, and so you can’t rely on your normal common sense reasoning in order to deal with this. In particular, powerful AI systems are probably going to look like expected utility maximizers due to various coherence arguments, like the Von Neumann–Morgenstern rationality theorem, and these expected utility maximizers have convergent instrumental sub-goals, like not wanting to be switched off because then they can’t achieve their goal, and wanting to accumulate a lot of power and resources.

The standard argument goes, because AI systems are going to be built this way, they will have these convergent instrumental sub-goals. This makes them dangerous because they will be pursuing goals that we don’t want.

Lucas Perry: Before we continue too much deeper into this, I’d want to actually start off with a really simple question for both of you. What is AI alignment?

Rohin Shah: Different people mean different things by it. When I use the word alignment, I’m usually talking about what has been more specifically called intent alignment, which is basically aiming for the property that the AI system is trying to do what you want. It’s trying to help you. Possibly it doesn’t know exactly how to best help you, and it might make some mistakes in the process of trying to help you, but really what it’s trying to do is to help you.

Buck Shlegeris: The way I would say what I mean by AI alignment, I guess I would step back a little bit, and think about why it is that I care about this question at all. I think that the fundamental fact which has me interested in anything about powerful AI systems of the future is that I think they’ll be a big deal in some way or another. And when I ask myself the question “what are the kinds of things that could be problems about how these really powerful AI systems work or affect the world”, one of the things which feels like a problem is that, we might not  know how to apply these systems reliably to the kinds of problems which we care about, and so by default humanity will end up applying them in ways that lead to really bad outcomes. And so I guess, from that perspective, when I think about AI alignment, I think about trying to make ways of building AI systems such that we can apply them to tasks that are valuable, such that that they’ll reliably pursue those tasks instead of doing something else which is really dangerous and bad.

I’m fine with intent alignment as the focus. I kind of agree with, for instance, Paul Christiano, that it’s not my problem if my AI system incompetently kills everyone, that’s the capability’s people’s problem. I just want to make the system so it’s trying to cause good outcomes.

Lucas Perry: Both of these understandings of what it means to build beneficial AI or aligned AI systems can take us back to what Rohin was just talking about, where there’s this basic analysis of AI risk, about AI as powerful optimizers and the associated risks there. With that framing and those definitions, Rohin, can you take us back into this basic analysis of AI risk?

Rohin Shah: Sure. The traditional argument looks like AI systems are going to be goal-directed. If you expect that your AI system is going to be goal-directed, and that goal is not the one that humans care about, then it’s going to be dangerous because it’s going to try to gain power and resources with which to achieve its goal.

If the humans tried to turn it off, it’s going to say, “No, don’t do that,” and it’s going to try to take actions that avoid that. So it pits the AI and the humans in an adversarial game with each other, and you ideally don’t want to be fighting against a superintelligent AI system. That seems bad.

Buck Shlegeris: I feel like Rohin is to some extent setting this up in a way that he’s then going to argue is wrong, which I think is kind of unfair. In particular, Rohin, I think you’re making these points about VNM theorems and stuff to set up the fact that it seems like these arguments don’t actually work. I feel that this makes it kind of unfairly sound like the earlier AI alignment arguments are wrong. I think this is an incredibly important question, of whether early arguments about the importance of AI safety were quite flawed. My impression is that overall the early arguments about AI safety were pretty good. And I think it’s a very interesting question whether this is in fact true. And I’d be interested in arguing about it, but I think it’s the kind of thing that ought to be argued about explicitly.

Rohin Shah: Yeah, sure.

Buck Shlegeris: And I get that you were kind of saying it narratively, so this is only a minor complaint. It’s a thing I wanted to note.

Rohin Shah: I think my position on that question of “how good were the early AI risk arguments,” probably people’s internal beliefs were good as to why AI was supposed to be risky, and the things they wrote down were not very good. Some things were good and some things weren’t. I think Intelligence Explosion Microeconomics was good. I think AI Alignment: Why It’s Hard and Where to Start, was misleading.

Buck Shlegeris: I think I agree with your sense that people probably had a lot of reasonable beliefs but that the written arguments seem flawed. I think another thing that’s true is that random people like me who were on LessWrong in 2012 or something, ended up having a lot of really stupid beliefs about AI alignment, which I think isn’t really the fault of the people who were thinking about it the best, but is maybe sociologically interesting.

Rohin Shah: Yes, that seems plausible to me. Don’t have a strong opinion on it.

Lucas Perry: To provide a little bit of framing here and better analysis of basic AI x-risk arguments, can you list what the starting arguments for AI risk were?

Rohin Shah: I think I am reasonably well portraying what the written arguments were. Underlying arguments that people probably had would be something more like, “Well, it sure seems like if you want to do useful things in the world, you need to have AI systems that are pursuing goals.” If you have something that’s more like tool AI, like Google Maps, that system is going to be good at the one thing it was designed to do, but it’s not going to be able to learn and then apply its knowledge to new tasks autonomously. It sure seems like if you want to do really powerful things in the world, like run companies or make policies, you probably do need AI systems that are constantly learning about their world and applying their knowledge in order to come up with new ways to do things.

In the history of human thought, we just don’t seem to know of a way to cause that to happen except by putting goals in systems, and so probably AI systems are going to be goal-directed. And one way you can formalize goal-directedness is by thinking about expected utility maximizers, and people did a bunch of formal analysis of that. Mostly going to ignore it because I think you can just say all the same thing with the idea of pursuing goals and it’s all fine.

Buck Shlegeris: I think one important clarification to that, is you were saying the reason that tool AIs aren’t just the whole story of what happens with AI is that you can’t apply it to all problems. I think another important element is that people back then, and I now, believe that if you want to build a really good tool, you’re probably going to end up wanting to structure that as an agent internally. And even if you aren’t trying to structure it as an agent, if you’re just searching over lots of different programs implicitly, perhaps by training a really large recurrent policy, you’re going to end up finding something agent shaped.

Rohin Shah: I don’t disagree with any of that. I think we were using the words tool AI differently.

Buck Shlegeris: Okay.

Rohin Shah: In my mind, if we’re talking about tool AI, we’re imagining a pretty restricted action space where no matter what actions in this action space are taken, with high probability, nothing bad is going to happen. And you’ll search within that action space, but you don’t go to arbitrary action in the real world or something like that. This is what makes tool AI hard to apply to all problems.

Buck Shlegeris: I would have thought that’s a pretty non-standard use of the term tool AI.

Rohin Shah: Possibly.

Buck Shlegeris: In particular, I would have thought that restricting the action space enough that you’re safe, regardless of how much it wants to hurt you, seems kind of non-standard.

Rohin Shah: Yes. I have never really liked the concept of tool AI very much, so I kind of just want to move on.

Lucas Perry: Hey, It’s post-podcast Lucas here. I just want to highlight here a little bit of clarification that Rohin was interested in adding, which is that he thinks that “tool AI evokes a sense of many different properties that he doesn’t know which properties most people are  usually thinking about and as a result he prefers not to use the phrase tool AI. And instead would like to use more precise terminology. He doesn’t necessarily feel though that the concepts underlying tool AI are useless.” So let’s tie things a bit back to these basic arguments for x-risk that many people are familiar with, that have to do with convergent instrumental sub-goals and the difficulty of specifying and aligning systems with our goals and what we actually care about in our preference hierarchies.

One of the things here that Buck was seeming to bring up, he was saying that you may have been narratively setting up the Von Neumann–Morgenstern theorem, which sets up AIs as expected utility maximizers, and that you are going to argue that that argument, which is sort of the formalization of these earlier AI risk arguments, that that is less convincing to you now than it was before, but Buck still thinks that these arguments are strong. Could you unpack this a little bit more or am I getting this right?

Rohin Shah: To be clear, I also agree with Buck, that the spirit of the original arguments does seem correct, though, there are people who disagree with both of us about that. Basically, the VNM theorem roughly says, if you have preferences over a set of outcomes, and you satisfy some pretty intuitive axioms about how you make decisions, then you can represent your preferences using a utility function such that your decisions will always be, choose the action that maximizes the expected utility. This is, at least in writing, given as a reason to expect that AI systems would be maximizing expected utility. The thing is, when you talk about AI systems that are acting in the real world, they’re just selecting a universe history, if you will. Any observed behavior is compatible with the maximization of some utility function. Utility functions are a really, really broad class of things when you apply it to choosing from universe histories.

Buck Shlegeris: An intuitive example of this: suppose that you see that every day I walk home from work in a really inefficient way. It’s impossible to know whether I’m doing that because I happened to really like that path. For any sequence of actions that I take, there’s some utility functions such that that was the optimal sequence of actions. And so we don’t actually learn anything about how my policy is constrained based on the fact that I’m an expected utility maximizer.

Lucas Perry: Right. If I only had access to your behavior and not your insides.

Rohin Shah: Yeah, exactly. If you have a robot twitching forever, that’s all it does, there is a utility function over a universe history that says that is the optimal thing to do. Every time the robot twitches to the right, it’s like, yeah, the thing that was optimal to do at that moment in time was twitching to the right. If at some point somebody takes a hammer and smashes the robot and it breaks, then the utility function that corresponds to that being optimal is like, yeah, that was the exact right moment to break down.

If you have these pathologically complex utility functions as possibilities, every behavior is compatible with maximizing expected utility, you might want to say something like, probably we’ll have the simple utility maximizers, but that’s a pretty strong assumption, and you’d need to justify it somehow. And the VNM theorem wouldn’t let you do that.

Lucas Perry: So is the problem here that you’re unable to fully extract human preference hierarchies from human behavior?

Rohin Shah: Well, you’re unable to extract agent preferences from agent behavior. You can see any agent behavior and you can rationalize it as expected utility maximization, but it’s not very useful. Doesn’t give you predictive power.

Buck Shlegeris: I just want to have my go at saying this argument in three sentences. Once upon a time, people said that because all rational systems act like they’re maximizing an expected utility function, we should expect them to have various behaviors like trying to maximize the amount of power they have. But every set of actions that you could take is consistent with being an expected utility maximizer, therefore you can’t use the fact that something is an expected utility maximizer in order to argue that it will have a particular set of behaviors, without making a bunch of additional arguments. And I basically think that I was wrong to be persuaded by the naive argument that Rohin was describing, which just goes directly from rational things are expected utility maximizers, to therefore rational things are power maximizing.

Rohin Shah: To be clear, this was the thing I also believed. The main reason I wrote the post that argued against it was because I spent half a year under the delusion that this was a valid argument.

Lucas Perry: Just for my understanding here, the view is that because any behavior, any agent from the outside can be understood as being an expected utility maximizer, that there are behaviors that clearly do not do instrumental sub-goal things, like maximize power and resources, yet those things can still be viewed as expected utility maximizers from the outside. So additional arguments are required for why expected utility maximizers do instrumental sub-goal things, which are AI risky.

Rohin Shah: Yeah, that’s exactly right.

Lucas Perry: Okay. What else is on offer other than expected utility maximizers? You guys talked about comprehensive AI services might be one. Are there other formal agentive classes of ‘thing that is not an expected utility maximizer but still has goals?’

Rohin Shah: A formalism for that? I think some people like John Wentworth is for example, thinking about markets as a model of agency. Some people like to think of multi-agent groups together leading to an emergent agency and want to model human minds this way. How formal are these? Not that formal yet.

Buck Shlegeris: I don’t think there’s anything which is competitively popular with expected utility maximization as the framework for thinking about this stuff.

Rohin Shah: Oh yes, certainly not. Expected utility maximization is used everywhere. Nothing else comes anywhere close.

Lucas Perry: So there’s been this complete focus on utility functions and representing the human utility function, whatever that means. Do you guys think that this is going to continue to be the primary way of thinking about and modeling human preference hierarchies? How much does it actually relate to human preference hierarchies? I’m wondering if it might just be substantially different in some way.

Buck Shlegeris: Me and Rohin are going to disagree about this. I think that trying to model human preferences as a utility function is really dumb and bad and will not help you do things that are useful. I don’t know; If I want to make an AI that’s incredibly good at recommending me movies that I’m going to like, some kind of value learning thing where it tries to learn my utility function over movies is plausibly a good idea. Even things where I’m trying to use an AI system as a receptionist, I can imagine value learning being a good idea.

But I feel extremely pessimistic about more ambitious value learning kinds of things, where I try to, for example, have an AI system which learns human preferences and then acts in large scale ways in the world. I basically feel pretty pessimistic about every alignment strategy which goes via that kind of a route. I feel much better about either trying to not use AI systems for problems where you have to think about large scale human preferences, or having an AI system which does something more like modeling what humans would say in response to various questions and then using that directly instead of trying to get a value function out of it.

Rohin Shah: Yeah. Funnily enough, I was going to start off by saying I think Buck and I are going to agree on this.

Buck Shlegeris: Oh.

Rohin Shah: And I think I mostly agree with the things that you said. The thing I was going to say was I feel pretty pessimistic about trying to model the normative underlying human values, where you have to get things like population ethics right, and what to do with the possibility of infinite value. How do you deal with fanaticism? What’s up with moral uncertainty? I feel pretty pessimistic about any sort of scheme that involves figuring that out before developing human-level AI systems.

There’s a related concept which is also called value learning, which I would prefer to be called something else, but I feel like the name’s locked in now. In my sequence, I called it narrow value learning, but even that feels bad. Maybe at least for this podcast we could call it specification learning, which is sort of more like the tasks Buck mentioned, like if you want to learn preferences over movies, representing that using a utility function seems fine.

Lucas Perry: Like superficial preferences?

Rohin Shah: Sure. I usually think of it as you have in mind a task that you want your AI system to do, and now you have to get your AI system to reliably do it. It’s unclear whether this should even be called a value learning at this point. Maybe it’s just the entire alignment problem. But techniques like inverse reinforcement learning, preference learning, learning from corrections, inverse reward design where you learn from a proxy reward, all of these are more trying to do the thing where you have a set of behaviors in mind, and you want to communicate that to the agent.

Buck Shlegeris: The way that I’ve been thinking about how optimistic I should be about value learning or specification learning recently has been that I suspect that at the point where AI is human level, by default we’ll have value learning which is about at human level. We’re about as good at giving AI systems information about our preferences that it can do stuff with as we are giving other humans information about our preferences that we can do stuff with. And when I imagine hiring someone to recommend music to me, I feel like there are probably music nerds who could do a pretty good job of looking at my Spotify history, and recommending bands that I’d like if they spent a week on it. I feel a lot more pessimistic about being able to talk to a philosopher for a week, and then them answer hard questions about my preferences, especially if they didn’t have the advantage of already being humans themselves.

Rohin Shah: Yep. That seems right.

Buck Shlegeris: So maybe that’s how I would separate out the specification learning stuff that I feel optimistic about from the more ambitious value learning stuff that I feel pretty pessimistic about.

Rohin Shah: I do want to note that I collated a bunch of stuff arguing against ambitious value learning. If I had to make a case for optimism about even that approach, it would look more like, “Under the value learning approach, it seems possible with uncertainty over rewards, values, preferences, whatever you want to call them to get an AI system such that you actually are able to change it, because it would reason that if you’re trying to change it, well then that means something about it is currently not good for helping you and so it would be better to let itself be changed. I’m not very convinced by this argument.”

Buck Shlegeris: I feel like if you try to write down four different utility functions that the agent is uncertain between, I think it’s just actually really hard for me to imagine concrete scenarios where the AI is corrigible as a result of its uncertainty over utility functions. Imagine the AI system thinks that you’re going to switch it off and replace it with an AI system which has a different method of inferring values from your actions and your words. It’s not going to want to let you do that, because its utility function is to have the world be the way that is expressed by your utility function as estimated the way that it approximates utility functions. And so being replaced by a thing which estimates utility functions or infers utility functions some other way means that it’s very unlikely to get what it actually wants, and other arguments like this. I’m not sure if these are super old arguments that you’re five levels of counter-arguments to.

Rohin Shah: I definitely know this argument. I think the problem of fully updated deference is what I would normally point to as representing this general class of claims and I think it’s a good counter argument. When I actually think about this, I sort of start getting confused about what it means for an AI system to terminally value the final output of what its value learning system would do. It feels like some additional notion of how the AI chooses actions has been posited, that hasn’t actually been captured in the model and so I feel fairly uncertain about all of these arguments and kind of want to defer to the future. 

Buck Shlegeris: I think the thing that I’m describing is just what happens if you read the algorithm literally. Like, if you read the value learning algorithm literally, it has this notion of the AI system wants to maximize the human’s actual utility function.

Rohin Shah: For an optimal agent playing a CIRL (cooperative inverse reinforcement learning) game, I agree with your argument. If you take optimality as defined in the cooperative inverse reinforcement learning paper and it’s playing over a long period of time, then yes, it’s definitely going to prefer to keep itself in charge rather than a different AI system that would infer values in a different way.

Lucas Perry: It seems like so far utility functions are the best way of trying to get an understanding of what human beings care about and value and have preferences over, you guys are bringing up all of the difficult intricacies with trying to understand and model human preferences as utility functions. One of the things that you also bring up here, Rohin, in your review, is the risk of lock-in, which may require us to solve hard philosophical problems before the development of AGI. That has something to do with ambitious value learning, which would be like learning the one true human utility function which probably just doesn’t exist.

Buck Shlegeris: I think I want to object to a little bit of your framing there. My stance on utility functions of humans isn’t that there are a bunch of complicated subtleties on top, it’s that modeling humans with utility functions is just a really sad state to be in. If your alignment strategy involves positing that humans behave as expected utility maximizers, I am very pessimistic about it working in the short term, and I just think that we should be trying to completely avoid anything which does that. It’s not like there’s a bunch of complicated sub-problems that we need to work out about how to describe us as expected utility maximizers, my best guess is that we would just not end up doing that because it’s not a good idea.

Lucas Perry: For the ambitious value learning?

Buck Shlegeris: Yeah, that’s right.

Lucas Perry: Okay, do you have something that’s on offer?

Buck Shlegeris: The two options instead of that, which seem attractive to me? As I said earlier, one is you just convince everyone to not use AI systems for things where you need to have an understanding of large scale human preferences. The other one is the kind of thing that Paul Christiano’s iterated distillation and amplification, or a variety of his other ideas, the kind of thing that he’s trying to get there is, I think, if you make a really powerful AI system, it’s actually going to have an excellent model of human values in whatever representation is best for actually making predictions about  humans because a really excellent AGI, like a really excellent paperclip maximizer, it’s really important for it to really get how humans work so that it can manipulate them into letting it build lots of paperclip factories or whatever.

So I think that if you think that we have AGI, then by assumption I think we have a system which is able to reason about human values if it wants. And so if we can apply these really powerful AI systems to tasks such that the things that they do display their good understanding of human values, then we’re fine and it’s just okay that there was no way that we could represent a utility function directly. So for instance, the idea in IDA is that if we could have this system which is just trying to answer questions the same way that humans would, but enormously more cheaply because it can run faster than humans and a few other tricks, then we don’t have to worry about writing down a utility functions of humans directly because we can just make the system do things that are kind of similar to the things humans would have done, and so it implicitly has this human utility function built into it. That’s option two. Option one is don’t use anything that requires a complex human utility function, option two is have your systems learn human values implicitly, by giving them a task such that this is beneficial for them and such that their good understanding of human values comes out in their actions.

Rohin Shah: One way I might condense that point, is that you’re asking for a nice formalism for human preferences and I just point to all the humans out there in the world who don’t know anything about utility functions, which is 99% of them and nonetheless still seem pretty good at inferring human preferences.

Lucas Perry: On this part about AGI, if it is AGI it should be able to reason about human preferences, then why would it not be able to construct something that was more explicit and thus was able to do more ambitious value learning?

Buck Shlegeris: So it can totally do that, itself. But we can’t force that structure from the outside with our own algorithms.

Rohin Shah: Image classification is a good analogy. Like, in the past we were using hand engineered features, namely SIFT and HOG and then training classifiers over these hand engineered features in order to do image classification. And then we came to the era of deep learning and we just said, yeah, throw away all those features and just do everything end to end with a convolutional neural net and it worked way better. The point was that, in fact there are good representations for most tasks and humans trying to write them down ahead of time just doesn’t work very well at that. It tends to work better if you let the AI system discover its own representations that best capture the thing you wanted to capture.

Lucas Perry: Can you unpack this point a little bit more? I’m not sure that I’m completely understanding it. Buck is rejecting this modeling human beings explicitly as expected utility maximizers and trying to explicitly come up with utility functions in our AI systems. The first was to convince people not to use these kinds of things. And the second is to make it so that the behavior and output of the AI systems has some implicit understanding of human behavior. Can you unpack this a bit more for me or give me another example?

Rohin Shah: So here’s another example. Let’s say I was teaching my kid that I don’t have, how to catch a ball. It seems that the formalism that’s available to me for learning how to catch a ball is, well, you can go all the way down to look at our best models of physics, we could use Newtonian mechanics let’s say, like here are these equations, estimate the velocity and the distance of the ball and the angle at which it’s thrown plug that into these equations and then predict that the ball’s going to come here and then just put your hand there and then magically catch it. We won’t even talk about the catching part. That seems like a pretty shitty way to teach a kid how to catch a ball.

Probably it’s just a lot better to just play catch with the kid for a while and let the kid’s brain figure out this is how to predict where the ball is going to go such that I can predict where it’s going to be and then catch it.

I’m basically 100% confident that the thing that the brain is doing is not Newtonian mechanics. It’s doing something else that’s just way more efficient at predicting where the ball is going to be so that I can catch it and if I forced the brain to use Newtonian mechanics, I bet it would not do very well at this task.

Buck Shlegeris: I feel like that still isn’t quite saying the key thing here. I don’t know how to say this off the top of my head either, but I think there’s this key point about: just because your neural net can learn a particular feature of the world doesn’t mean that you can back out some other property of the world by forcing the neural net to have a particular shape. Does that make any sense, Rohin?

Rohin Shah: Yeah, vaguely. I mean, well, no, maybe not.

Buck Shlegeris: The problem isn’t just the capabilities problem. There’s this way you can try and infer a human utility function by asking, according to this model, what’s the maximum likelihood utility function given all these things the human did. If you have a good enough model, you will in fact end up making very good predictions about the human, it’s just that the decomposition into their planning function and their utility function is not going to result in a utility function which is anything like a thing that I would want maximized if this process was done on me. There is going to be some decomposition like this, which is totally fine, but the utility function part just isn’t going to correspond to the thing that I want.

Rohin Shah: Yeah, that is also a problem, but I agree that is not the thing I was describing.

Lucas Perry: Is the point there that there’s a lack of alignment between the utility function and the planning function. Given that the planning function imperfectly optimizes the utility function.

Rohin Shah: It’s more like there are just infinitely many possible pairs of planning functions and utility functions that exactly predict human behavior. Even if it were true that humans were expected utility maximizers, which Buck is arguing we’re not, and I agree with him. There is a planning function that’s like humans are perfectly anti-rational and if you’re like what utility function works with that planner to predict human behavior. Well, the literal negative of the true utility function when combined with the anti-rational planner produces the same behavior as the true utility function with the perfect planner, there’s no information that lets you distinguish between these two possibilities.

You have to build it in as an assumption. I think Buck’s point is that building things in as assumptions is probably not going to work.

Buck Shlegeris: Yeah.

Rohin Shah: A point I agree with. In philosophy this is called the is-ought problem, right? What you can train your AI system on is a bunch of “is” facts and then you have to add in some assumptions in order to jump to “ought” facts, which is what the utility function is trying to do. The utility function is trying to tell you how you ought to behave in new situations and the point of the is-ought distinction is that you need some bridging assumptions in order to get from is to ought.

Buck Shlegeris: And I guess an important part here is your system will do an amazing job of answering “is” questions about what humans would say about “ought” questions. And so I guess maybe you could phrase the second part as: to get your system to do things that match human preferences, use the fact that it knows how to make accurate “is” statements about humans’ ought statements?

Lucas Perry: It seems like we’re strictly talking about inferring the human utility function or preferences via looking at behavior. What if you also had more access to the actual structure of the human’s brain?

Rohin Shah: This is like the approach that Stuart Armstrong likes to talk about. The same things still apply. You still have the is-ought problem where the facts about the brain are “is” facts and how you translate that into “ought” facts is going to involve some assumptions. Maybe you can break down such assumptions that everyone would agree with. Maybe it’s like if this particular neuron in a human brain spikes, that’s a good thing and we want more of it and if this other one spikes, that’s a bad thing. We don’t want it. Maybe that assumption is fine.

Lucas Perry: I guess I’m just pointing out, if you could find the places in the human brain that generate the statements about Ought questions.

Rohin Shah: As Buck said, that lets you predict what humans would say about ought statements, which your assumption could then be, whatever humans say about ought statements, that’s what you ought to do. And that’s still an assumption. Maybe it’s a very reasonable assumption that we’re happy to put it into our AI system.

Lucas Perry: If we’re not willing to accept some humans’ “is” statements about “ought” questions then we have to do some meta-ethical moral policing in our assumptions around getting “is” statements from “ought” questions.

Rohin Shah: Yes, that seems right to me. I don’t know how you would do such a thing, but you would have to do something along those lines.

Buck Shlegeris: I would additionally say that I feel pretty great about trying to do things which use the fact that we can trust our AI to have good “is” answers to “ought” questions, but there’s a bunch of problems with this. I think it’s a good starting point but trying to use that to do arbitrarily complicated things in the world has a lot of problems. For instance, suppose I’m trying to decide whether we should design a city this way or that way. It’s hard to know how to go from the ability to know how humans would answer questions about preferences to knowing what you should do to design the city. And this is for a bunch of reasons, one of them is that the human might not be able to figure out from your city building plans what the city’s going to actually be like. And another is that the human might give inconsistent answers about what design is good, depending on how you phrase the question, such that if you try to figure out a good city plan by optimizing for the thing that the human is going to be most enthusiastic about, then you might end up with a bad city plan. Paul Christiano has written in a lot of detail about a lot of this.

Lucas Perry: That also reminds me of what Stuart Armstrong wrote about the framing on the questions changing output on the preference.

Rohin Shah: Yep.

Buck Shlegeris: Sorry, to be clear other people than Paul Christiano have also written a lot about this stuff, (including Rohin). My favorite writing about this stuff is by Paul.

Lucas Perry: Yeah, those do seem problematic but it would also seem that there would be further “is” statements that if you queried people’s meta-preferences about those things, you would get more “is” statements about that, but then that just pushes the “ought” assumptions that you need to make further back. Getting into very philosophically weedy territory. Do you think that this kind of thing could be pushed to the long reflection as is talked about by William MacAskill and Toby Ord or how much of this do you actually think needs to be solved in order to have safe and aligned AGI?

Buck Shlegeris: I think there are kind of two different ways that you could hope to have good outcomes from AGI. One is: set up a world such that you never needed to make an AGI which can make large scale decisions about the world. And two is: solve the full alignment problem.

I’m currently pretty pessimistic about the second of those being technically feasible. And I’m kind of pretty pessimistic about the first of those being a plan that will work. But in the world where you can have everyone only apply powerful and dangerous AI systems in ways that don’t require an understanding of human values, then you can push all of these problems onto the long reflection. In worlds where you can do arbitrarily complicated things in ways that humans would approve of, you don’t really need to long reflect this stuff because of the fact that these powerful AI systems already have the capacity of doing portions of the long reflection work inside themselves as needed. (Quotes about the long reflection

Rohin Shah: Yeah, so I think my take, it’s not exactly disagreeing with Buck. It’s more like from a different frame as Buck’s. If you just got AI systems that did the things that humans did now, this does not seem to me to obviously require solving hard problems in philosophy. That’s the lower bound on what you can do before having to do long reflection type stuff. Eventually you do want to do a longer reflection. I feel relatively optimistic about having a technical solution to alignment that allows us to do the long reflection after building AI systems. So the long reflection would include both humans and AI systems thinking hard, reflecting on difficult problems and so on.

Buck Shlegeris: To be clear, I’m super enthusiastic about there being a long reflection or something along those lines.

Lucas Perry: I always find it useful reflecting on just how human beings do many of these things because I think that when thinking about things in the strict AI alignment sense, it can seem almost impossible, but human beings are able to do so many of these things without solving all of these difficult problems. It seems like in the very least, we’ll be able to get AI systems that very, very approximately do what is good or what is approved of by human beings because we can already do that.

Buck Shlegeris: That argument doesn’t really make sense to me. It also didn’t make sense when Rohin referred to it a minute ago.

Rohin Shah: It’s not an argument for we technically know how to do this. It is more an argument for this as at least within the space of possibilities.

Lucas Perry: Yeah, I guess that’s how I was also thinking of it. It is within the space of possibilities. So utility functions are good because they can be optimized for, and there seem to be risks with optimization. Is there anything here that you guys would like to say about better understanding agency? I know this is one of the things that is important within the MIRI agenda.

Buck Shlegeris: I am a bad MIRI employee. I don’t really get that part of the MIRI agenda, and so I’m not going to defend it. I have certainly learned some interesting things from talking to Scott Garrabrant and other MIRI people who have lots of interesting thoughts about this stuff. I don’t quite see the path from there to good alignment strategies. But I also haven’t spent a super long time thinking about it because I, in general, don’t try to think about all of the different AI alignment things that I could possibly think about.

Rohin Shah: Yeah. I also am not a good person to ask about this. Most of my knowledge comes from reading things and MIRI has stopped writing things very much recently, so I don’t know what their ideas are. I, like Buck, don’t really see a good alignment strategy that starts with, first we understand optimization and so that’s the main reason why I haven’t looked into it very much.

Buck Shlegeris: I think I don’t actually agree with the thing you said there, Rohin. I feel like understanding optimization could plausibly be really nice. Basically the story there is, it’s a real bummer if we have to make really powerful AI systems via searching over large recurrent policies for things that implement optimizers. If it turned out that we could figure out some way of coding up optimizer stuffs directly, then this could maybe mean you didn’t need to make mesa-optimizers. And maybe this means that your inner alignment problems go away, which could be really nice. The thing that I was saying I haven’t thought that much about is, the relevance of thinking about, for instance, the various weirdnesses that happen when you consider embedded agency or decision theory, and things like that.

Rohin Shah: Oh, got it. Yeah. I think I agree that understanding optimization would be great if we succeeded at it and I’m mostly pessimistic about us succeeding at it, but also there are people who are optimistic about it and I don’t know why they’re optimistic about it.

Lucas Perry: Hey it’s post-podcast Lucas here again. So, I just want to add a little more detail here again on behalf of Rohin. Here he feels pessimistic about us understanding optimization well enough and in a short enough time period that we are able to create powerful optimizers that we understand that rival the performance of the AI systems we’re already building and will build in the near future. Back to the episode. 

Buck Shlegeris: The arguments that MIRI has made about this,… they think that there are a bunch of questions about what optimization is, that are plausibly just not that hard compared to other problems which small groups of people have occasionally solved, like coming up with foundations of mathematics, kind of a big conceptual deal but also a relatively small group of people. And before we had formalizations of math, I think it might’ve seemed as impossible to progress on as formalizing optimization or coming up with a better picture of that. So maybe that’s my argument for some optimism.

Rohin Shah: Yeah, I think pointing to some examples of great success does not imply… Like there are probably many similar things that didn’t work out and we don’t know about them cause nobody bothered to tell us about them because they failed. Seems plausible maybe.

Lucas Perry: So, exploring more deeply this point of agency can either, or both of you, give us a little bit of a picture about the relevance or non relevance of decision theory here to AI alignment and I think, Buck, you mentioned the trickiness of embedded decision theory.

Rohin Shah: If you go back to our traditional argument for AI risk, it’s basically powerful AI systems will be very strong optimizers. They will possibly be misaligned with us and this is bad. And in particular one specific way that you might imagine this going wrong is this idea of mesa optimization where we don’t know how to build optimizers right now. And so what we end up doing is basically search across a huge number of programs looking for ones that do well at optimization and use that as our AGI system. And in this world, if you buy that as a model of what’s happening, then you’ll basically have almost no control over what exactly that system is optimizing for. And that seems like a recipe for misalignment. It sure would be better if we could build the optimizer directly and know what it is optimizing for. And in order to do that, we need to know how to do optimization well.

Lucas Perry: What are the kinds of places that we use mesa optimizers today?

Rohin Shah: It’s not used very much yet. The field of meta learning is the closest example. In the field of meta learning you have a distribution over tasks and you use gradient descent or some other AI technique in order to find an AI system that itself, once given a new task, learns how to perform that task well.

Existing meta learning systems are more like learning how to do all the tasks well and then when they’ll see a new task they just figure out ah, it’s this task and then they roll out the policy that they already learned. But the eventual goal for meta learning is to get something that, online, learns how to do the task without having previously figured out how to do that task.

Lucas Perry: Okay, so Rohin did what you say cover embedded decision theory?

Rohin Shah: No, not really. I think embedded decision theory is just, we want to understand optimization. Our current notion of optimization, one way you could formalize it is to say my AI agent is going to have Bayesian belief over all the possible ways that the environment could be. It’s going to update that belief over time as it gets observations and then it’s going to act optimally with respect to that belief, by maximizing its expected utility. And embedded decision theory basically calls into question the idea that there’s a separation between the agent and the environment. In particular I, as a human, couldn’t possibly have a Bayesian belief about the entire earth because the entire Earth contains me. I can’t have a Bayesian belief over myself so this means that our existing formalization of agency is flawed. It can’t capture these things that affect real agents. And embedded decision theory, embedded agency, more broadly, is trying to deal with this fact and have a new formalization that works even in these situations.

Buck Shlegeris: I want to give my understanding of the pitch for it. One part is that if you don’t understand embedded agency, then if you try to make an AI system in a hard coded way, like making a hard coded optimizer, traditional phrasings of what an optimizer is, are just literally wrong in that, for example, they’re assuming that you have these massive beliefs over world states that you can’t really have. And plausibly, it is really bad to try to make systems by hardcoding assumptions that are just clearly false. And so if we want to hardcode agents with particular properties, it would be good if we knew a way of coding the agent that isn’t implicitly making clearly false assumptions.

And the second pitch for it is something like when you want to understand a topic, sometimes it’s worth looking at something about the topic which you’re definitely wrong about, and trying to think about that part until you are less confused about it. When I’m studying physics or something, a thing that I love doing is looking for the easiest question whose answer I don’t know, and then trying to just dive in until I have satisfactorily answered that question, hoping that the practice that I get about thinking about physics from answering a question correctly will generalize to much harder questions. I think that’s part of the pitch here. Here is a problem that we would need to answer, if we wanted to understand how superintelligent AI systems work, so we should try answering it because it seems easier than some of the other problems.

Lucas Perry: Okay. I think I feel satisfied. The next thing here Rohin in your AI alignment 2018-19 review is value learning. I feel like we’ve talked a bunch about this already. Is there anything here that you want to say or do you want to skip this?

Rohin Shah: One thing we didn’t cover is, if you have uncertainty over what you’re supposed to optimize, this turns into an interactive sort of game between the human and the AI agent, which seems pretty good. A priori you should expect that there’s going to need to be a lot of interaction between the human and the AI system in order for the AI system to actually be able to do the things that the human wants it to do. And so having formalisms and ideas of where this interaction naturally falls out seems like a good thing.

Buck Shlegeris: I’ve said a lot of things about how I am very pessimistic about value learning as a strategy. Nevertheless it seems like it might be really good for there to be people who are researching this, and trying to get as good as we can get at improving sample efficiency so that can have your AI systems understand your preferences over music with as little human interaction as possible, just in case it turns out to be possible to solve the hard version of value learning. Because a lot of the engineering effort required to make ambitious value learning work will plausibly be in common with the kinds of stuff you have to do to make these more simple specification learning tasks work out. That’s a reason for me to be enthusiastic about people researching value learning even if I’m pessimistic about the overall thing working.

Lucas Perry: All right, so what is robustness and why does it matter?

Rohin Shah: Robustness is one of those words that doesn’t super clearly have a definition and people use it differently. Robust agents don’t fail catastrophically in situations slightly different from the ones that they were designed for. One example of a case where we see a failure of robustness currently, is in adversarial examples for image classifiers, where it is possible to take an image, make a slight perturbation to it, and then the resulting image is completely misclassified. You take a correctly classified image of a Panda, slightly perturb it such that a human can’t tell what the difference is, and then it’s classified as a gibbon with 99% confidence. Admittedly this was with an older image classifier. I think you need to make the perturbations a bit larger now in order to get them.

Lucas Perry: This is because the relevant information that it uses are very local to infer panda-ness rather than global properties of the panda?

Rohin Shah: It’s more like they’re high frequency features or imperceptible features. There’s a lot of controversy about this but there is a pretty popular recent paper that I believe, but not everyone believes, that claims that this was because they’re picking up on real imperceptible features that do generalize to the test set, that humans can’t detect. That’s an example of robustness. Recently people have been applying this to reinforcement learning both by adversarially modifying the observations that agents get and also by training agents that act in the environment adversarially towards the original agent. One paper out of CHAI showed that there’s this kick and defend environment where you’ve got two MuJoCo robots. One of them is kicking a soccer ball. The other one’s a goalie, that’s trying to prevent the kicker from successfully shooting a goal, and they showed that if you do self play in order to get kickers and defenders and then you take the kicker, you freeze it, you don’t train it anymore and you retrain a new defender against this kicker.

What is the strategy that this new defender learns? It just sort of falls to the ground and flaps about in a random looking way and the kicker just gets so confused that it usually fails to even touch the ball and so this is sort of an adversarial example for RL agents now, it’s showing that even they’re not very robust.

There was also a paper out of DeepMind that did the same sort of thing. For their adversarial attack they learned what sorts of mistakes the agent would make early on in training and then just tried to replicate those mistakes once the agent was fully trained and they found that this helped them uncover a lot of bad behaviors. Even at the end of training.

From the perspective of alignment, it’s clear that we want robustness. It’s not exactly clear what we want robustness to. This robustness to adversarial perturbations was kind of a bit weird as a threat model. If there is an adversary in the environment they’re probably not going to be restricted to small perturbations. They’re probably not going to get white box access to your AI system; even if they did, this doesn’t seem to really connect with the AI system as adversarially optimizing against humans story, which is how we get to the x-risk part, so it’s not totally clear.

I think on the intent alignment case, which is the thing that I usually think about, you mostly want to ensure that whatever is driving the “motivation” of the AI system, you want that to be very robust. You want it to agree with what humans would want in all situations or at least all situations that are going to come up or something like that. Paul Christiano has written a few blog posts about this that talk about what techniques he’s excited about solving that problem, which boil down to interpretability, adversarial training, and improving adversarial training through relaxations of the problem.

Buck Shlegeris: I’m pretty confused about this, and so it’s possible what I’m going to say is dumb. When I look at problems with robustness or problems that Rohin put in this robustness category here, I want to divide it into two parts. One of the parts is, things that I think of as capability problems, which I kind of expect the rest of the world will need to solve on its own. For instance, things about safe exploration, how do I get my system to learn to do good things without ever doing really bad things, this just doesn’t seem very related to the AI alignment problem to me. And I also feel reasonably optimistic that you can solve it by doing dumb techniques which don’t have anything too difficult to them, like you can have your system so that it has a good model of the world that it got from unsupervised learning somehow and then it never does dumb enough things. And also I don’t really see that kind of robustness problem leading to existential catastrophes. And the other half of robustness is the half that I care about a lot, which in my mind, is mostly trying to make sure that you succeeded at inner alignment. That is, that the mesa optimizers you’ve found through gradient descent have goals that actually match your goals.

This is like robustness in the sense that you’re trying to guarantee that in every situation, your AI system, as Rohin was saying, is intent aligned with you. It’s trying to do the kind of thing that you want. And I worry that, by default, we’re going to end up with AI systems not intent aligned, so there exist a bunch of situations they can be put in such that they do things that are very much not what you’d want, and therefore they fail at robustness. I think this is a really important problem, it’s like half of the AI safety problem or more, in my mind, and I’m not very optimistic about being able to solve it with prosaic techniques.

Rohin Shah: That sounds roughly similar to what I was saying. Yes.

Buck Shlegeris: I don’t think we disagree about this super much except for the fact that I think you seem to care more about safe exploration and similar stuff than I think I do.

Rohin Shah: I think safe exploration’s a bad example. I don’t know what safe exploration is even trying to solve but I think other stuff, I agree. I do care about it more. One place where I somewhat disagree with you is, you sort of have this point about all these robustness problems are the things that the rest of the world has incentives to figure out, and will probably figure out. That seems true for alignment too, it sure seems like you want your system to be aligned in order to do the things that you actually want. Everyone that has an incentive for this to happen. I totally expect people who aren’t EAs or rationalists or weird longtermists to be working on AI alignment in the future and to some extent even now. I think that’s one thing.

Buck Shlegeris: You should say your other thing, but then I want to get back to that point.

Rohin Shah: The other thing is I think I agree with you that it’s not clear to me how failures of the robustness of things other than motivation lead to x-risk, but I’m more optimistic than you are that our solutions to those kinds of robustness will help with the solutions to “motivation robustness” or how to make your mesa optimizer aligned.

Buck Shlegeris: Yeah, sorry, I guess I actually do agree with that last point. I am very interested in trying to figure out how to have aligned to mesa optimizers, and I think that a reasonable strategy to pursue in order to get aligned mesa optimizers is trying to figure out how to make your image classifiers robust to adversarial examples. I think you probably won’t succeed even if you succeed with the image classifiers, but it seems like the image classifiers are still probably where you should start. And I guess if we can’t figure out how to make image classifiers robust to adversarial examples in like 10 years, I’m going to be super pessimistic about the harder robustness problem, and that would be great to know.

Rohin Shah: For what it’s worth, my take on the adversarial examples of image classifiers is, we’re going to train image classifiers on more data with bigger nets, it’s just going to mostly go away. Prediction. I’m laying my cards on the table.

Buck Shlegeris: That’s also something like my guess.

Rohin Shah: Okay.

Buck Shlegeris: My prediction is: to get image classifiers that are robust to epsilon ball perturbations or whatever, some combination of larger things and adversarial training and a couple other clever things, will probably mean that we have robust image classifiers in 5 or 10 years at the latest.

Rohin Shah: Cool. And you wanted to return to the other point about the world having incentives to do alignment.

Buck Shlegeris: So I don’t quite know how to express this, but I think it’s really important which is going to make this a really fun experience for everyone involved. You know how Airbnb… Or sorry, I guess a better example of this is actually Uber drivers. Where I give basically every Uber driver a five star rating, even though some Uber drivers are just clearly more pleasant for me than others, and Uber doesn’t seem to try very hard to get around these problems, even though I think that if Uber caused there to be a 30% difference in pay between the drivers who I think of as 75th percentile and the drivers I think of as 25th percentile, this would make the service probably noticeably better for me. I guess it seems to me that a lot of the time the world just doesn’t try do kind of complicated things to make systems actually aligned, and it just does hack jobs, and then everyone deals with the fact that everything is unaligned as a result.

To draw this analogy back, I think that we’re likely to have the kind of alignment techniques that solve problems that are as simple and obvious as: we should have a way to have rate your hosts on Airbnb. But I’m worried that we won’t ever get around to solving the problems that are like, but what if your hosts are incentivized to tell you sob stories such that you give them good ratings, even though actually they were worse than some other hosts. And this is never a big enough deal that people are unilaterally individually incentivized to solve the harder version of the alignment problem, and then everyone ends up using these systems that actually aren’t aligned in the strong sense and then we end up in a doomy world. I’m curious if any of that made any sense.

Lucas Perry: Is a simple way to put that we fall into inadequate or an unoptimal equilibrium and then there’s tragedy of the commons and bad game theory stuff that happens that keeps us locked and that the same story could apply to alignment?

Buck Shlegeris: Yeah, that’s not quite what I mean.

Lucas Perry: Okay.

Rohin Shah: I think Buck’s point is that actually Uber or Airbnb could unilaterally, no gains required, make their system better and this would be an improvement for them and everyone else, and they don’t do it. There is nothing about equilibrium that is a failure of Uber to do this thing that seems so obviously good.

Buck Shlegeris: I’m not actually claiming that it’s better for Uber, I’m just claiming that there is a misalignment there. Plausibly, an Uber exec, if they were listening to this they’d just be like, “LOL, that’s a really stupid idea. People would hate it.” And then they would say more complicated things like “most riders are relatively price sensitive and so this doesn’t matter.” And plausibly they’re completely right.

Rohin Shah: That’s what I was going to say.

Buck Shlegeris: But the thing which feels important to me is something like a lot of the time it’s not worth solving the alignment problems at any given moment because something else is a bigger problem to how things are going locally. And this can continue being the case for a long time, and then you end up with everyone being locked in to this system where they never solved the alignment problems. And it’s really hard to make people understand this, and then you get locked into this bad world.

Rohin Shah: So if I were to try and put that in the context of AI alignment, I think this is a legitimate reason for being more pessimistic. And the way that I would make that argument is: it sure seems like we are going to decide on what method or path we’re going to use to build AGI. Maybe we’ll do a bunch of research and decide we’re just going to scale up language models or something like this. I don’t know. And we will do that before we have any idea of which technique would be easiest to align and as a result, we will be forced to try to align this exogenously chosen AGI technique and that would be harder than if we got to design our alignment techniques and our AGI techniques simultaneously.

Buck Shlegeris: I’m imagining some pretty slow take off here, and I don’t imagine this as ever having a phase where we built this AGI and now we need to align it. It’s more like we’re continuously building and deploying these systems that are gradually more and more powerful, and every time we want to deploy a system, it has to be doing something which is useful to someone. And many of the things which are useful, require things that are kind of like alignment. “I want to make a lot of money from my system that will give advice,” and if it wants to give good generalist advice over email, it’s going to need to have at least some implicit understanding of human preferences. Maybe we just use giant language models and everything’s just totally fine here. A really good language model isn’t able to give arbitrarily good aligned advice, but you can get advice that sounds really good from a language model, and I’m worried that the default path is going to involve the most popular AI advice services being kind of misaligned, and just never bothering to fix that. Does that make any more sense?

Rohin Shah: Yeah, I think I totally buy that that will happen. But I think I’m more like as you get to AI systems doing more and more important things in the world, it becomes more and more important that they are really truly aligned and investment in alignment increases correspondingly.

Buck Shlegeris: What’s the mechanism by which people realize that they need to put more work into alignment here?

Rohin Shah: I think there’s multiple. One is I expect that people are aware, like even in the Uber case, I expect people are aware of the misalignment that exists, but decide that it’s not worth their time to fix it. So the continuation of that, people will be aware of it and then they will decide that they should fix it.

Buck Shlegeris: If I’m trying to sell to city governments this language model based system which will give them advice on city planning, it’s not clear to me that at any point the city governments are going to start demanding better alignment features. Maybe that’s the way that it goes but it doesn’t seem obvious that city governments would think to ask that, and —

Rohin Shah: I wasn’t imagining this from the user side. I was imagining this from the engineers or designers side.

Buck Shlegeris: Yeah.

Rohin Shah: I think from the user side I would speak more to warning shots. You know, you have your cashier AI system or your waiter AIs and they were optimizing for tips more so than actually collecting money and so they like offer free meals in order to get more tips. At some point one of these AI systems passes all of the internal checks and makes it out into the world and only then does the problem arise and everyone’s like, “Oh my God, this is terrible. What the hell are you doing? Make this better.”

Buck Shlegeris: There’s two mechanisms via which that alignment might be okay. One of them is that researchers might realize that they want to put more effort into alignment and then solve these problems. The other mechanism is that users might demand better alignment because of warning shots. I think that I don’t buy that either of these is sufficient. I don’t buy that it’s sufficient for researchers to decide to do it because in a competitive world, the researchers who realize this is important, if they try to only make aligned products, they are not going to be able to sell them because their products will be much less good than the unaligned ones. So you have to argue that there is demand for the things which are actually aligned well. But for this to work, your users have to be able to distinguish between things that have good alignment properties and those which don’t, and this seems really hard for users to do. And I guess, when I try to imagine analogies, I just don’t see many examples of people successfully solving problems like this, like businesses making products that are different levels of dangerousness, and then users successfully buying the safe ones.

Rohin Shah: I think usually what happens is you get regulation that forces everyone to be safe. I don’t know if it was regulation, but like airplanes are incredibly safe. Cars are incredibly safe.

Buck Shlegeris: Yeah but in this case what would happen is doing the unsafe thing allows you to make enormous amounts of money, and so the countries which don’t put in the regulations are going to be massively advantaged compared to ones which don’t.

Rohin Shah: Why doesn’t that apply for cars and airplanes?

Buck Shlegeris: So to start with, cars in poor countries are a lot less safe. Another thing is that a lot of the effort in making safer cars and airplanes comes from designing them. Once you’ve done the work of designing it, it’s that much more expensive to put your formally-verified 747 software into more planes, and because of weird features of the fact that there are only like two big plane manufacturers, everyone gets the safer planes.

Lucas Perry: So tying this into robustness. The fundamental concern here is about the incentives to make aligned systems that are safety and alignment robust in the real world.

Rohin Shah: I think that’s basically right. I sort of see these incentives as existing and the world generally being reasonably good at dealing with high stakes problems.

Buck Shlegeris: What’s an example of the world being good at dealing with a high stakes problem?

Rohin Shah: I feel like biotech seems reasonably well handled, relatively speaking,

Buck Shlegeris: Like bio-security?

Rohin Shah: Yeah.

Buck Shlegeris: Okay, if the world handles AI as well as bio-security, there’s no way we’re okay.

Rohin Shah: Really? I’m aware of ways in which we’re not doing bio-security well, but there seem to be ways in which we’re doing it well too.

Buck Shlegeris: The nice thing about bio-security is that very few people are incentivized to kill everyone, and this means that it’s okay if you’re sloppier about your regulations, but my understanding is that lots of regulations are pretty weak.

Rohin Shah: I guess I was more imagining the research community’s coordination on this. Surprisingly good.

Buck Shlegeris: I wouldn’t describe it that way.

Rohin Shah: It seems like the vast majority of the research community is onboard with the right thing and like 1% isn’t. Yeah. Plausibly we need to have regulations for that last 1%.

Buck Shlegeris: I think that 99% of the synthetic biology research community is on board with “it would be bad if everyone died.” I think that some very small proportion is onboard with things like “we shouldn’t do research if it’s very dangerous and will make the world a lot worse.” I would say like way less than half of synthetic biologists seem to agree with statements like “it’s bad to do really dangerous research.” Or like, “when you’re considering doing research, you consider differential technological development.” I think this is just not a thing biologists think about, from my experience talking to biologists.

Rohin Shah: I’d be interested in betting with you on this afterwards.

Buck Shlegeris: Me too.

Lucas Perry: So it seems like it’s going to be difficult to come down to a concrete understanding or agreement here on the incentive structures in the world and whether they lead to the proliferation of unaligned AI systems or semi aligned AI systems versus fully aligned AI systems and whether that poses a kind of lock-in, right? Would you say that that fairly summarizes your concern Buck?

Buck Shlegeris: Yeah. I expect that Rohin and I agree mostly on the size of the coordination problem required, or the costs that would be required by trying to do things the safer way. And I think Rohin is just a lot more optimistic about those costs being paid.

Rohin Shah: I think I’m optimistic both about people’s ability to coordinate paying those costs and about incentives pointing towards paying those costs.

Buck Shlegeris: I think that Rohin is right that I disagree with him about the second of those as well.

Lucas Perry: Are you interested in unpacking this anymore? Are you happy to move on?

Buck Shlegeris: I actually do want to talk about this for two more minutes. I am really surprised by the claim that humans have solved coordination problems as hard as this one. I think the example you gave is humans doing radically nowhere near well enough. What are examples of coordination problem type things… There was a bunch of stuff with nuclear weapons, where I feel like humans did badly enough that we definitely wouldn’t have been okay in an AI situation. There are a bunch of examples of the US secretly threatening people with nuclear strikes, which I think is an example of some kind of coordination failure. I don’t think that the world has successfully coordinated on never threaten first nuclear strikes. If we had successfully coordinated on that, I would consider nuclear weapons to be less of a failure, but as it is the US has actually according to Daniel Ellsberg threatened a bunch of people with first strikes.

Rohin Shah: Yeah, I think I update less on specific scenarios and update quite a lot more on, “it just never happened.” The sheer amount of coincidence that would be required given the level of, Oh my God, there were close calls multiple times a year for many decades. That seems just totally implausible and it just means that our understanding of what’s happening is wrong.

Buck Shlegeris: Again, also the thing I’m imagining is this very gradual takeoff world where people, every year, they release their new most powerful AI systems. And if, in a particular year, AI Corp decided to not release its thing, then AI Corps two and three and four would rise to being one, two and three in total profits instead of two, three and four. In that kind of a world, I feel a lot more pessimistic.

Rohin Shah: I’m definitely imagining more of the case where they coordinate to all not do things. Either by international regulation or via the companies themselves coordinating amongst each other. Even without that, it’s plausible that AI Corp one does this. One example I’d give is, Waymo has just been very slow to deploy self driving cars relative to all the other self driving car companies, and my impression is that this is mostly because of safety concerns.

Buck Shlegeris: Interesting and slightly persuasive example. I would love to talk through this more at some point. I think this is really important and I think I haven’t heard a really good conversation about this.

Apologies for describing what I think is going wrong inside your mind or something, which is generally a bad way of saying things, but it sounds kind of to me like you’re implicitly assuming more concentrated advantage and fewer actors than I think actually are implied by gradual takeoff scenarios.

Rohin Shah: I’m usually imagining something like a 100+ companies trying to build the next best AI system, and 10 or 20 of them being clear front runners or something.

Buck Shlegeris: That makes sense. I guess I don’t quite see how the coordination successes you were describing arise in that kind of a world. But I am happy to move on.

Lucas Perry: So before we move on on this point, is there anything which you would suggest as obvious solutions, should Buck’s model of the risks here be the case. So it seemed like it would demand more centralized institutions which would help to mitigate some of the lock in here.

Rohin Shah: Yeah. So there’s a lot of work in policy and governance about this. Not much of which is public unfortunately. But I think the thing to say is that people are thinking about it and it does sort of look like trying to figure out how to get the world to actually coordinate on things. But as Buck has pointed out, we have tried to do this before and so there’s probably a lot to learn from past cases as well. But I am not an expert on this and don’t really want to talk as though I were one.

Lucas Perry: All right. So there’s lots of governance and coordination thought that kind of needs to go into solving many of these coordination issues around developing beneficial AI. So I think with that we can move along now to scaling to superhuman abilities. So Rohin, what do you have to say about this topic area?

Rohin Shah: I think this is in some sense related to what we were talking about before, you can predict what a human would say, but it’s hard to back out true underlying values beneath them. Here the problem is, suppose you are learning from some sort of human feedback about what you’re supposed to be doing, the information contained in that tells you how to do whatever the human can do. It doesn’t really tell you how to exceed what the human can do without having some additional assumptions.

Now, depending on how the human feedback is structured, this might lead to different things like if the human is demonstrating how to do the task to you, then this would suggest that it would be hard to do the task any better than the human can, but if the human was evaluating how well you did the task, then you can do the task better in a way that the human wouldn’t be able to tell was better. Ideally, at some point we would like to have AI systems that can actually do just really powerful, great things, that we are unable to understand all the details of and so we would neither be able to demonstrate or evaluate them.

How do we get to those sorts of AI systems? The main proposals in this bucket are iterated amplification, debate, and recursive reward modeling. So in iterated amplification, we started with an initial policy, and we alternate between amplification and distillation, which increases capabilities and efficiency respectively. This can encode a bunch of different algorithms, but usually amplification is done by decomposing questions into easier sub questions, and then using the agent to answer those sub questions. While distillation can be done using supervised learning or reinforcement learning, so you get these answers that are created by these amplified systems that take a long time to run, and you just train a neural net to very quickly predict the answers without having to do this whole big decomposition thing. In debate, we train an agent through self play in a zero sum game where the agent’s goal is to win a question answering debate as evaluated by a human judge. The hope here is that since both sides of the debate can point out flaws in the other side’s arguments — they’re both very powerful AI systems — such a set up can use a human judge to train far more capable agents while still incentivizing the agents to provide honest true information. With recursive reward modeling, you can think of it as an instantiation of the general alternate between amplification and distillation framework, but it works sort of bottom up instead of top down. So you’ll start by building AI systems that can help you evaluate simple, easy tasks. Then use those AI systems to help you evaluate more complex tasks and you keep iterating this process until eventually you have AI systems that help you with very complex tasks like how to design the city. And this lets you then train an AI agent that can design the city effectively even though you don’t totally understand why it’s doing the things it’s doing or why they’re even good.

Lucas Perry: Do either of you guys have any high level thoughts on any of these approaches to scaling to superhuman abilities?

Buck Shlegeris: I have some.

Lucas Perry: Go for it.

Buck Shlegeris: So to start with, I think it’s worth noting that another approach would be ambitious value learning, in the sense that I would phrase these not as approaches for scaling to superhuman abilities, but they’re like approaches for scaling to superhuman abilities while only doing tasks that relate to the actual behavior of humans rather than trying to back out their values explicitly. Does that match your thing Rohin?

Rohin Shah: Yeah, I agree. I often phrase that as with ambitious value learning, there’s not a clear ground truth to be focusing on, whereas with all three of these methods, the ground truth is what a human would do if they got a very, very long time to think or at least that is what they’re trying to approximate. It’s a little tricky to see why exactly they’re approximating that, but there are some good posts about this. The key difference between these techniques and ambitious value learning is that there is in some sense a ground truth that you are trying to approximate.

Buck Shlegeris: I think these are all kind of exciting ideas. I think they’re all kind of better ideas than I expected to exist for this problem a few years ago. Which probably means we should update against my ability to correctly judge how hard AI safety problems are, which is great news, in as much as I think that a lot of these problems are really hard. Nevertheless, I don’t feel super optimistic that any of them are actually going to work. One thing which isn’t in the elevator pitch for IDA, which is iterated distillation and amplification (and debate), is that you get to hire the humans who are going to be providing the feedback, or the humans whose answers AI systems are going to be trained with. And this is actually really great. Because for instance, you could have this program where you hire a bunch of people and you put them through your one month long training an AGI course. And then you only take the top 50% of them. I feel a lot more optimistic about these proposals given you’re allowed to think really hard about how to set it up such that the humans have the easiest time possible. And this is one reason why I’m optimistic about people doing research in factored cognition and stuff, which I’m sure Rohin’s going to explain in a bit.

One comment about recursive reward modeling: it seems like it has a lot of things in common with IDA. The main downside that it seems to have to me is that the human is in charge of figuring out how to decompose the task into evaluations at a variety of levels. Whereas with IDA, your system itself is able to naturally decompose the task into a variety levels, and for this reason I feel a bit more optimistic about IDA.

Rohin Shah: With recursive reward modeling, one agent that you can train is just an agent that’s good at doing decompositions. That is a thing you can do with it. It’s a thing that the people at DeepMind are thinking about. 

Buck Shlegeris: Yep, that’s a really good point. 

Rohin Shah: I also strongly like the fact that you can train your humans to be good at providing feedback. This is also true about specification learning. It’s less clear if it’s true about ambitious value learning. No one’s really proposed how you could do ambitious value learning really. Maybe arguably Stuart Russell’s book is kind of a proposal, but it doesn’t have that many details.

Buck Shlegeris: And, for example, it doesn’t address any of my concerns in ways that I find persuasive.

Rohin Shah: Right. But for specification learning also you definitely want to train the humans who are going to be providing feedback to the AI system. That is an important part of why you should expect this to work.

Buck Shlegeris: I often give talks where I try to give an introduction to IDA and debate as a proposal for AI alignment. I’m giving these talks to people with computer science backgrounds, and they’re almost always incredibly skeptical that it’s actually possible to decompose thought in this kind of a way. And with debate, they’re very skeptical that truth wins, or that the nash equilibrium is accuracy. For this reason I’m super enthusiastic about research into the factored cognition hypothesis of the type that Ought is doing some of.

I’m kind of interested in your overall take for how likely it is that the factored cognition hypothesis holds and that it’s actually possible to do any of this stuff, Rohin. You could also explain what that is.

Rohin Shah: I’ll do that. So basically with both iterated amplification, debate, or recursive reward modeling, they all hinge on this idea of being able to decompose questions, maybe it’s not so obvious why that’s true for debate, but it’s true. Go listen to the podcast about debate if you want to get more details on that.

So this hypothesis is basically for any tasks that we care about, it is possible to decompose this into a bunch of sub tasks that are all easier to do. Such that if you’re able to do the sub tasks, then you can do the overall top level tasks and in particular you can iterate this down, building a tree of smaller and smaller tasks until you can get to the level of tasks that a human could do in a day. Or if you’re trying to do it very far, maybe tasks that a human can do in a couple of minutes. Whether or not you can actually decompose the task “be an effective CEO” into a bunch of sub tasks that eventually bottom out into things humans can do in a few minutes is totally unclear. Some people are optimistic, some people are pessimistic. It’s called the factored cognition hypothesis and Ought is an organization that’s studying it.

It sounds very controversial at first and I, like many other people had the intuitive reaction of, ‘Oh my God, this is never going to work and it’s not true’. I think the thing that actually makes me optimistic about it is you don’t have to do what you might call a direct decomposition. You can do things like if your task is to be an effective CEO, your first sub question could be, what are the important things to think about when being a CEO or something like this, as opposed to usually when I think of decompositions I would think of, first I need to deal with hiring. Maybe I need to understand HR, maybe I need to understand all of the metrics that the company is optimizing. Very object level concerns, but the decompositions are totally allowed to also be meta level where you’ll spin off a bunch of computation that is just trying to answer the meta level of question of how should I best think about this question at all.

Another important reason for optimism is that based on the structure of iterated amplification, debate and recursive reward modeling, this tree can be gigantic. It can be exponentially large. Something that we couldn’t run even if we had all of the humans on Earth collaborating to do this. That’s okay. Given how the training process is structured, considering the fact that you can do the equivalent of millennia of person years of effort in this decomposed tree, I think that also gives me more of a, ‘okay, maybe this is possible’ and that’s also why you’re able to do all of this meta level thinking because you have a computational budget for it. When you take all of those together, I sort of come up with “seems possible. I don’t really know.”

Buck Shlegeris: I think I’m currently at 30-to-50% on the factored cognition thing basically working out. Which isn’t nothing.

Rohin Shah: Yeah, that seems like a perfectly reasonable thing. I think I could imagine putting a day of thought into it and coming up with numbers anywhere between 20 and 80.

Buck Shlegeris: For what it’s worth, in conversation at some point in the last few years, Paul Christiano gave numbers that were not wildly more optimistic than me. I don’t think that the people who are working on this think it’s obviously fine. And it would be great if this stuff works, so I’m really in favor of people looking into it.

Rohin Shah: Yeah, I should mention another key intuition against it. We have all these examples of human geniuses like Ramanujan, who were posed very difficult math problems and just immediately get the answer and then you ask them how did they do it and they say, well, I asked myself what should the answer be? And I was like, the answer should be a continued fraction. And then I asked myself which continued fraction and then I got the answer. And you’re like, that does not sound very decomposable. It seems like you need these magic flashes of intuition. Those would be the hard cases for factored cognition. It still seems possible that you could do it by both this exponential try a bunch of possibilities and also by being able to discover intuitions that work in practice and just believing them because they work in practice and then applying them to the problem at hand. You could imagine that with enough computation you’d be able to discover such intuitions.

Buck Shlegeris: You can’t answer a math problem by searching exponentially much through the search tree. The only exponential power you get from IDA is IDA is letting you specify the output of your cognitive process in such a way that’s going to match some exponentially sized human process. As long as that exponentially sized human process was only exponentially sized because it’s really inefficient, but is kind of fundamentally not an exponentially sized problem, then your machine learning should be able to speed it up a bunch. But the thing where you search over search strategy is not valid. If that’s all you can do, that’s not good enough.

Rohin Shah: Searching over search strategies, I agree you can’t do, but if you have an exponential search that could be implemented by humans. We know by hypothesis, if you can solve it with a flash of intuition, there is in fact some more efficient way to do it and so whether or not the distillation steps will actually be enough to get to the point where you can do those flashes of intuition. That’s an open question.

Buck Shlegeris: This is one of my favorite areas of AI safety research and I would love for there to be more of it. Something I have been floating for a little while is I kind of wish that there was another Ought. It just seems like it would be so good if we had definitive information about the factored cognition hypothesis. And it also it seems like the kind of thing which is potentially parallelizable. And I feel like I know a lot of people who love talking about how thinking works. A lot of rationalists are really into this. I would just be super excited for some of them to form teams of four and go off on their own and build an Ought competitor. I feel like this is the kind of thing where plausibly, a bunch of enthusiastic people could make progress on their own.

Rohin Shah: Yeah, I agree with that. Definitely seems like one of the higher value things but I might be more excited about universality.

Lucas Perry: All right, well let’s get started with universality then. What is universality and why are you optimistic about it?

Rohin Shah: So universality is hard to explain well, in a single sentence. For whatever supervisor is training our agent, you want that supervisor to “know everything the agent knows.” In particular if the agent comes up with some deceptive strategy to look like it’s achieving the goal, but actually it hasn’t. The supervisors should know that it was doing this deceptive strategy for the reason of trying to trick the supervisor and so the supervisor can then penalize it. The classic example of why this is important and hard also due to Paul Christiano is plagiarism. Suppose you are training on the AI system to produce novel works of literature and as part of its training data, the AI system gets to read this library of a million books.

It’s possible that this AI system decides, Hey, you know the best way I can make a great novel seeming book is to just take these five books and take out plot points, passages from each of them and put them together and then this new book will look totally novel and will be very good because I used all of the best Shakespearean writing or whatever. If your supervisor doesn’t know that the agent has done this, the only way the supervisor can really check is to go read the entire million books. Even if the agent only read 10 books and so then the supervision becomes a way more costly than running the agent, which is not a great state to be in, and so what you really want is that if the agent does this, the supervisor is able to say, I see that you just copied this stuff over from these other books in order to trick me into thinking that you had written something novel that was good.

That’s bad. I’m penalizing you. Stop doing that in the future. Now, this sort of property, I mean it’s very nice in the abstract, but who knows whether or not we can actually build it in practice. There’s some reason for optimism that I don’t think I can adequately convey, but I wrote a newsletter summarizing some of it sometime ago, but again, reading through the posts I became more optimistic that it was an achievable property, than when I first heard what the property was. The reason I’m optimistic about it is that it just sort of seems to capture the thing that we actually care about. It’s not everything, like it doesn’t solve the robustness problem. Universality only tells you what the agent’s currently doing. You know all the facts about that. Whereas for robustness you want to say even in these hypothetical situations that the agent hasn’t encountered yet and doesn’t know stuff about, even when it encounters those situations, it’s going to stay aligned with you so universality doesn’t get you all the way there, but it definitely feels like it’s getting you quite a bit.

Buck Shlegeris: That’s really interesting to hear you phrase it that way. I guess I would have thought of universality as a subset of robustness. I’m curious what you think of that first.

Rohin Shah: I definitely think you could use universality to achieve a subset of robustness. Maybe I would say universality is a subset of interpretability.

Buck Shlegeris: Yeah, and I care about interpretability as a subset of robustness basically, or as a subset of inner alignment, which is pretty close to robustness in my mind. The other thing I would say is you were saying there that one difference between universality and robustness is that universality only tells you why the agent did the thing it currently did, and this doesn’t suffice to tell us about the situations that the agent isn’t currently in. One really nice thing though is that if the agent is only acting a particular way because it wants you to trust it, that’s a fact about its current behavior that you will know, and so if you have the universality property, your overseer just knows your agent is trying to deceive it. Which seems like it would be incredibly great and would resolve like half of my problem with safety if you had it.

Rohin Shah: Yeah, that seems right. The case that universality doesn’t cover is when your AI system is initially not deceptive, but then at some point in the future it’s like, ‘Oh my God, now it’s possible to go and build Dyson spheres or something, but wait, in this situation probably I should be doing this other thing and humans won’t like that. Now I better deceive humans’. The transition into deception would have to be a surprise in some sense even to the AI system.

Buck Shlegeris: Yeah, I guess I’m just not worried about that. Suppose I have this system which is as smart as a reasonably smart human or 10 reasonably smart humans, but it’s not as smart as the whole world. If I can just ask it what its best sense about how aligned it is, is? And if I can trust its answer? I don’t know man, I’m pretty okay with systems that think they’re aligned, answering that question honestly.

Rohin Shah: I think I somewhat agree. I like this reversal where I’m the pessimistic one.

Buck Shlegeris: Yeah me too. I’m like, “look, system, I want you to think as hard as you can to come up with the best arguments you can come up with for why you are misaligned, and the problems with you.” And if I just actually trust the system to get this right, then the bad outcomes I get here are just pure accidents. I just had this terrible initialization of my neural net parameters, such that I had this system that honestly believed that it was going to be aligned. And then as it got trained more, this suddenly changed and I couldn’t do anything about it. I don’t quite see the story for how this goes super wrong. It seems a lot less bad than the default situation.

Rohin Shah: Yeah. I think the story I would tell is something like, well, if you look at humans, they’re pretty wrong about what their preferences will be in the future. For example, there’s this trope of how teenagers fall in love and then fall out of love, but when they’re in love, they swear undying oaths to each other or something. To the extent that is true, that seems like the sort of failure that could lead to x-risk if it also happened with AI systems.

Buck Shlegeris: I feel pretty optimistic about all the garden-variety approaches to solving this. Teenagers were not selected very hard on accuracy of their undying oaths. And if you instead had accuracy of self-model as a key feature you were selecting for in your AI system, plausibly you’ll just be way more okay.

Rohin Shah: Yeah. Maybe people could coordinate well on this. I feel less good about people coordinating on this sort of problem.

Buck Shlegeris: For what it’s worth, I think there are coordination problems here and I feel like my previous argument about why coordination is hard and won’t happen by default also probably applies to us not being okay. I’m not sure how this all plays out. I’d have to think about it more.

Rohin Shah: Yeah. I think it’s more like this is a subtle and non-obvious problem, which by hypothesis doesn’t happen in the systems you actually have and only happens later and those are the sorts of problems I’m like, Ooh, not sure if we can deal with those ones, but I agree that there’s a good chance that there’s just not a problem at all in the world where we already have universality and checked all the obvious stuff.

Buck Shlegeris: Yeah. I would like to say universality is one of my other favorite areas of AI alignment research, in terms of how happy I’d be if it worked out really well.

Lucas Perry: All right, so let’s see if we can slightly pick up the pace here. Moving forward and starting with interpretability.

Rohin Shah: Yeah, so I mean I think we’ve basically discussed interpretability already. Universality is a specific kind of interpretability, but the case for interpretability is just like, sure seems like it would be good if you could understand what your AI systems are doing. You could then notice when they’re not aligned, and fix that somehow. It’s a pretty clear cut case for a thing that would be good if we achieved it and it’s still pretty uncertain how likely we are to be able to achieve it.

Lucas Perry: All right, so let’s keep it moving and let’s hit impact regularization now.

Rohin Shah: Yeah, impact regularization in particular is one of the ideas that are not trying to align the AI system but are instead trying to say, well, whatever AI system we build, let’s make sure it doesn’t cause a catastrophe. It doesn’t lead to extinction or existential risk. What it hopes to do is say, all right, AI system, do whatever it is you wanted to do. I don’t care about that. Just make sure that you don’t have a huge impact upon the world.

Whatever you do, keep your impact not too high. And so there’s been a lot of work on this in recent years there’s been relative reachability, attainable utility preservation, and I think in general the sense is like, wow, it’s gone quite a bit further than people expected it to go. I think it definitely does prevent you from doing very, very powerful things of the sort, like if you wanted to stop all competing AI projects from ever being able to build AGI, that doesn’t seem like the sort of thing you can do with an impact regularized AI system, but it sort of seems plausible that you could prevent convergent instrumental sub goals using impact regularization. Where AI systems that are trying to steal resources and power from humans, you could imagine that you’d say, hey, don’t do that level of impact, you can still have the level of impact of say running a company or something like that.

Buck Shlegeris: My take on all this is that I’m pretty pessimistic about all of it working. I think that impact regularization or whatever is a non-optimal point on the capabilities / alignment trade off or something, in terms of safety you’re getting for how much capability you’re sacrificing. My basic a problem here is basically analogous to my problem with value learning, where I think we’re trying to take these extremely essentially fuzzy concepts and then factor our agent through these fuzzy concepts like impact, and basically the thing that I imagine happening is any impact regularization strategy you try to employ, if your AI is usable, will end up not helping with its alignment. For any definition of impacts you come up with, it’ll end up doing something which gets around that. Or it’ll make your AI system completely useless, is my basic guess as to what happens.

Rohin Shah: Yeah, so I think again in this setting, if you formalize it and then say, consider the optimal agent. Yeah, that can totally get around your impact penalty, but in practice it sure seems like, what you want to do is say this convergent instrumental subgoal stuff, don’t do any of that. Continue to do things that are normal in regular life. And those seem like pretty distinct categories. Such that I would not be shocked if we could actually distinguish between the two.

Buck Shlegeris: It sounds like the main benefit you’re going for is trying to make your AI system not do insane, convergent, instrumental sub-goal style stuff. So another approach I can imagine taking here would be some kind of value learning or something, where you’re asking humans for feedback on whether plans are insanely convergent, instrumental sub-goal style, and just not doing the things which, when humans are asked to rate how sketchy the plans are the humans rate as sufficiently sketchy? That seems like about as good a plan. I’m curious what you think.

Rohin Shah: The idea of power as your attainable utility across a wide variety of utility functions seems like a pretty good formalization to me. I think in the worlds where I actually buy a formalization, I tend to expect the formalization to work better. I do think the formalization is not perfect. Most notably with the current formalization of power, your power never changes if you have extremely good beliefs. Your notion, you’re just like, I always have the same power because I’m always able to do the same things and you never get surprised, so maybe I agree with you because I think the current formalization is not good enough.  (The strike through section has been redacted by Rohin. It’s incorrect and you can see why here.) Yeah, I think I agree with you but I could see it going either way.

Buck Shlegeris: I could be totally wrong about this, and correct me if I’m wrong, my sense is that you have to be able to back out the agent’s utility function or its models of the world. Which seems like it’s assuming a particular path for AI development which doesn’t seem to me particularly likely.

Rohin Shah: I definitely agree with that for all the current methods too.

Buck Shlegeris: So it’s like: assume that we have already perfectly solved our problems with universality and robustness and transparency and whatever else. I feel like you kind of have to have solved all of those problems before you can do this, and then you don’t need it or something.

Rohin Shah: I don’t think I agree with that. I definitely agree that the current algorithms that people have written assume that you can just make a change to the AI’s utility function. I don’t think that’s what even their proponents would suggest as the actual plan.

Buck Shlegeris: What is the actual plan?

Rohin Shah: I don’t actually know what their actual plan would be, but one plan I could imagine is figure out what exactly the conceptual things we have to do with impact measurement are, and then whatever method we have for building AGI, probably there’s going to be some part which is specify the goal and then in the specify goal part, instead of just saying pursue X, we want to say pursue X without changing your ability to pursue Y, and Z and W, and P, and Q.

Buck Shlegeris: I think that that does not sound like a good plan. I don’t think that we should expect our AI systems to be structured that way in the future.

Rohin Shah: Plausibly we have to do this with natural language or something.

Buck Shlegeris: It seems very likely to me that the thing you do is reinforcement learning where at the start of the episode you get a sentence of English which is telling you what your goal is and then blah, blah, blah, blah, blah, and this seems like a pretty reasonable strategy for making powerful and sort of aligned AI. Aligned enough to be usable for things that aren’t very hard. But you just fundamentally don’t have access to the internal representations that the AI is using for its sense of what belief is, and stuff like that. And that seems like a really big problem.

Rohin Shah: I definitely see this as more of an outer alignment thing, or like an easier to specify outer alignment type thing than say, IDA is that type stuff.

Buck Shlegeris: Okay, I guess that makes sense. So we’re just like assuming we’ve solved all the inner alignment problems?

Rohin Shah: In the story so far yeah, I think all of the researchers who actually work on this haven’t thought much about inner alignment.

Buck Shlegeris: My overall summary is that I really don’t like this plan. I feel like it’s not robust to scale. As you were saying Rohin, if your system gets more and more accurate beliefs, stuff breaks. It just feels like the kind of thing that doesn’t work.

Rohin Shah: I mean, it’s definitely not conceptually neat and elegant in the sense of it’s not attacking the underlying problem. And in a problem setting where you expect adversarial optimization type dynamics, conceptual elegance actually does count for quite a lot in whether or not you believe your solution will work.

Buck Shlegeris: I feel it’s like trying to add edge detectors to your image classifiers to make them more adversarily robust or something, which is backwards.

Rohin Shah: Yeah, I think I agree with that general perspective. I don’t actually know if I’m more optimistic than you. Maybe I just don’t say… Maybe we’d have the same uncertainty distributions and you just say yours more strongly or something.

Lucas Perry: All right, so then let’s just move a little quickly through the next three, which are causal modeling, oracles, and decision theory.

Rohin Shah: Yeah, I mean, well decision theory, MIRI did some work on it. I am not the person to ask about it, so I’m going to skip that one. Even if you look at the long version, I’m just like, here are some posts. Good luck. So causal modeling, I don’t fully understand what the overall story is here but the actual work that’s been published is basically what we can do is we can take potential plans or training processes for AI systems. We can write down causal models that tell us how the various pieces of the training system interact with each other and then using algorithms developed for causal models we can tell when an AI system would have an incentive to either observe or intervene on an underlying variable.

One thing that came out of this was that you can build a model-based reinforcement learner that doesn’t have any incentive to wire head as long as when it makes its plans, the plans are evaluated by the current reward function as opposed to whatever future reward function it would have. And that was explained using this framework of causal modeling. Oracles, Oracles are basically the idea that we can just train an AI system to just answer questions, give it a question and it tries to figure out the best answer it can to that question, prioritizing accuracy.

One worry that people have recently been talking about is the predictions that the Oracle makes then affect the world, which can affect whether or not the prediction was correct. Like maybe if I predict that I will go to bed at 11 then I’m more likely to actually go to bed at 11 because I want my prediction to come true or something and so then the Oracles can still “choose” between different self confirming predictions and so that gives them a source of agency and one way that people want to avoid this is using what are called counter-factual Oracles where you set up the training, such that the Oracles are basically making predictions under the assumption that their predictions are not going to influence the future.

Lucas Perry: Yeah, okay. Oracles seem like they just won’t happen. There’ll be incentives to make things other than Oracles and that Oracles would even be able to exert influence upon the world in other ways.

Rohin Shah: Yeah, I think I agree that Oracles do not seem very competitive.

Lucas Perry: Let’s do forecasting now then.

Rohin Shah: So the main sub things within forecasting one, there’s just been a lot of work recently on actually building good forecasting technology. There has been an AI specific version of Metaculus that’s been going on for a while now. There’s been some work at the Future of Humanity Institute on building better tools for working with probability distributions under recording and evaluating forecasts. There was an AI resolution council where basically now you can make forecasts about what this particular group of people will think in five years or something like that, which is much easier to operationalize than most other kinds of forecasts. So this helps with constructing good questions. On the actual object level, I think there are two main things. One is that it became increasingly more obvious in the past two years that AI progress currently is being driven by larger and larger amounts of compute.

It totally could be driven by other things as well, but at the very least, compute is a pretty important factor. And then takeoff speeds. So there’s been this long debate in the AI safety community over whether — to take the extremes, whether or not we should expect that AI capabilities will see a very sharp spike. So initially, your AI capabilities are improving by like one unit a year, maybe then with some improvements it got to two units a year and then for whatever reason, suddenly they’re now at 20 units a year or a hundred units a year and they just swoop way past what you would get by extrapolating past trends, and so that’s what we might call a discontinuous takeoff. If you predict that that won’t happen instead you’ll get AI that’s initially improving at one unit per year. Then maybe two units per year, maybe three units per year. Then five units per year, and the rate of progress continually increases. The world’s still gets very, very crazy, but in a sort of gradual, continuous way that would be called a continuous takeoff.

Basically there were two posts that argued pretty forcefully for continuous takeoff back in, I want to say February of 2018, and this at least made me believe that continuous takeoff was more likely. Sadly, we just haven’t actually seen much defense of the other side of the view since then. Even though we do know that there definitely are people who still believe the other side, that there will be a discontinuous takeoff.

Lucas Perry: Yeah so what are both you guys’ views on them?

Buck Shlegeris: Here are a couple of things. One is that I really love the operationalization of slow take off or continuous take off that Paul provided in his post, which was one of the ones Rohin was referring to from February 2018. He says, “by slow takeoff, I mean that there is a four year doubling of the economy before there is a one year doubling of the economy.” As in, there’s a period of four years over which world GDP increases by a factor four, after which there is a period of one year. As opposed to a situation where the first one-year doubling happens out of nowhere. Currently, doubling times for the economy are on the order of like 20 years, and so a one year doubling would be a really big deal. The way that I would phrase why we care about this, is because worlds where we have widespread, human level AI feel like they have incredibly fast economic growth. And if it’s true that we expect AI progress to increase gradually and continuously, then one important consequence of this is that by the time we have human level AI systems, the world is already totally insane. A four year doubling would just be crazy. That would be economic growth drastically higher than economic growth currently is.

This means it would be obvious to everyone who’s paying attention that something is up and the world is radically changing in a rapid fashion. Another way I’ve been thinking about this recently is people talk about transformative AI, by which they mean AI which would have at least as much of an impact on the world as the industrial revolution had. And it seems plausible to me that octopus level AI would be transformative. Like suppose that AI could just never get better than octopus brains. This would be way smaller of a deal than I expect AI to actually be, but it would still be a massive deal, and would still possibly lead to a change in the world that I would call transformative. And if you think this is true, and if you think that we’re going to have octopus level AI before we have human level AI, then you should expect that radical changes that you might call transformative have happened by the time that we get to the AI alignment problems that we’ve been worrying about. And if so, this is really big news.

When I was reading about this stuff when I was 18, I was casually imagining that the alignment problem is a thing that some people have to solve while they’re building an AGI in their lab while the rest of the world’s ignoring them. But if the thing which is actually happening is the world is going insane around everyone, that’s a really important difference.

Rohin Shah: I would say that this is probably the most important contested question in AI alignment right now. Some consequences of it are in a gradual or continuous takeoff world you expect that by the time we get to systems that can pose an existential risk. You’ve already had pretty smart systems that have been deployed in the real world. They probably had some failure modes. Whether or not we call them alignment failure modes or not is maybe not that important. The point is people will be aware that AI systems can fail in weird ways, depending on what sorts of failures you expect, you might expect this to lead to more coordination, more involvement in safety work. You might also be more optimistic about using testing and engineering styles of approaches to the problem which rely a bit more on trial and error type of reasoning because you actually will get a chance to see errors before they happen at a super intelligent existential risk causing mode. There are lots of implications of this form that pretty radically change which alignment plans you think are feasible.

Buck Shlegeris: Also, it’s pretty radically changed how optimistic you are about this whole AI alignment situation, at the very least, people who are very optimistic about AI alignment causing relatively small amounts of existential risk. A lot of the reason for this seems to be that they think that we’re going to get these warning shots where before we have superintelligent AI, we have sub-human level intelligent AI with alignment failures like the cashier Rohin was talking about earlier. And then people start caring about AI alignment a lot more. So optimism is also greatly affected by what you think about this.

I’ve actually been wanting to argue with people about this recently. I wrote a doc last night where I was arguing that even in gradual takeoff worlds, we should expect a reasonably high probability of doom if we can’t solve the AI alignment problem. And I’m interested to have this conversation in more detail with people at some point. But yeah, I agree with what Rohin said.

Overall on takeoff speeds, I guess I still feel pretty uncertain. It seems to me that currently, what we can do with AI, like AI capabilities are increasing consistently, and a lot of this comes from applying relatively non-mindblowing algorithmic ideas to larger amounts of compute and data. And I would be kind of surprised if you can’t basically ride this wave away until you have transformative AI. And so if I want to argue that we’re going to have fast takeoffs, I kind of have to argue that there’s some other approach you can take which lets you build AI without having to go along that slow path, which also will happen first. And I guess I think it’s kind of plausible that that is what’s going to happen. I think that’s what you’d have to argue for if you want to argue for a fast take off.

Rohin Shah: That all seems right to me. I’d be surprised if, out of nowhere, we saw a new AI approach suddenly started working and overtook deep learning. You also have to argue that it then very quickly reaches human level AI, which would be quite surprising, right? In some sense, it would have to be something completely novel that we failed to think about in the last 60 years. We’re putting in way more effort now than we were in the last 60 years, but then counter counterpoint is that all of that extra effort is going straight into deep learning. It’s not really searching for completely new paradigm-shifting ways to get to AGI.

Buck Shlegeris: So here’s how I’d make that argument. Perhaps a really important input into a field like AI, is the number of really smart kids who have been wanting to be an AI researcher since they were 16 because they thought that it’s the most important thing in the world. I think that in physics, a lot of the people who turn into physicists have actually wanted to be physicists forever. I think the number of really smart kids who wanted to be AI researchers forever has possibly gone up by a factor of 10 over the last 10 years, it might even be more. And there just are problems sometimes, that are bottle necked on that kind of a thing, probably. And so it wouldn’t be totally shocking to me, if as a result of this particular input to AI radically increasing, we end up in kind of a different situation. I haven’t quite thought through this argument fully.

Rohin Shah: Yeah, the argument seems plausible. There’s a large space of arguments like this. I think even after that, then I’ve started questioning, “Okay, we get a new paradigm. The same arguments apply to that paradigm?” Not as strongly. I guess not the arguments you were saying about compute going up over time, but the arguments given in the original slow takeoff posts which were people quickly start taking the low-hanging fruit and then move on. When there’s a lot of effort being put into getting some property, you should expect that easy low-hanging fruit is usually just already taken, and that’s why you don’t expect discontinuities. Unless the new idea just immediately rockets you to human-level AGI, or x-risk causing AGI, I think the same argument would pretty quickly start applying to that as well.

Buck Shlegeris: I think it’s plausible that you do get rocketed pretty quickly to human-level AI. And I agree that this is an insane sounding claim.

Rohin Shah: Great. As long as we agree on that.

Buck Shlegeris: Something which has been on my to-do list for a while, and something I’ve been doing a bit of and I’d be excited for someone else doing more of, is reading the history of science and getting more of a sense of what kinds of things are bottlenecked by what, where. It could lead me to be a bit less confused about a bunch of this stuff. AI Impacts has done a lot of great work cataloging all of the things that aren’t discontinuous changes, which certainly is a strong evidence to me against my claim here.

Lucas Perry: All right. What is the probability of AI-induced existential risk?

Rohin Shah: Unconditional on anything? I might give it 1 in 20. 5%.

Buck Shlegeris: I’d give 50%.

Rohin Shah: I had a conversation with AI Impacts that went into this in more detail and partially just anchored on the number I gave there, which was 10% conditional on no intervention from longtermists, I think the broad argument is really just the one that Buck and I were disagreeing about earlier, which is to what extent will society be incentivized to solve the problem? There’s some chance that the first thing we try just works and we don’t even need to solve any sort of alignment problem. It might just be fine. This is not implausible to me. Maybe that’s 30% or something.

Most of the remaining probability comes from, “Okay, the alignment problem is a real problem. We need to deal with it.” It might be very easy in which case we can just solve it straight away. That might be the case. That doesn’t seem that likely to me if it was a problem at all. But what we will get is a lot of these warning shots and people understanding the risks a lot more as we get more powerful AI systems. This estimate is also conditional on gradual takeoff. I keep forgetting to say that, mostly because I don’t know what probability I should put on discontinuous takeoff.

Lucas Perry: So is 5% with longtermist intervention, increasing to 10% if fast takeoff?

Rohin Shah: Yes, but still with longtermist intervention. I’m pretty pessimistic on fast takeoff, but my probability assigned to fast takeoff is not very high. In a gradual takeoff world, you get a lot of warning shots. There will just generally be awareness of the fact that the alignment problem is a real thing and you won’t have the situation you have right now of people saying this thing about worrying about superintelligent AI systems not doing what we want is totally bullshit. That won’t be a thing. Almost everyone will not be saying that anymore, in the version where we’re right and there is a problem. As a result, people will not want to build AI systems that are going to kill them. People tend to be pretty risk averse in my estimation of the world, which Buck will probably disagree with. And as a result, you’ll get a lot of people trying to actually work on solving the alignment problem. There’ll be some amount of global coordination which will give us more time to solve the alignment problem than we may otherwise have had. And together, these forces mean that probably we’ll be okay.

Buck Shlegeris: So I think my disagreements with Rohin are basically that I think fast takeoffs are more likely. I basically think there is almost surely a problem. I think that alignment might be difficult, and I’m more pessimistic about coordination. I know I said four things there, but I actually think of this as three disagreements. I want to say that “there isn’t actually a problem” is just a kind of “alignment is really easy to solve.” So then there’s three disagreements. One is gradual takeoff, another is difficulty of solving competitive prosaic alignment, and another is how good we are at coordination.

I haven’t actually written down these numbers since I last changed my mind about a lot of the inputs to them, so maybe I’m being really dumb. I guess, it feels to me that in fast takeoff worlds, we are very sad unless we have competitive alignment techniques, and so then we’re just only okay if we have these competitive alignment techniques. I guess I would say that I’m something like 30% on us having good competitive alignment techniques by the time that it’s important, which incidentally is higher than Rohin I think.

Rohin Shah: Yeah, 30 is totally within the 25th to 75th interval on the probability, which is a weird thing to be reporting. 30 might be my median, I don’t know.

Buck Shlegeris: To be clear, I’m not just including the outer alignment proportion here, which is what we were talking about before with IDA. I’m also including the inner alignment.

Rohin Shah: Yeah, 30% does seem a bit high. I think I’m a little more pessimistic.

Buck Shlegeris: So I’m like 30% that we can just solve the AI alignment problem in this excellent way, such that anyone who wants to can have very little extra cost and then make AI systems that are aligned. I feel like in worlds where we did that, it’s pretty likely that things are reasonably okay. I think that the gradual versus fast takeoff isn’t actually enormously much of a crux for me because I feel like in worlds without competitive alignment techniques and gradual takeoff, we still have a very high probability of doom. And I think that comes down to disagreements about coordination. So maybe the main important disagreement between Rohin and I is, actually how well we’ll be able to coordinate, or how strongly individual incentives will be for alignment.

Rohin Shah: I think there are other things. The reason I feel a bit more pessimistic than you in the fast takeoff world is just solving problems in advance just is really quite difficult and I really like the ability to be able to test techniques on actual AI systems. You’ll have to work with less powerful things. At some point, you do have to make the jump to more powerful things. But, still, being able to test on the less powerful things, that’s so good, so much safety from there.

Buck Shlegeris: It’s not actually clear to me that you get to test the most important parts of your safety techniques. So I think that there are a bunch of safety problems that just do not occur on dog-level AIs, and do occur on human-level AI. If there are three levels of AI, there’s a thing which is as powerful as a dog, there’s a thing which is as powerful as a human, and there’s a thing which is as powerful as a thousand John von Neumanns. In gradual takeoff world, you have a bunch of time in both of these two milestones, maybe. I guess it’s not super clear to me that you can use results on less powerful systems as that much evidence about whether your safety techniques work on drastically more powerful systems. It’s definitely somewhat helpful.

Rohin Shah: It depends what you condition on in your difference between continuous takeoff and discontinuous takeoff to say which one of them happens faster. I guess the delta between dog and human is definitely longer in gradual takeoff for sure. Okay, if that’s what you were saying, yep, I agree with that.

Buck Shlegeris: Yeah, sorry, that’s all I meant.

Rohin Shah: Cool. One thing I wanted to ask is when you say dog-level AI assistant, do you mean something like a neural net that if put in a dog’s body replacing its brain would do about as well as a dog? Because such a neural net could then be put in other environments and learn to become really good at other things, probably superhuman at many things that weren’t in the ancestral environment. Do you mean that sort of thing?

Buck Shlegeris: Yeah, that’s what I mean. Dog-level AI is probably much better than GPT2 at answering questions. I’m going to define something as dog-level AI, if it’s about as good as a dog at things which I think dogs are pretty heavily optimized for, like visual processing or motor control in novel scenarios or other things like that, that I think dogs are pretty good at.

Rohin Shah: Makes sense. So I think in that case, plausibly, dog-level AI already poses an existential risk. I can believe that too.

Buck Shlegeris: Yeah.

Rohin Shah: The AI cashier example feels like it could totally happen probably before a dog-level AI. You’ve got all of the motivation problems already at that point of the game, and I don’t know what problems you expect to see beyond then.

Buck Shlegeris: I’m more talking about whether you can test your solutions. I’m not quite sure how to say my intuitions here. I feel like there are various strategies which work for corralling dogs and which don’t work for making humans do what you want. In as much as your alignment strategy is aiming at a flavor of problem that only occurs when you have superhuman things, you don’t get to test that either way. I don’t think this is a super important point unless you think it is. I guess I feel good about moving on from here.

Rohin Shah: Mm-hmm (affirmative). Sounds good to me.

Lucas Perry: Okay, we’ve talked about what you guys have called gradual and fast takeoff scenarios, or continuous and discontinuous. Could you guys put some probabilities down on the likelihood of, and stories that you have in your head, for fast and slow takeoff scenarios?

Rohin Shah: That is a hard question. There are two sorts of reasoning I do about probabilities. One is: use my internal simulation of whatever I’m trying to predict, internally simulate what it looks like, whether it’s by my own models, is it likely? How likely is it? At what point would I be willing to bet on it. Stuff like that. And then there’s a separate extra step where I’m like, “What do other people think about this? Oh, a lot of people think this thing that I assigned one percent probability to is very likely. Hmm, I should probably not be saying one percent then.” I don’t know how to do that second part for, well, most things but especially in this setting. So I’m going to just report Rohin’s model only, which will predictably be understating the probability for fast takeoff in that if someone from MIRI were to talk to me for five hours, I would probably say a higher number for the probability of fast takeoff after that, and I know that that’s going to happen. I’m just going to ignore that fact and report my own model anyway.

On my own model, it’s something like in worlds where AGI happens soon, like in the next couple of decades, then I’m like, “Man, 95% on gradual take off.” If it’s further away, like three to five decades, then I’m like, “Some things could have changed by then, maybe I’m 80%.” And then if it’s way off into the future and centuries, then I’m like, “Ah, maybe it’s 70%, 65%.” The reason it goes down over time is just because it seems to me like if you want to argue for discontinuous takeoff, you need to posit that there’s some paradigm change in how AI progress is happening and that seems more likely the further in the future you go.

Buck Shlegeris: I feel kind of surprised that you get so low, like to 65% or 70%. I would have thought that those arguments are a strong default and then maybe at the moment where in a position that seems particularly gradual takeoff-y, but I would have thought that you over time get to 80% or something.

Rohin Shah: Yeah. Maybe my internal model is like, “Holy shit, why do these MIRI people keep saying that discontinuous takeoff is so obvious.” I agree that the arguments in Paul’s posts feel very compelling to me and so maybe I should just be more confident in them. I think saying 80%, even in centuries is plausibly a correct answer.

Lucas Perry: So, Rohin, is the view here that since compute is the thing that’s being leveraged to make most AI advances that you would expect that to be the mechanism by which that continues to happen in the future and we have some certainty over how compute continues to change into the future? Whereas things that would be leading to a discontinuous takeoff would be world-shattering, fundamental insights into algorithms that would have powerful recursive self-improvement, which is something you wouldn’t necessarily see if we just keep going this leveraging compute route?

Rohin Shah: Yeah, I think that’s a pretty good summary. Again, on the backdrop of the default argument for this is people are really trying to build AGI. It would be pretty surprising if there is just this really important thing that everyone had just missed.

Buck Shlegeris: It sure seems like in machine learning when I look at the things which have happened over the last 20 years, all of them feel like the ideas are kind of obvious or someone else had proposed them 20 years earlier. ConvNets were proposed 20 years before they were good on ImageNet, and LSTMs were ages before they were good for natural language, and so on and so on and so on. Other subjects are not like this, like in physics sometimes they just messed around for 50 years before they knew what was happening. I don’t know, I feel confused how to feel about the fact that in some subjects, it feels like they just do suddenly get better at things for reasons other than having more compute.

Rohin Shah: I think physics, at least, was often bottlenecked by measurements, I want to say.

Buck Shlegeris: Yes, so this is one reason I’ve been interested in history of science recently, but there are certainly a bunch of things. People were interested in chemistry for a long time and it turns out that chemistry comes from quantum mechanics and you could, theoretically, have guessed quantum mechanics 70 years earlier than people did if you were smart enough. It’s not that complicated a hypothesis to think of. Or relativity is the classic example of something which could have been invented 50 years earlier. I don’t know, I would love to learn more about this.

Lucas Perry: Just to tie this back to the question, could you give your probabilities as well?

Buck Shlegeris: Oh, geez, I don’t know. Honestly, right now I feel like I’m 70% gradual takeoff or something, but I don’t know. I might change my mind if I think about this for another hour. And there’s also theoretical arguments as well for why most takeoffs are gradual, like the stuff in Paul’s post. The easiest summary is, before someone does something really well, someone else does it kind of well in cases where a lot of people are trying to do the thing.

Lucas Perry: Okay. One facet of this, that I haven’t heard discussed, is recursive self-improvement, and I’m confused about where that becomes the thing that affects whether it’s discontinuous or continuous. If someone does something kind of well before something does something really well, if recursive self-improvement is a property of the thing being done kind of well, is it just kind of self-improving really quickly, or?

Buck Shlegeris: Yeah. I think Paul’s post does a great job of talking about this exact argument. I think his basic claim is, which I find pretty plausible, before you have a system which is really good at self-improving, you have a system which is kind of good at self-improving, if it turns out to be really helpful to have a system be good at self-improving. And as soon as this is true, you have to posit an additional discontinuity.

Rohin Shah: One other thing I’d note is that humans are totally self improving. Productivity techniques, for example, are a form of self-improvement. You could imagine that AI systems might have advantages that humans don’t, like being able to read their own weights and edit them directly. How much of an advantage this gives to the AI system, unclear. Still, I think then I just go back to the argument that Buck already made, which is at some point you get to an AI system that is somewhat good at understanding its weights and figuring out how to edit them, and that happens before you get the really powerful ones. Maybe this is like saying, “Well, you’ll reach human levels of self-improvement by the time you have rat-level AI or something instead of human-level AI,” which argues that you’ll hit this hyperbolic point of the curve earlier, but it still looks like a hyperbolic curve that’s still continuous at every point.

Buck Shlegeris: I agree.

Lucas Perry: I feel just generally surprised about your probabilities on continuous takeoff scenarios that they’d be slow.

Rohin Shah: The reason I’m trying to avoid the word slow and fast is because they’re misleading. Slow takeoff is not slow in calendar time relative to fast takeoff. The question is, is there a spike at some point? Some people, upon reading Paul’s posts are like, “Slow takeoff is faster than fast takeoff.” That’s a reasonably common reaction to it.

Buck Shlegeris: I would put it as slow takeoff is the claim that things are insane before you have the human-level AI.

Rohin Shah: Yeah.

Lucas Perry: This seems like a helpful perspective shift on this takeoff scenario question. I have not read Paul’s post. What is it called so that we can include it in the page for this podcast?

Rohin Shah: It’s just called Takeoff Speeds. Then the corresponding AI Impacts post is called Will AI See Discontinuous Progress?, I believe.

Lucas Perry: So if each of you guys had a lot more reach and influence and power and resources to bring to the AI alignment problem right now, what would you do?

Rohin Shah: I get this question a lot and my response is always, “Man, I don’t know.” It seems hard to scalably use people right now for AI risk. I can talk about which areas of research I’d like to see more people focus on. If you gave me people where I’m like, “I trust your judgment on your ability to do good conceptual work” or something, where would I put them? I think a lot of it would be on making good robust arguments for AI risk. I don’t think we really have them, which seems like kind of a bad situation to be in. I think I would also invest a lot more in having good introductory materials, like this review, except this review is a little more aimed at people who are already in the field. It is less aimed at people who are trying to enter the field. I think we just have pretty terrible resources for people coming into the field and that should change.

Buck Shlegeris: I think that our resources are way better than they used to be.

Rohin Shah: That seems true.

Buck Shlegeris: In the course of my work, I talk to a lot of people who are new to AI alignment about it and I would say that their level of informedness is drastically better now than it was two years ago. A lot of which is due to things like 80,000 hours podcast, and other things like this podcast and the Alignment Newsletter, and so on. I think we just have made it somewhat easier for people to get into everything. The Alignment Forum, having its sequences prominently displayed, and so on.

Rohin Shah: Yeah, you named literally all of the things I would have named. Buck definitely has more information on this than I do. I do not work with people who are entering the field as much. I do think we could be substantially better.

Buck Shlegeris: Yes. I feel like I do have access to resources, not directly but in the sense that I know people at eg Open Philanthropy and the EA Funds  and if I thought there were obvious things they should do, I think it’s pretty likely that those funders would have already made them happen. And I occasionally embark on projects myself that I think are good for AI alignment, mostly on the outreach side. On a few occasions over the last year, I’ve just done projects that I was optimistic about. So I don’t think I can name things that are just shovel-ready opportunities for someone else to do, which is good news because it’s mostly because I think most of these things are already being done.

I am enthusiastic about workshops. I help run with MIRI these AI Risks for Computer Scientists workshops and I ran my own computing workshop with some friends, with kind of a similar purpose, aimed at people who are interested in this kind of stuff and who would like to spend some time learning more about it. I feel optimistic about this kind of project as a way of doing the thing Rohin was saying, making it easier for people to start having really deep thoughts about a lot of AI alignment stuff. So that’s a kind of direction of projects that I’m pretty enthusiastic about. A couple other random AI alignment things I’m optimistic about. I’ve already mentioned that I think there should be an Ought competitor just because it seems like the kind of thing that more work could go into. I agree with Rohin on it being good to have more conceptual analysis of a bunch of this stuff. I’m generically enthusiastic about there being more high quality research done and more smart people, who’ve thought about this a lot, working on it as best as they can.

Rohin Shah: I think the actual bottleneck is good research and not necessarily field building, and I’m more optimistic about good research. Specifically, I am particularly interested in universality, interpretability. I would love for there to be some way to give people who work on AI alignment the chance to step back and think about the high-level picture for a while. I don’t know if people don’t do this because they don’t want to or because they don’t feel like they have the affordance to do so, and I would like the affordance to be there. I’d be very interested in people building models of what AGI systems could look like. Expected utility maximizers are one example of a model that you could have. Maybe we just try to redo evolution. We just create a very complicated, diverse environment with lots of agents going around and in their multi-agent interaction, they develop general intelligence somehow. I’d be interested for someone to take that scenario, flesh it out more, and then talk about what the alignment problem looks like in that setting.

Buck Shlegeris: I would love to have someone get really knowledgeable about evolutionary biology and try and apply analogies of that to AI alignment. I think that evolutionary biology has lots of smart things to say about what optimizers are and it’d be great to have those insights. I think Eliezer sort of did this many years ago. It would be good for more people to do this in my opinion.

Lucas Perry: All right. We’re in the home stretch here. AI timelines. What do you think about the current state of predictions? There’s been surveys that have been done with people giving maybe 50% probability over most researchers at about 2050 or so. What are each of your AI timelines? What’s your probability distribution look like? What do you think about the state of predictions on this?

Rohin Shah: Haven’t looked at the state of predictions in a while. It depends on who was surveyed. I think most people haven’t thought about it very much and I don’t know if I expect their predictions to be that good, but maybe wisdom of the crowds is a real thing. I don’t think about it very much. I mostly use my inside view and talk to a bunch of people. Maybe, median, 30 years from now, which is 2050. So I guess I agree with them, don’t I? That feels like an accident. The surveys were not an input into this process.

Lucas Perry: Okay, Buck?

Buck Shlegeris: I don’t know what I think my overall timelines are. I think AI in the next 10 or 20 years is pretty plausible. Maybe I want to give it something around 50% which puts my median at around 2040. In terms of the state of things that people have said about AI timelines, I have had some really great conversations with people about their research on AI timelines which hasn’t been published yet. But at some point in the next year, I think it’s pretty likely that much better stuff about AI timelines modeling will have been published than has currently been published, so I’m excited for that.

Lucas Perry: All right. Information hazards. Originally, there seemed to be a lot of worry in the community about information hazards and even talking about superintelligence and being afraid of talking to anyone in positions of power, whether they be in private institutions or in government, about the strategic advantage of AI, about how one day it may confer a decisive strategic advantage. The dissonance here for me is that Putin comes out and says that who controls AI will control the world. Nick Bostrom published Superintelligence, which basically says what I already said. Max Tegmark’s Life 3.0 basically also. My initial reaction and intuition is the cat’s out of the bag. I don’t think that echoing this increases risks any further than the risk is already at. But maybe you disagree.

Buck Shlegeris: Yeah. So here are two opinions I have about info hazards. One is: how bad is it to say stuff like that all over the internet? My guess is it’s mildly bad because I think that not everyone thinks those things. I think that even if you could get those opinions as consequences from reading Superintelligence, I think that most people in fact have not read Superintelligence. Sometimes there are ideas where I just really don’t want them to be crystallized common knowledge. I think that, to a large extent, assuming gradual takeoff worlds, it kind of doesn’t matter because AI systems are going to be radically transforming the world inevitably. I guess you can affect how governments think about it, but it’s a bit different there.

The other point I want to make about info hazards is I think there are a bunch of trickinesses with AI safety, where thinking about AI safety makes you think about questions about how AI development might go. I think that thinking about how AI development is going to go occasionally leads to think about things that are maybe, could be, relevant to capabilities, and I think that this makes it hard to do research because you then get scared about talking about them.

Rohin Shah: So I think my take on this is info hazards are real in the sense that there, in fact, are costs to saying specific kinds of information and publicizing them a bit. I think I’ll agree in principle that some kinds of capabilities information has the cost of accelerating timelines. I usually think these are pretty strongly outweighed by the benefits in that it just seems really hard to be able to do any kind of shared intellectual work when you’re constantly worried about what you do and don’t make public. It really seems like if you really want to build a shared understanding within the field of AI alignment, that benefit is worth saying things that might be bad in some other ways. This depends on a lot of background facts that I’m not going to cover here but, for example, I probably wouldn’t say the same thing about bio security.

Lucas Perry: Okay. That makes sense. Thanks for your opinions on this. So at the current state in time, do you guys think that people should be engaging with people in government or in policy spheres on questions of AI alignment?

Rohin Shah: Yes, but not in the sense of we’re worried about when AGI comes. Even saying things like it might be really bad, as opposed to saying it might kill everybody, seems not great. Mostly on the basis of my model for what it takes to get governments to do things is, at the very least, you need consensus in the field so it seems kind of pointless to try right now. It might even be poisoning the well for future efforts. I think it does make sense to engage with government and policymakers about things that are in fact problems right now. To the extent that you think that recommender systems are causing a lot of problems, I think it makes sense to engage with government about how alignment-like techniques can help with that, especially if you’re doing a bunch of specification learning-type stuff. That seems like the sort of stuff that should have relevance today and I think it would be great if those of us who did specification learning were trying to use it to improve existing systems.

Buck Shlegeris: This isn’t my field. I trust the judgment of a lot of other people. I think that it’s plausible that it’s worth building relationships with governments now, not that I know what I’m talking about. I will note that I basically have only seen people talk about how to do AI governance in the cases where the AI safety problem is 90th percentile easiest. I basically only see people talking about it in the case where the technical safety problem is pretty doable, and this concerns me. I’ve just never seen anyone talk about what you do in a world where you’re as pessimistic as I am, except to completely give up.

Lucas Perry: All right. Wrapping up here, is there anything else that we didn’t talk about that you guys think was important? Or something that we weren’t able to spend enough time on, that you would’ve liked to spend more time on?

Rohin Shah: I do want to eventually continue the conversation with Buck about coordination, but that does seem like it should happen not on this podcast.

Buck Shlegeris: That’s what I was going to say too. Something that I want someone to do is write a trajectory for how AI goes down, that is really specific about what the world GDP is in every one of the years from now until insane intelligence explosion. And just write down what the world is like in each of those years because I don’t know how to write an internally consistent, plausible trajectory. I don’t know how to write even one of those for anything except a ridiculously fast takeoff. And this feels like a real shame.

Rohin Shah: That seems good to me as well. And also the sort of thing that I could not do because I don’t know economics.

Lucas Perry: All right, so let’s wrap up here then. So if listeners are interested in following either of you or seeing more of your blog posts or places where you would recommend they read more materials on AI alignment, where can they do that? We’ll start with you, Buck.

Buck Shlegeris: You can Google me and find my website. I often post things on the Effective Altruism Forum. If you want to talk to me about AI alignment in person, perhaps you should apply to the AI Risks for Computer Scientists workshops run by MIRI.

Lucas Perry: And Rohin?

Rohin Shah: I write the Alignment Newsletter. That’s a thing that you could sign up for. Also on my website, if you Google Rohin Shah Alignment Newsletter, I’m sure I will come up. These are also cross posted to the Alignment Forum, so another thing you can do is go to the Alignment Forum, look up my username and just see things that are there. I don’t know that this is actually the thing that you want to be doing. If you’re new to AI safety and want to learn more about it, I would echo the resources Buck mentioned earlier, which are the 80k podcasts about AI alignment. There are probably on the order of five of these. There’s the Alignment Newsletter. There are the three recommended sequences on the Alignment Forum. Just go to alignmentforum.org and look under recommended sequences. And this podcast, of course.

Lucas Perry: All right. Heroic job, everyone. This is going to be a really good resource, I think. It’s given me a lot of perspective on how thinking has changed over the past year or two.

Buck Shlegeris: And we can listen to it again in a year and see how dumb we are.

Lucas Perry: Yeah. There were lots of predictions and probabilities given today, so it’ll be interesting to see how things are in a year or two from now. That’ll be great. All right, so cool. Thank you both so much for coming on.

End of recorded material

FLI Podcast: Lessons from COVID-19 with Emilia Javorsky and Anthony Aguirre

The global spread of COVID-19 has put tremendous stress on humanity’s social, political, and economic systems. The breakdowns triggered by this sudden stress indicate areas where national and global systems are fragile, and where preventative and preparedness measures may be insufficient. The COVID-19 pandemic thus serves as an opportunity for reflecting on the strengths and weaknesses of human civilization and what we can do to help make humanity more resilient. The Future of Life Institute’s Emilia Javorsky and Anthony Aguirre join us on this special episode of the FLI Podcast to explore the lessons that might be learned from COVID-19 and the perspective this gives us for global catastrophic and existential risk.

Topics discussed in this episode include:

  • The importance of taking expected value calculations seriously
  • The need for making accurate predictions
  • The difficulty of taking probabilities seriously
  • Human psychological bias around estimating and acting on risk
  • The massive online prediction solicitation and aggregation engine, Metaculus
  • The risks and benefits of synthetic biology in the 21st Century

Timestamps: 

0:00 Intro 

2:35 How has COVID-19 demonstrated weakness in human systems and risk preparedness 

4:50 The importance of expected value calculations and considering risks over timescales 

10:50 The importance of being able to make accurate predictions 

14:15 The difficulty of trusting probabilities and acting on low probability high cost risks

21:22 Taking expected value calculations seriously 

24:03 The lack of transparency, explanation, and context around how probabilities are estimated and shared

28:00 Diffusion of responsibility and other human psychological weaknesses in thinking about risk

38:19 What Metaculus is and its relevance to COVID-19 

45:57 What is the accuracy of predictions on Metaculus and what has it said about COVID-19?

50:31 Lessons for existential risk from COVID-19 

58:42 The risk of synthetic bio enabled pandemics in the 21st century 

01:17:35 The extent to which COVID-19 poses challenges to democratic institutions

 

This podcast is possible because of the support of listeners like you. If you found this conversation to be meaningful or valuable consider supporting it directly by donating at futureoflife.org/donate. Contributions like yours make these conversations possible.

All of our podcasts are also now on Spotify and iHeartRadio! Or find us on SoundCloudiTunesGoogle Play and Stitcher.

You can listen to the podcast above or read the transcript below. 

Lucas Perry: Welcome to the Future of Life Institute Podcast. I’m Lucas Perry. Today’s episode is a special focused on lessons from COVID-19 with two members of the Future of Life Institute team, Anthony Aguirre and Emilia Javorsky. The ongoing coronavirus pandemic has helped to illustrate the frailty of human systems, the difficulty of international coordination on global issues and our general underpreparedness for risk. This podcast is focused on what COVID-19 can teach us about being better prepared for future risk from the perspective of global catastrophic and existential risk. The AI Alignment Podcast and the end of the month Future of Life Institute podcast will release as normally scheduled. 

Anthony Aguirre has been on the podcast recently to discuss the ultimate nature of reality and problems of identity. He is a physicist that studies the formation, nature, and evolution of the universe, focusing primarily on the model of eternal inflation—the idea that inflation goes on forever in some regions of universe—and what it may mean for the ultimate beginning of the universe and time. He is the co-founder and Associate Scientific Director of the Foundational Questions Institute and is also a Co-Founder of the Future of Life Institute. He also co-founded Metaculus, which is something we get into during the podcast, which is an effort to optimally aggregate predictions about scientific discoveries, technological breakthroughs, and other interesting issues.

Emilia Javorsky develops tools to improve human health and wellbeing and has a background in healthcare and research. She leads clinical research and work on translation of science from academia to commercial setting at Artic Fox, and is the Chief Scientific Officer and Co-Founder of Sundaily, as well as the Director of Scientists Against Inhumane Weapons. Emilia is an advocate for the safe and ethical deployment of technology, and is currently heavily focused on lethal autonomous weapons issues.  

And with that, let’s get into our conversation with Anthony and Emilia on COVID-19. 

We’re here to try and get some perspective on COVID-19 for how it is both informative surrounding issues regarding global catastrophic and existential risk and to see ways in which we can learn from this catastrophe and how it can inform existential risk and global catastrophic thought. Just to start off then, what are ways in which COVID-19 has helped demonstrate weaknesses in human systems and preparedness for risk?

Anthony Aguirre: One of the most upsetting things I think to many people is how predictable it was and how preventable it was with sufficient care taken as a result of those predictions. It’s been known by epidemiologists for decades that this sort of thing was not only possible, but likely given enough time going by. We had SARS and MERS as kind of dry runs that almost were pandemics, but didn’t have quite the right characteristics. Everybody in the community of people thinking hard about this, and I would like to hear more of Emilia’s perspective on this knew that something like this was coming eventually. That it might be a few percent probable each year, but after 10 or 20 or 30 years, you start to get large probability of something like this happening. So it was known that it was coming eventually and pretty well known what needed to happen to be well prepared for it.

And yet nonetheless, many countries have found themselves totally unprepared or largely unprepared and unclear on what exactly to do and making very poor decisions in response to things that they should be making high quality decisions on. So I think part of what I’m interested in doing is thinking about why has that happened, even though we scientifically understand what’s going on? We numerically model what could happen, we know many of the things that should happen in response. Nonetheless, as a civilization, we’re kind of being caught off guard in a way and making a bad situation much, much worse. So why is that happening and how can we do it better now and next time?

Lucas Perry: So in short, the ways in which this is frustrating is that it was very predictable and was likely to happen given computational models and then also, lived experience given historical cases like SARS and MERS.

Anthony Aguirre: Right. This was not some crazy thing out of the blue, this was just a slightly worse version of things that have happened before. Part of the problem, in my mind, is the sort of mismatch between the likely cost of something like this and how many resources society is willing to put into planning and preparing and preventing it. And so here, I think a really important concept is expected value. So, the basic idea that when you’re calculating the value of something that is unsure that you want to think about different probabilities for different values that that thing might have and combine them.

So for example, if I’m thinking I’m going to spend some money on something and there’s a 50% chance that it’s going to cost a dollar and there’s a 50% chance that it’s going to cost $1,000, so how much should I expect to pay for it? So on one hand, I don’t know, it’s a 50/50 chance, it could be a dollar, it could be $1,000, but if I think I’m going to do this over and over again, you can ask how much am I going to pay on average? And that’s about 50% of a dollar plus 50% of $1,000 so about $500, $500 and 50 cents. The idea of thinking in terms of expected value is that when I have probabilities for something, I should always think as if I’m going to do this thing many, many, many times, like I’m going to roll the dice many, many times and I should reason in a way that makes sense if I’m going to do it a lot of times. So I’d want to expect that I’m going to spend something like $500 on this thing, even though that’s not either of the two possibilities.

So, if we’re thinking about a pandemic, if you imagine the cost just in dollars, let alone all the other things that are going to happen, but just purely in terms of dollars, we’re talking about trillions of dollars. So if this was something that is going to cost trillions and trillions of dollars and there was something like a 10% chance of this happening over a period of a decade say, we should have been willing to pay hundreds and hundreds of billions of dollars to prevent this from happening or to dramatically decrease the cost when it does happen. And that is way, way, way orders of magnitude, more money than we have in fact spent on that.

So, part of the tricky thing is that people don’t generally think in these terms, they think of “What is the most likely thing?” And then they plan for that. But if the most likely thing is relatively cheap and a fairly unlikely thing is incredibly expensive, people don’t like to think about the incredibly expensive, unlikely thing, right? They think, “That’s scary. I don’t want to think about it. I’m going to think about the likely thing that’s cheap.” But of course, that’s terrible planning. You should put some amount of resources into planning for the unlikely incredibly expensive thing.

And it’s often, and it is in this case, that even a small fraction of the expected cost of this thing could have prevented the whole thing from happening in the sense that there’s going to be trillions and trillions of dollars of costs. It was anticipated at 10% likely, so it’s hundreds of billions of dollars that in principle society should have been willing to pay to prevent it from happening, but even a small fraction of that, in fact, could have really, really mitigated the problem. So it’s not even that we actually have to spend exactly the amount of money that we think we will lose in order to prevent something from happening.

Even a small fraction would have done. The problem is that we spend not even close to that. These sorts of situations where there’s a small probability of something extraordinarily costly happening, our reaction in society tends to be to just say, “It’s a small probability, so I don’t want to think about it.” Rather than “It’s a small probability, but the cost is huge, so I should be willing to pay some fraction of that small probability times that huge cost to prevent it from happening.” And I think if we could have that sort of calculation in mind a little bit more firmly, then we could prevent a lot of terrible things from happening at a relatively modest investment. But the tricky thing is that it’s very hard to take seriously those small probability, high cost things without really having a firm idea of what they are, what the probability of that happening is and what the cost will be.

Emilia Javorsky: I would add to that, but in complete agreement with Anthony, part of what is at issue here too is needing to think overtime scales, because if something has a certain probability that is small at any given short term horizon, but that probability rises to something that’s more significant with a tremendously high cost over a longer term time scale, you need to be able to be willing to think on those longer term timescales in order to act. And from the perspective of medicine, this is something we’ve struggled with a lot, at both the individual level, at the healthcare system level and at the societal public health policy level, is that prevention, while we know it’s much cheaper to prevent a disease than to treat it, the same thing with pandemic preparedness, a lot of the things we’re talking about were actually quite cheap mitigation measures to put in place. Right now, we’re seeing a crisis of personal protective equipment.

We’re talking about basic cheap supplies like gloves and masks and then national stockpiles of ventilators. These are very basic, very conserved across any pandemic type, right? We know that in all likelihood when a pandemic arises, it is some sort of respiratory borne illness. Things like masks and respirators are a very wise thing to stockpile and have on hand. Yet despite having several near misses, even in the very recent past, we’re talking about the past 20 years, there was not a critical will or a critical lobby or a critical voice that enabled us to do these very basic, relatively cheap measures to be prepared for something like this to happen.

If you talk about something like vaccine development, that’s something that you need to prepare pretty much in real time. That’s pathogen specific, but the places that were fumbling to manage this epidemic today are things that were totally basic, cheap and foreseeable. We really need to find ways in the here and now to motivate thinking on any sort of longterm horizon. Not even 50 years, a hundred years down the line, but one to five years are things that we struggle with.

Anthony Aguirre: To me, another surprising thing has been the sudden discovery of how important it is to be able to predict things. It’s of course, always super important. This is what we do throughout our life. We’re basically constantly predicting things, predicting the consequences of certain actions or choices we might make, and then making those choices dependent on which things we want to have happen. So we’re doing it all the time and yet when confronted with this pandemic, suddenly, we extra super realize how important it is to have good predictions, because what’s unusual I would say about a situation like this is that all of the danger is sort of in the future. If you look at it in any given time, you say, “Oh, there’s a couple of dozen cases here in my county, everything’s under control.” Unbelievably ineffective and wishful thinking, because of course, the number of cases is growing exponentially and by the time you notice that there’s any problem that’s of significance at all, the next day or the next few days, it’s going to be doubly as big.

So the fact that things are happening exponentially in a pandemic or an epidemic, makes it incredibly vital that you have the ability to think about what’s going to happen in the future and how bad things can get quite quickly, even if at the moment, everything seems fine. Everybody who thinks in this field or who just is comfortable with how exponentials work know this intellectually, but it still isn’t always easy to get the intuitive feeling for that, because it just seems like so not a big deal for so long, until suddenly it’s the biggest thing in the world.

This has been a particularly salient lesson that we really need to understand both exponential growth and how to do good projections and predictions about things, because there could be lots of things that are happening under the radar. Beyond the pandemic, there are lots of things that are exponentially growing that if we don’t pay attention to the people who are pointing out those exponentially growing things and just wait until they’re a problem, then it’s too late to do anything about the problem.

At the beginning stages, it’s quite easy to deal with. If we take ourselves back to sometime in late December, early January or something, there was a time where this pandemic could have easily been totally prevented by the actions of the few people, if they had just known exactly what the right things to do were. I don’t think you can totally blame people for that. It’s very hard to see what it would turn into, but there is a time at the beginning of the exponential where action is just so much easier and every little bit of delay just makes it incredibly harder to do anything about it. It really brings home how important it is to have good predictions about things and how important it is to believe those predictions if you can and take decisive action early on to prevent exponentially growing things from really coming to bite you.

Lucas Perry: I see a few central issues here and lessons from COVID-19 that we can draw on. The first is that this is something that was predictable and was foreseeable and that experts were saying had a high likelihood of happening, and the ways in which we failed were either in the global system, there aren’t the kinds of incentives for private organizations or institutions to work towards mitigating these kinds of risks or people just aren’t willing to listen to experts making these kinds of predictions. The second thing seems to be that even when we do have these kinds of predictions, we don’t know how basic decision theory works and we’re not able to feel and intuit the reality of exponential growth sufficiently well. So what are very succinct ways of putting solutions to these problems?

Anthony Aguirre: The really hard part is having probabilities that you feel like you can trust. If you go to a policy maker and tell them there’s a danger of this thing happening, maybe it’s a natural pandemic, maybe it’s a human engineered pandemic or a AI powered cyber attack, something that if it happens, is incredibly costly to society and you say, “I really think we should be devoting some resources to preventing this from happening, because I think there’s a 10% chance that this is going to happen in the next 10 years.” They’re going to ask you, “Where does that 10% chance come from?” And “Are you sure that it’s not a 1% chance or a 0.1% chance or a .00001% chance?” And that makes a huge difference, right? If something really is a tiny, tiny fraction of a percent likely, then that plays directly into how much effort you should go in to preventing it if it has some fixed cost.

So I think the reaction that people have often to low probability, high cost things is to doubt exactly what the probability is and having that doubt in their mind, just avoid thinking about the issue at all, because it’s so easy to not think about it if the probability is really small. A big part of it is really understanding what the probabilities are and taking them seriously. And that’s a hard thing to do, because it’s really, really hard to estimate what the probabilities say of a gigantic AI powered cyber attack is, where do you even start with that? It has all kinds of ingredients that there’s no model for, there’s no set quantitative assessment strategy for it. That’s a part of the root of the conundrum that even for things like this pandemic that everybody knew was coming at some level, I would say nobody knew whether it was a 5% chance over 10 years or a 50% chance over 10 years.

It’s very hard to get firm numbers, so one thing I think we need are better ways of assessing probabilities of different sorts of low probability, high cost things. That’s something I’ve been working a lot on over the past few years in the form of Metaculus which maybe we can talk about, but I think in general, most people and policy makers can understand that if there’s some even relatively low chance of a hugely costly thing that we should do some planning for it. We do that all the time, we do it with insurance, we do it with planning for wars. There are all kinds of low probability things that we plan for, but if you can’t tell people what the probability is and it’s small and the thing is weird, then it’s very, very hard to get traction.

Emilia Javorsky: Part of this is how do we find the right people to make the right predictions and have the ingredients to model those out? But the other side of this is how do we get the policy makers and decision makers and leaders in society to listen to those predictions and to have trust and confidence in them? From the perspective of that, when you’re communicating something that is counterintuitive, which is how many people end up making decisions, there really has to be a foundation of trust there, where you’re telling me something that is counterintuitive to how I would think about decision making and planning in this particular problem space. And so, it has to be built on a foundation and trust. And I think one of the things that characterize good models and good predictions is exactly as you say, they’re communicated with a lot of trepidation.

They explain what the different variables are that go into them and the uncertainty that bounds each of those variables and an acknowledgement that some things are known and unknown. And I think that’s very hard in today’s world where information is always at maximum volume and it’s very polarized and you’re competing against voices, whether they be in a policy maker’s ear or a CEO’s ear, that will speak in absolutes and speak in levels of certainty, overestimating risk, or underestimating risk.

That is the element that is necessary for these predictions to have impact is how do you connect ambiguous and qualified and cautious language that characterizes these kind of long term predictions with a foundation of trust so people can hear and appreciate those and you don’t get drowned out by the noise on either side of things that are much likely to be less well founded if they’re speaking in absolutes and problem spaces that we know just have a tremendous amount of uncertainty.

Anthony Aguirre: That’s a very good point. You’re mentioning of the kind of unfamiliarity with these things is an important one in the sense that, as an individual, I can think of improbable things that might happen to me and they seem, well, that’s probably not going to happen to me, but I know intellectually it will and I can look around the world and see that that improbable thing is happening to lots of people all the time. Even if there’s kind of a psychological barrier to my believing that it might happen to me, I can’t deny that it’s a thing and I can’t really deny what sort of probability it might have to happen to me, because I see it happening all around. Whereas when we’re talking about things that are happening to a country or a civilization, we don’t have a whole lot of statistics on them.

We can’t just say of all the different planets that are out there with civilizations like ours, 3% of them are undergoing pandemics right now. If we could do that then we could really count on those probabilities. We can’t do that. We can look historically at what happened in our world, but of course, since it’s really changing dramatically over the years, that’s not always such a great guide and so, we’re left with reasoning by putting together scientific models, all the uncertainties that you were mentioning that we have to feed into those sorts of models or just other ways of making predictions about things through various means and trying to figure out how can we have good confidence in those predictions. And this is an important point that you bring up, not so much in terms of certainty, because there are all of these complex things that we’re trying to predict about the possibility of good or bad things happening to our society as a whole, none of them can be predicted with certainty.

I mean, almost nothing in the world can be predicted with certainty, certainly not these things, and so it’s always a question of giving probabilities for things and both being confident in those probabilities and taking seriously what those probabilities mean. And as you say, people don’t like that. They want to be told what is going to happen or what isn’t going to happen and make a decision on that basis. That is unfortunately not information that’s available on most important things and so, we’d have to accept that they’re going to be probabilities, but then where do we them from? How do we use them? There’s a science and an art to that I think, and a subtlety to it as you say, that we really have to get used to and get comfortable with.

Lucas Perry: There seems to be lots of psychological biases and problems around human beings understanding and fully integrating probabilistic estimations into our lives and decision making. I’m sure there’s probably literature that already exists upon this, but it would be skillful I think to apply it to existential and global catastrophic risk. So, assuming that we’re able to sufficiently develop our ability to generate accurate and well-reasoned probabilistic estimations of risks, and Anthony, we’ll get into Metaculus shortly, then you mentioned that the prudent and skillful thing to do would be to feed that into a proper decision theory, which explain a little bit more about the nerdy side of that if you feel it would be useful, and in particular, you talked a little bit about expected value, could you say a little bit more about how if policy and government officials were able to get accurate probabilistic reasoning and then fed it into the correct decision theoretic models that it would produce better risk mitigation efforts?

Anthony Aguirre: I mean, there’s all kinds of complicated discussions and philosophical explorations of different versions of decision theory. We really don’t need to think about things in such complicated terms in the sense that what it really is about is just taking expected values seriously and thinking about actions we might take based on how much value we expect given each decision. When you’re gambling, this is exactly what you’re doing, you might say, “Here, I’ve got some cards in my hand. If I draw, there’s a 10% chance that I’ll get nothing and a 20% chance that I’ll get a pair and a tiny percent chance that I’ll fill out my flush or something.” And with each of those things, I want to think of, “What is the probable payoff when I have that given outcome?” And I want to make my decisions based on the expected value of things rather than just what is the most probable or something like that.

So it’s a willingness to quantitatively take into account, if I make decision A, here is the likely payoff of making decision A, if I make decision B, here’s the likely payoff that is the expected value of my payoff in decision B, looking at which one of those is higher and making that decision. So it’s not very complicated in that sense. There are all kinds of subtleties, but in practice it can be very complicated because usually you don’t know, if I make decision A, what’s going to happen? If I make decision B, what’s going to happen? And exactly what value can I associate with those things? But this is what we do all the time, when we weigh the pros and cons of things, we’re kind of thinking, “Well, if I do this, here are the things that I think are likely to happen. Here’s what I think I’m going to feel and experience and maybe gain in doing A, let me think through the same thing in my mind with B and then, which one of those feels better is the one that I do.”

So, this is what we do all the time on an intuitive level, but we can do quantitative and systematic method of it. If we are more carefully thinking about what the actual numerical and quantitative implications of something are and if we have actual probabilities that we can assign to the different outcomes in order to make our decision. All of this, I think, is quite well known to decision makers of all sorts. What’s hard is that often decision makers won’t really have those sorts of tools in front of them. They won’t have ability to look at different possibilities, ability to attribute probabilities and costs and payoffs to those things in order to make good decisions. So those are tools that we could put in people’s hands and I think would just allow people to make better decisions.

Emilia Javorsky: And what I like about what you’re saying, Anthony, implicit in that is that it’s a standardized tool. The way you assign the probabilities and decide between different optionalities is standardized. And I think one thing that can be difficult in the policy space is different advocacy groups or different stakeholders will present data and assign probabilities based on different assumptions and vested interests, right? So, when a policy maker is making a decision, they’re using probabilities and using estimates and outcomes that are developed using completely different models with completely different assumptions and different biases baked into them and different interests baked into them. What I think is so vital is to make sure as best one can, again knowing the inherent ambiguity that’s existing in modeling in general, that you’re having an apples to apples comparison when you’re assigning different probabilities and making decisions based off of them.

Anthony Aguirre: Yeah, that’s a great point that part of the problem is that people are just used to probabilities not meaning anything because they’re often given without context, without explanation and by groups that have a vested interest in them looking a certain way. If I ask someone, what’s the probability that this thing is going to happen, and they’d tell me 17%, I don’t know what to do with that. Do I believe them? I mean, on what basis are they telling me 17%? In order for me to believe that, I have to either have an understanding of what exactly went into that 17% and really agree step-by-step with all their assumptions and modeling and so on, or maybe I have to believe them from some other reason.

Like they’ve provided probabilities for lots of things before, and they’ve given accurate probabilities for all these different things that they provided, so I kind of trust their ability to give accurate probabilities. But usually that’s not available. That’s part of the problem. Our general lesson has been if people are giving you probabilities, usually they don’t mean much, but that’s not always the case. There are probabilities we use all the time, like for the weather where we more or less know what they mean. You see that there’s a 15% chance of rain.

That’s a meaningful thing, and it’s meaningful because both of you sort of trust that the weather people know what they’re doing, which they sort of do, and it’s meaningful in that it has a particular interpretation, which is that if I look at the weather forecast for a year and look at all the days where it said that there was a 15% chance of rain, about 15% of all those days it will have been raining. There’s a real meaning to that, and those numbers come from a careful calibration of weather models for exactly that reason. When you get 15% chance of rain from the weather forecast, what that generally means is that they’ve run a whole bunch of weather models with slightly different initial conditions and in 15% of them it’s raining today in your location.

They’re carefully calibrated usually, like the National Weather Service calibrates them, so that it really is true that if you look at all the days of, whatever, it’s 15% chance, about 15% of those days it was in fact raining. Those are probabilities that you can really use and you can say, “15% chance of rain, is it worth taking an umbrella? The umbrella is kind of annoying to carry around. Am I willing to take my chances for 15%? Yeah, maybe. If it was 30%, I’d probably take the umbrella. If it was 5%, I definitely wouldn’t.” That’s a number that you can fold into your decision theory because it means something. Whereas when somebody says, “There’s a 18% chance at this point that some political thing is going to happen, that some bill is going to pass,” maybe that’s true, but you have no idea where that 18% comes from. It’s really hard to make use of it.

Lucas Perry: Part of them proving this getting prepared for risks is better understanding and taking seriously the reasoning and reasons behind different risk estimations that experts or certain groups provide. You guys explained that there are many different vested interests or interest groups who may be biasing or framing percentages and risks in a certain way, so that policy and action can be directed towards things which may benefit them. Are there other facets to our failure to respond here other than our inability to take risks seriously?

Emilia Javorsky: If we had a sufficiently good understanding of the probabilities and we were able to see all of the reasons behind the probabilities and take them all seriously, and then we took those and we fed them into a standardized and appropriate decision theory, which used expected value calculations and some agreed upon risk tolerance to determine how much resources should be put into mitigating risks, are there other psychological biases or weaknesses in human virtue that would still lead to us insufficiently acting on these risks? An example that comes to mind maybe of something like a diffusion of responsibility.

That’s very much what COVID-19 in many ways has played out to be, right? We kind of started this with the assumptions that this was quite a foreseeable risk, and any which way you looked at the probabilities, it was a sufficiently high probability that basic levels of preparedness and a robustness of preparedness should have been employed. I think what you allude to in terms of diffusion of responsibility is certainly one aspect of it. It’s difficult to say where that decision-making fell apart, but we did hear very early on a lot of discussion of this is something that is a problem localized to China.

Anyone that has any familiarity with these models would have told you, “Based on the probabilities we already knew about, plus what we’re witnessing from this early data, which was publicly available in January, we had a pretty good idea of what was going on, that this would become something that was in all likelihood be global.” This next question becomes, why wasn’t anything done or acted on at that time? I think part of that comes with a lack of advocacy and a lack of having the ears of the key decision makers of what was actually coming. It is very, very easy when you have to make difficult decisions to listen to the vocal voices that tell you not to do something and provide reasons for inaction.

Then the voices of action are perhaps more muted coming from a scientific community, spoken in language that’s not as definitive as the other voices in the room and the other stakeholders in the room that have a vested interest in policymaking. The societal incentives to act or not act aren’t just from a pure, what’s the best long-term course of action, they’re very, very much vested in what are the loudest voices in the room, what is the kind of clout and power that they hold, and weighing those. I think there’s a very real political and social atmosphere and economic atmosphere that this happens in that dilutes some of the writing that was very clearly on the wall of what was coming.

Anthony Aguirre: I would add I think that it’s especially easy to ignore something that is predicted and quite understandable to experts who understand the dynamics of it, but unfamiliar or where historically you’ve seen it turn out the other way. Like on one hand, we had multiple warnings through near pandemics that this could happen, right? We had SARS and MERS and we had H1N1 and there was Ebola. All these things were clear indications of how possible it was for this to happen. But at the same time, you could easily take the opposite lesson, which is yes, an epidemic arises in some foreign country and people go and take care of it and it doesn’t really bother me.

You can easily take the lesson from that that the tendency of these things is to just go away on their own and the proper people will take care of them and I don’t have to worry about this. What’s tricky is understanding from the actual characteristics of the system and your understanding of the system what makes it different from those other previous examples. In this case, something that is more transmissible, transmissible when it’s not very symptomatic, yet has a relatively high fatality rate, not very high like some of these other things, which would have been catastrophic, but a couple of percent or whatever it turns out to be.

I think people who understood the dynamics of infectious disease and saw high transmissibility and potential asymptomatic transmission and a death rate that was much higher than the flu immediately put those three things together and saw, oh my god, this is a major problem and a little bit different from some of those previous ones that had a lower fatality rate or were very, very obviously symptomatic when they were transmissible, and so it was much easier to quarantine people and so on. Those characteristics you can understand if you’re trained for that sort of thing to look for it, and those people did, but if not, you just sort of see it as another far away disease in a far off land that people will take care of and it’s very easy to dismiss it.

I think it’s not really a failure of imagination, but a failure to take seriously something that could happen that is perfectly plausible just because something like it hasn’t really happened like that before. That’s a very dangerous one I think.

Emilia Javorsky: It comes back to human nature sometimes and the frailty of our biases and our virtue. It’s very easy to convince yourself and recall examples where things did not come to pass. Because dealing with the reality of the negative outcome that you’re looking at, even if it looks like it has a fairly high probability, is something that is innately adverse for people, right? We look at negative outcomes and we look for reasons that those negative outcomes will not come to pass.

It’s easy to say, “Well, yes, it’s only let’s say a 40% probability and we’ve had these before,” and it becomes very easy to identify reasons and not look at a situation completely objectively as to why the best course of action is not to take the kind of drastic measures that are necessary to avoid the probability of the negative outcome, even if you know that it’s likely to come to pass.

Anthony Aguirre: It’s even worst that when people do see something coming and take significant action and mitigate the problem, they rarely get the sort of credit that they should.

Emilia Javorsky: Oh, completely.

Anthony Aguirre: Because you never see the calamity unfold that they avoided.

Emilia Javorsky: Yes.

Anthony Aguirre: The tendency will be, “Oh, you overreacted, or oh, that was never a big problem in the first place.” It’s very hard to piece together like Y2K. I think it’s still unclear, at least it is to me, what exactly would have happened if we hadn’t made a huge effort to mitigate Y2K. There are many similar other things where it could be that there really was a calamity there and we totally prevented it by just being on top of it and putting a bunch of effort in, or it could be that it wasn’t that big of a deal, and it’s very, very hard to tell in retrospect.

That’s another unfortunate bias that if we could see the counterfactual world in which we didn’t do anything about Y2K and saw all this terrible stuff unfold, then we could make heroes out of the people that put all that effort in and sounded the warning and did all the mitigation. But we don’t see that. It’s rather unrewarding in a literal sense. It’s just you don’t get much reward for preventing catastrophes and you get lots of blame if you don’t prevent them.

Emilia Javorsky: This is something we deal with all the time on the healthcare side of things. This is why preventative health and public health and basic primary care really suffer to get the funding, get the attention that they really need. It’s exactly this. Nobody cares about the disease that they didn’t get, the heart attack they didn’t have, the stroke that they didn’t have. For those of us that come from a public health background, it’s been kind of a collective banging our head against the wall for a very long time because we know looking at the data that this is the best way to take care of population level health.

Emilia Javorsky: Yet knowing that and having the data to back it up, it’s very difficult to get the attention across all levels of the healthcare system, from getting the individual patient on board all the way up to how do we fund healthcare research in the US and abroad.

Lucas Perry: These are all excellent points. What I’m seeing from everything that you guys said is to back it up to what Anthony said quite while ago, there is a kind of risk exceptionalism where we feel that our country or ourselves won’t be exposed to catastrophic risks. It’s other people’s families who lose someone in a car accident but not mine, even though the risk of that is fairly high. There’s this second kind of bias going on that acting on risk in order to mitigate it based off pure reasoning alone seems to be very difficult, especially when the intervention to mitigate the risk is very expensive because it requires a lot of trust in the experts and the reasoning that goes behind it, like spending billions of dollars to prevent the next pandemic.

It feels more tangible and intuitive now, but maybe for people of newer generations it felt a little bit more silly and would have had to have been more of a rational cognitive decision. Then the last thing here seems to be that there’s asymmetry between different kinds of risks. Like if someone mitigates a pandemic from happening, it’s really hard to appreciate how good that was of a thing to do, but that seems to not be true of all risks. For example, with risks where the risk actually just exists somewhere like in a lab or a nuclear missile silo. For example, people like Stanislav Petrov and Vasili Arkhipov we’re able to appreciate it very easily just because there was a concrete event and there was a big dangerous thing and they have stopped it from happening.

It seems also skillful here to at least appreciate which kinds of risks are the kinds where if they would have happened, but they didn’t because we prevented them, we can notice that versus the kinds of risks where if we stop them from happening, we can’t even notice that we stopped it from happening. Adjusting our attitude towards those with each feature would seem skillful. Let’s focus in then on making good predictions. Anthony, earlier you brought up Metaculus, could you explain what Metaculus is and what it’s been doing and how it’s been involved in COVID-19?

Anthony Aguirre: Metaculus is at some level an effort to deal with precisely the problem that we’ve been discussing, that it’s difficult to make predictions and it’s difficult to have a reason to trust predictions, especially when they’re probabilistic ones about complicated things. The idea of Metaculus is sort of twofold or threefold maybe I would say. One part of it is that it’s been shown through the years and this is work by Tetlock and The Good Judgment Project and a whole series of projects within IARPA, the Intelligence Advanced Research Projects Agency, that groups of people making predictions about things and having those predictions carefully combined can make better predictions often than even small numbers of experts. There tend to be kind of biases on different sides.

If you carefully aggregate people’s predictions, you can at some level wash out those biases. As well, making predictions is something that some people are just really good at. It’s a skill that varies person to person and can be trained. There are people who are just really good at making predictions across a wide range of domains. Sometimes in making a prediction, general prediction skill can trump actual subject matter expertise. Of course, it’s good to have both if you possibly can, but lots of times experts have a huge understanding of the subject matter.

But if they’re not actually practiced or trained or spend a lot of time making predictions, they may not make better predictions than someone who is really good at making predictions, but has less depth of understanding of the actual topic. That’s something that some of these studies made clear. The idea of combining those two is to create a system that solicits predictions from lots of different people on questions of interest, aggregates those predictions, and identifies which people are really good at making predictions and kind of counts their prediction and input more heavily than other people.

So that if someone has just a year’s long track record of over and over again making good predictions about things, they have a tremendous amount of credibility and that gives you a reason to think that they’re going to make good predictions about things in the future. If you take lots of people, all of whom are good at making predictions in that way and combine their predictions together, you’re going to get something that’s much, much more reliable than just someone off the street or even an expert making a prediction in a one-off way about something.

That’s one aspect of it is identify good predictors, have them accrue a very objective track record of being right, and then have them in aggregate make predictions about things that are just going to be a lot more accurate than other methods you can come up with. Then the second thing, and it took me a long time to really see the importance of this, but I think our earlier conversation has kind of brought this out, is that if you have a single system or a single consistent set of predictions and checks on those predictions. Metaculus is a system that has many, many questions that have had predictions made on them and have resolved that has been checked against what actually happened.

What you can do then is start to understand what does it mean when Metaculus as a system says that there’s a 10% chance of something happening. You can really say of all the things on Metaculus that have a 10% chance of happening, about 10% of those actually happen. There’s a meaning to the 10%, which you can understand quite well, that if you say I went to Metaculus and where to go and make bets based on a whole bunch of predictions that were on it, you would know that the 10% predictions on Metaculus come true about 10% of the time, and you can use those numbers and actually making decisions. Whereas when you go to some random person and they say, “Oh, there’s a 10% chance,” as we discussed earlier, it’s really hard to know what exactly to make of that, especially if it’s a one-off event.

The idea of Metaculus was to both make a system that makes highly accurate predictions as best as possible, but also a kind of collection of events that have happened or not happened in the world that you can use to ground the probabilities and give meaning to them, so that there’s some operational meaning to saying that something on the system has a 90% chance of happening. This has been going on since about 2014 or ’15. It was born basically at the same time as the Future of Life Institute actually for very much the same reason, thinking about what can we do to positively affect the future.

In my mind, I went through exactly the reasoning of, if we want to positively affect the future, we have to understand what’s going to happen in probabilistic terms and how to think about what we can decide now and what sort of positive or negative effects will that have. To do that, you need predictions and you need probabilities. That got me thinking about, how could we generate those? What kind of system could give us the sorts of predictions and probabilities that we want? It’s now grown pretty big. Metaculus now has 1,800 questions that are live on the site and 210,000 predictions on them, sort of of order of a hundred predictions per question.

The questions are all manner of things from who is going to be elected in some election to will we have a million residents on Mars by 2052, to what will the case fatality rate be for COVID-19. It spans all kinds of different things. The track record has been pretty good. Something that’s unusual in the world is that you can just go on the site and see every prediction that the system has made and how it’s turned out and you can score it in various ways, but you can get just a clear sense of how accurate the system has been over time. Each user also has a similar track record that you can see exactly how accurate each person has been over time. They get a reputation and then the system folds that reputation in when it’s making predictions about new things.

With COVID-19, as I mentioned earlier, lots of people suddenly realized that they really wanted good predictions about things. We’ve had a huge influx of people and interest in the site focused on the pandemic. That suggested to us that this was something that people were really looking for and was helpful to people, so we put a bunch of effort into creating a kind of standalone subset of Metaculus called pandemic.metaculus.com that’s hosting just COVID-19 and pandemic related things. That has 120 questions or so live on it now with 23,000 predictions on them. All manner of how many cases, how many deaths will there be and various things, what sort of medical interventions might turn out to be useful, when will a lock down in a certain place be lifted. Of course, all these things are unknowable.

But again, the point here is to get a best estimate of the probabilities that can be folded into planning. I also find that even when it’s not a predictive thing, it’s quite useful as just an information aggregator. For example, one of the really frustratingly hard to pin down things in the COVID-19 pandemic is the infection or case fatality, like what is the ratio of fatalities to the total number of identified cases or symptomatic cases or infections. Those really are all over the place. There’s a lot of controversy right now about whether that’s more like 2% or more like 0.2% or even less. There are people advocating views like that. It’s a little bit surprising that it’s so hard to pin down, but that’s all tied up in the prevalence of testing and asymptomatic cases and all these sorts of things.

Even a way to have a sort of central aggregation place for people to discuss and compare and argue about and then make numerical estimates of this rate, even if it’s less a prediction, right, because this is something that exists now, there is some value of this ratio, so even something like that, having people come together and have a specific way to put in their numbers and compare and combine those numbers I think is a really useful service.

Lucas Perry: Can you say a little bit more about the efficacy of the predictions? Like for example, I think that you mentioned that Metaculus predicted COVID-19 at a 10% probability?

Anthony Aguirre: Well, somewhat amusingly, somewhat tragically, I guess, there was a series of questions on Metaculus about pandemics in general long before this one happened. In December, one of those questions closed, that is no more predictions were made on it, and that question was, will there be a naturally spawned pandemic leading to at least a hundred million reported infections or at least 10 million deaths in a 12 month period by the end of 2025? The probability that was given to that was 36% on Metaculus. It’s a surprisingly high number. We now know that that was more like 100% but of course we didn’t know that at the time, but I think that was a much higher number than a fair number of people would have given it and certainly a much higher number than we were taking into account in our decisions. If anyone in a position of power had really believed that there were 36% chance of that happening, that would have led, as we discussed earlier, to a lot different actions taken. So that’s one particular question that I found interesting, but I think the more interesting thing really is to look across a very large number of questions and how accurate the system is overall. And then again, to have a way to say that there’s a meaning to the probabilities that are generated by the system, even for things that are only going to happen once and never again.

Like there’s just one time that chloroquine is either going to work or not work. We’re going to discover that it does or that it doesn’t. Nonetheless, we can usefully take probabilities from the system predicting it, that are more useful than probabilities you’re going to get through almost any other way. If you ask most doctors what’s the probability that chloroquine is going to turn out to be useful? They’ll say, “Well we don’t know. Let’s do the clinical trials” and that’s a perfectly good answer. That’s true. We don’t know. But if you wanted to make a decision in terms of resource allocation say, you really want to know how is it looking, what’s the probability of that versus some other possible things that I might put resources into. Now in this case, I think we should just put resources into all of them if we possibly can because it’s so important that it makes sense to try everything.

But you can imagine lots of cases where there would be a finite set of resources and even in this case there is a finite set of resources. You might want to think about where are the highest probability things and you’d want numbers ideally associated with those things. And so that’s the hope is to help provide those numbers and more clarity of thinking about how to make decisions based on those numbers.

Lucas Perry: Are there things like Metaculus for experts?

Anthony Aguirre: Well, I would say that it is already for experts in that we certainly encourage people with subject matter expertise to be involved and often they are. There are lots of people who have training in infectious disease and so on that are on pandemic.metaculus and I think hopefully that expertise will manifest itself in being right. Though as I said, you could be very expert in something but pretty bad at making predictions on it and vice versa.

So I think there’s already a fairly high level of expertise, and I should plug this for the listeners. If you like making or reading predictions and having in depth discussions and getting into the weeds about the numbers. Definitely check this out. Metaculus could use more people making predictions and making discussion on it. And I would also say we’ve been working very hard to make it useful for people who want accurate predictions about things. So we really want this to be helpful and useful to people and if there are things that you’d like to see on it, questions you’d like to have answered, capabilities whatever. The system is there, ask for those, give us feedback and so on. So yeah, I think Metaculus is already aimed at being a system that experts in a given topic would use but it doesn’t base its weightings on expertise.

We might fold this in at some point if it proves useful, it doesn’t at the moment say, oh you’ve got a PhD in this so I’m going to triple the weight that I give to your prediction. It doesn’t do that. Your PhD should hopefully manifest itself as being right and then that would give you extra weight. That’s less useful though in something that is brand new. Like when we have lots of new people coming in and making predictions. It might be useful to fold in some weighting according to what their credentials or expertise are or creating some other systems where they can exhibit that on the system. Like say, “Here I am, I’m such and such an expert. Here’s my model. Here are the details, here’s the published paper. This is why you should believe me”. That might influence other people to believe their prediction more and use it to inform their prediction and therefore could end up having a lot of weight. We’re thinking about systems like that. That could add to just the pure reputation based system we have now.

Lucas Perry: All right. Let’s talk about this from a higher level. From the view of people who are interested and work in global catastrophic and existential risks and the kinds of broader lessons that we’re able to extract from COVID-19. For example, from the perspective of existential risk minded people, we can appreciate how disruptive COVID-19 is to human systems like the economy and the healthcare system, but it’s not a tail risk and its severity is quite low. The case fatality rate is somewhere around a percent plus or minus 0.8% or so and it’s just completely shutting down economies. So it almost makes one feel worse and more worried about something which is just a little bit more deadly or a little bit more contagious. The lesson or framing on this is the lesson of the fragility of human systems and how the world is dangerous and that we lack resilience.

Emilia Javorsky: I think it comes back to part of the conversation on a combination of how we make decisions and how decisions are made as a society being one part, looking at information and assessing that information and the other part of it being experience. And past experience really does steer how we think about attacking certain problem spaces. We have had near misses but we’ve gone through quite a long period of time where we haven’t had anything this in the case of pandemic or we can think of other categories of risk as well that’s been sufficient to disturb society in this way. And I think that there is some silver lining here that people now acutely understand the fragility of the system that we live in and how something like the COVID-19 pandemic can have such profound levels of disruption. Where on the spectrum of the types of risks that we’re assessing and talking about. This would be on the more milder end of the spectrum.

And so I do think that there is an opportunity potentially here where people now unfortunately have had the experience of seeing how severely life can be disrupted, and how quickly our systems break down, and that absence of fail-safes and sort of resilience baked into them to be able to deal with these sorts of things. From one perspective I can see how you would feel worse. From another perspective I definitely think there’s a conversation to have. And start to take seriously some of the other risks that fall into the category of being catastrophic on a global scale and not entirely remote in terms of their probabilities. Now that people are really listening and paying attention.

Anthony Aguirre: The risk of a pandemic has probably been going up with population density and people pushing into animals habitats and so on, but not maybe dramatically increasing with time. Whereas there are other things like a deliberately or accidentally human caused pandemic where people have deliberately taken a pathogen and made it more dangerous in one way or another. And there are risks, for example, in synthetic biology where things that would never have occurred naturally can be designed by people. These are risks and possibilities that I think are growing very, very rapidly because the technology is growing so rapidly and may therefore be very, very underestimated when we’re basing our risks on frequencies of things happening in the past. This really gets worse the more you think about it because the idea that a naturally occurring thing could be so devastating and that when you talk to people in infectious disease about what in principle could be made, there are all kinds of nasty properties of different pathogens that if combined would be something really, really terrible and nature wouldn’t necessarily combine them like that. There’s no particular reason to, but humans could.

Then you really open up really, really terrifying scenarios. I think this does really drive home in an intuitive, very visceral way that we’re not somehow magically immune to those things happening and that there isn’t necessarily some amazing system in place that’s just going to prevent or stop those things from happening if those things get out into the world. We’ve seen containment fail, what this lesson tells us that we should be doing and what we should be paying more attention to. And I think it’s something we really, really urgently need to discuss.

Emilia Javorsky: So much of the cultural psyche that we’ve had around these types of risks has focused so much primarily on bad actors. When we talk about the risks that arise from pandemics, tools like genetic engineering and synthetic biology. We hear a lot about bad actors and the risks of bio-terrorism, but what you’re discussing, and I think really rightly highlighting, is that there doesn’t have to be any sort of ill will baked into these kinds of risks for them to occur. There can just be sloppy science that’s part of this or science with inadequate safety engineering. I think that that’s something people are starting to appreciate now that we’re experiencing a naturally occurring pandemic where there’s no actor to point to. There’s no ill will, there’s no enemy so to speak. Which is how I think so much of the pandemic conversation has happened up until this point and other risks as well where everyone assumes that it’s some sort of ill will.

When we talk about nuclear risk, people think about generally the risk of a nuclear war starting. Well we know that the risk of nuclear war versus the risk of nuclear accident, those two things are very different and its accidental risk that is much more likely to be devastating than purposeful initiation of some global nuclear war. So I think that’s important too, is just getting an appreciation that these things can happen either naturally occurring or when we think about emerging technologies, just a failure to understand and appreciate and engage in the precautions and safety measures that are needed when dealing with largely unknown science.

Anthony Aguirre: I completely agree with you, while also worrying a little bit that our human tendency is to react more strongly against things that we see as deliberate. If you look at just the numbers of people that have died of terrorist attacks say, they’re tiny compared to many, many other causes. And yet we feel as a society very threatened and have spent incredible amounts of energy and resources protecting ourselves against those sorts of attacks. So there’s some way in which we tend to take much more seriously for some reason, problems and attacks that are willful and where we can identify a wrongdoer, an enemy.

So I’m not sure what to think. I totally agree with you that there are lots of problems that won’t have an enemy to be fighting against. Maybe I’m agreeing with you that I worry that we’re not going to take them seriously for that reason. So I wonder in terms of pandemic preparedness, whether we shouldn’t keep emphasizing that there are bad actors that could cause these things just because people might pay more attention to that, whereas they seem to be awfully dismissive of the natural ones. I’m not sure how to think about that.

Emilia Javorsky: I actually think I’m in complete agreement with you, Anthony, that my point is coming from perhaps misplaced optimism that this could be an inflection point in that kind of thinking.

Anthony Aguirre: Fair enough.

Lucas Perry: I think that what we like to do is actually just declare war on everything, at least in America. So maybe we’ll have to declare a war on pathogens or something and then people will have an enemy to fight against. So continuing here on trying to consider what lessons the coronavirus situation can teach us about global catastrophic and existential risks. We have an episode with Toby Ord coming out tomorrow, at the time of this recording. In that conversation, global catastrophic risk was defined as something which kills 10% of the global population. Coronavirus is definitely not going to do that via its direct effects nor its indirect effects. There are real risks and a real class of risks which are far more deadly and widely impacting than COVID-19 and one of these that I’d like to pivot into now is what you guys just mentioned briefly was the risk of synthetic bio.

So that would be like AI enabled synthetic biology. So pathogens or viruses which are constructed and edited in labs via new kinds of biotechnology. Could you explain this risk and how it may be a much greater risk in the 21st century than naturally occurring pandemics?

Emilia Javorsky: I think what I would separate out is thinking about synthetic biology vs genetic engineering. So there are definitely tools we can use to intervene in pathogens that we already know and exist and one can foresee and thinking down sort of the bad actor train of thought, how you could intervene in those to increase their lethality, increase their transmissibility. The other side of this that’s a more unexplored side and you alluded to it being sort of AI enabled. It can be enabled by AI, it can be enabled by human intelligence, which is the idea of synthetic biology and creating life forms, sort of nucleotide by nucleotide. So we now have that capacity to really design DNA, to design life in ways that we previously just did not have that capacity to do. There’s certainly a pathogen angle that, but there’s also a tremendously unknown element.

We could end up creating life forms that are not things that we would intuitively think of as sort of human designers of life. And so what are the certain risks that are posed by potential entirely new classes of pathogens that we have not yet encountered before? When we talk about tools for either intervening and pathogens that already exist and changing their characteristics or creating designer ones from scratch, is just how cheap and ubiquitous these technologies have become. They’re far more accessible in terms of how cheap they are, how available they are and the level of expertise required to work with them. There’s that aspect of being a highly accessible, dangerous technology that also changes how we think about that.

Anthony Aguirre: Unfortunately, it seems not hard for me or I think anyone, but unfortunately not also for the biologists that I’ve talked to, to imagine pathogens that are just categorically worse than the sorts of things that have happened naturally. With AIDS, HIV, it took us decades and we still don’t have a vaccine and that’s something that was able to spread quite widely before anyone even noticed that it existed. So you can imagine awful combinations of long asymptomatic transmission combined with terrible consequences and difficulty of any kind of countermeasures being deliberately combined into something that just would be really, really orders of magnitude more terrible in the things we’ve experienced. It’s hard to imagine why someone would do that, but there are lots of things that are hard to imagine that people nonetheless do unfortunately. I think everyone whose thought much about this agrees that it’s just a huge problem, potentially the sort of super pathogen that could in principle wipe out a significant fraction of the world’s population.

What is the cost associated with that? The value of the world is hard to even know how to calculate it. It is just a vast number.

Lucas Perry: Plus the deep future.

Emilia Javorsky: Right.

Anthony Aguirre: I suppose there’s a 0.01% chance of someone developing something like that in the next 20 years and deploying it. That’s a really tiny chance, probably not going to happen, but when you multiply it by quadrillions of dollars, that still merits a fairly large response because it’s a huge expected cost. So we should not be putting thousands or hundreds of thousands or even millions of dollars into worrying about that. We really should be putting billions of dollars into worrying about that, if we were running the numbers even within an order of magnitude correctly. So I think that’s an example where our response to a low probability, high impact threat is utterly, utterly tiny compared to where it should be. And there are some other examples, but that’s one of those ones where I think it would be hard to find someone who would say that that isn’t 0.1 or even 1% likely over the next 20 years.

But if you really take that seriously, we should be doing a ton about this and we’re just not. Looking at many such examples and there are not a huge number, but there are enough that it takes a fair amount of work to look at them. And that’s part of what the future of Life Institute is here to do. And I’m looking forward to hearing your interview with Toby Ord as well along those lines. We really should be taking those things more seriously as a society and we don’t have to put in the right amount of money in the sense that if it’s 1% likely we don’t have to put in 1% of a quadrillion dollars because fortunately it’s way, way cheaper to prevent these things than to actually deal with them. But at some level, money should be no object when it comes to making sure that our entire civilization doesn’t get wiped out.

We can take as a lesson from this current pandemic that terrible things do happen even if nobody wants them to or almost nobody wants them to, they can easily outstrip our ability to deal with them after they’ve happened, particularly if we haven’t correctly planned for them. But that we are at a place in the world history where we can see them potentially coming and do something about it. I do think when we’re stuck at home thinking about in this terrible case scenario, 1% or even a few percent of our citizens could be killed by this disease. And I think back to what it must’ve been like in the middle ages when a third of Europe was destroyed by the Black Death and they had no idea what was going on. Imagine how terrifying that was and as bad as it is now, we’re not in that situation. We know exactly what’s going on at some level. We know what we can do to prevent it and there’s no reason why we shouldn’t be doing that.

Emilia Javorsky: Something that keeps me up at night about these scenarios is that prevention is really the only key strategy that has a good shot at being effective because we see how much, and I take your HIV example as being a great one, of how long it takes us to even to begin to understand the consequences of a new pathogen on the human body and nevermind to figure out how to intervene. We are at the infancy of our understanding about human physiology and even more so in how do we intervene in it. And when you see the strategies that are happening today with vaccine development, we still know about approximately how long that takes. A lot of that’s driven by the need for clinical studies. We don’t have good models to predict how things perform in people. That’s on the vaccine side, It’s also on the therapeutic side.

This is why clinical trials are long and expensive and still fail quite late stage. Even when we get to the point of knowing that something works in a Petri dish and then a mouse and then an early pilot study. At a phase three clinical study, that drug can fail its efficacy endpoint. And that’s quite common and that’s part of what drives up the cost of drug development. And so from my perspective, having come from the human biology side, it just strikes me given where medical knowledge is and the rate at which it’s progressing, which is quick, but it’s not revolutionary and it’s dwarfed by the rate of progress in some of these other domains, be it AI or synthetic biology. And so I’m just not confident that our field will move fast enough to be able to deal with an entirely novel pathogen if it comes 10, 20 even 50 years down the road. Personally what motivates me and gets me really passionate is thinking about these issues and mitigation strategies today because I think that is the best place for our efforts at the moment.

Anthony Aguirre: One thing that’s encouraging I would say about the COVID-19 pandemic is seeing how many people are working so quickly and so hard to do things about it. There are all kinds of components to that. There’s vaccine and antivirals and then all of the things that we’re seeing play out are inventions that we’ve devised to fight against this new pathogen. You can imagine a lot of those getting better and more effective and some of them much more effective so you can in principle, imagine really quick and easy vaccine development, that seems super hard.

But you can imagine testing if there were sort of all over the place, little DNA sequencers that could just sequence whatever pathogens are around in the air or in a person and spit out the list of things that are in there. That would seem to be just an enormous extra tool in our toolkit. You can imagine things like, and I suspect that this is coming in the current crisis because it exists in other countries and it probably will exist with us. Something where if I am tested and either have or don’t have an infection, that that will go into a hopefully, but not necessarily privacy preserving and encrypted database that will then be coordinated and shared in some way with other people so that the system as a whole can assess the likelihood that the people that I’ve been in contact with, their risk has gone up and they might be notified, they might be told, “Oh, you should get a test this week instead of next week,” or something like that.

So you can imagine the sort of huge amount of data that are gathered on people now, as part of our modern, somewhat sketchy online ecosystem being used for this purpose. I think they probably will, if we could do so in a way that we actually felt comfortable with, like if I had a system where I felt like I can share my personal health data and feel like I’ve got trust in the system to respect my privacy and my interest, and to be a good fiduciary, like a doctor would, and keeping my interest paramount. Of course I’d be happy to share that information, and in return get useful information from the system.

So I think lots of people would want to buy into that, if they trusted the system. We’ve unfortunately gotten to this place where nobody trusts anything. They use it, even though they don’t trust it, but nobody actually trusts much of anything. But you can imagine having a trusted system like that, which would be incredibly useful for this sort of thing. So I’m curious what you see as the competition between these dangers and the new components of the human immune system.

Emilia Javorsky: I am largely in agreement that on the very short term, we have technologies available today. The system you just described is one of them that can deal with this issue of data, and understanding who, what, when where are these symptoms and these infections. And we can make so much smarter decisions as a society, and really have prevented a lot of what we’re seeing today, if such a system was in place. That system could be enabled by the technology we have today. I mean, it’s not a far reach to think that that would be out of grasp or require any kind of advances in science and technology to put in place. They require perhaps maybe advances in trust in society, but that’s not a technology problem. I do think that’s something that there will be a will to do after the dust settles on this particular pandemic.

I think where I’m most concerned is actually our short term future, because some of the technologies we’re talking about, genetic engineering, synthetic biology, will ultimately also be able to be harnessed to be mitigation strategies for the kinds of things that we will face in the future. What I guess I’m worried about is this gap between when we’ve advanced these technologies to a place that we’re confident that they’re safe and effective in people, and we have the models and robust clinical data in place to feel comfortable using them, versus how quickly the threat is advancing.

So I think in my vision towards the longer term future, maybe on the 100 year horizon, which is still relatively very short, beyond that I think there could be a balance between the risks and the ability to harness these technologies to actually combat those risks. I think in the shorter term future, to me there’s a gap between the rate at which the risk is increasing because of the increased availability and ubiquity of these tools, versus our understanding of the human body and ability to harness these technologies against those risks.

So for me, I think there’s total agreement that there’s things we can do today based on data and tesingt, and rapid diagnostics. We talk a lot about wearables and how those could be used to monitor biometric data to detect these things before people become symptomatic, those are all strategies we can do today. I think there’s longer term strategies of how we harness these new tools in biology to be able to be risk mitigators. I think there’s a gap in between there where the risk is very high and the tools that we have that are scalable and ready to go are still quite limited.

Lucas Perry: Right, so there’s a duality here where AI and big data can both be applied to helping mitigate the current threats and risks of this pandemic, but also future pandemics. Yet, the same technology can also be applied for speeding up the development of potentially antagonistic synthetic biology, organisms which bad actors or people who are deeply misanthropic, or countries wish to gain power and hold the world hostage, may be able to use to realize a global catastrophic or existential risk.

Emilia Javorsky: Yeah, I mean, I think AI’s part of it, but I also think that there’s a whole category of risk here that’s probably even more likely in the short term, which is just the risks introduced by human level intelligence with these pathogens. That knowledge exists of how to make things more lethal and more transmissible with the technology available today. So I would say both.

Lucas Perry: Okay, thanks for that clarification. So there’s clearly a lot of risks in the 21st Century from synthetic bio gone wrong, or used for nefarious purposes. What are some ways in which synthetic bio might be able to help us with pandemic preparedness, or to help protect us against bad actors?

Emilia Javorsky: When we think about the tools that are available to us today within the realm of biotechnology, so I would include genetic engineering and synthetic biology in that category. The upside is actually tremendously positive. Where we see the future for these tools, the benefits have the potential to far outweigh the risks. When we talk about using these tools, these are the same tools, very similar to when we think about developing more powerful AI systems that are very fundamental and able to solve many problems. So when you start to be able to intervene in really fundamental biology, that really unlocks the potential to treat so many of the diseases that lack good treatments today, and that are largely incurable.

But beyond that, they can take that a step further, and being able to increase our health spans and our life spans. Even more broadly than that, really are key to some of the things we think about as existential risks and existential hope for our species. Today we are talking in depth about pandemics and the role that biology can play as a risk factor. But those same tools can be harnessed. We’re seeing it now with more rapid vaccine development, but things like synthetic biology and genetic engineering, are fundamental leaps forward in being able to protect ourselves against these threats with new mitigation strategies, and making our own biology and immune systems more resilient to these types of threats.

That ability for us to really now engineer and intervene in human biology, and thinking towards the medium to longterm future, unlocks a lot of possibilities for us, beyond just being able to treat and cure diseases. We think about how our own planet and climate is evolving, and we can use these same tools to evolve with it, and evolve to be more tolerant to some of the challenges that lie ahead. We all kind of know that eventually, whether that eventual will be sooner or much later, the survival of our species is contingent on becoming multi planetary. When we think about enduring the kind of stressors that even near term space travel impose and living in alien environments and adapting to alien environments, these are the fundamental tools that will really enable us to do that.

Well today, we’re starting to see the downsides of biology and some of the limitations of the tools we have today to intervene, and understanding what some of the near term risks are that the science of today poses in terms of pandemics. But really the future here is very, very bright for how these tools can be used to mitigate risk in the future, but also take us forward.

Lucas Perry:You have me thinking here about a great Carl Sagan quote that I really like where he says, “It will not be who reach Alpha Centauri and the other nearby stars, it will be a species very like us, but with more of our strengths and fewer of our weaknesses.” So, yeah, that seems to be in line with the upsides of synthetic bio.

Emilia Javorsky: You could even see the foundations of how we could use the tools that we have today to start to get to Proxima B. I think that quote would be realized in hopefully the not too distant future.

Lucas Perry: All right. So, taking another step back here, let’s get a little bit more perspective again on extracting some more lessons.

Anthony Aguirre: There were countries that were prepared for this and acted fairly quickly, and efficaciously, partly because they maybe had more firsthand experience with the previous perspective pandemics, but also maybe they just had a slightly different constituted society and leadership structure. There’s a danger here, I think, of seeing that top down and authoritarian governments have seen to be potentially more effective in dealing with this, because they can just take quick action. They don’t have to do a bunch of red tape or worry about pesky citizen’s rights and things, and they can just do what they want and crush the virus.

I don’t think that’s entirely accurate, but to the degree that it is, or that people perceive it to be, that worries me a little bit, because I really do strongly favor open societies and western democratic institutions over more totalitarian ones. I do worry that when our society and system of government so abjectly fails in serving its people, that people will turn to something rather different, or become very tolerant of something rather different, and that’s really bad news for us, I think.

So that worries me, a kind of competition of forms of government level that I really would like to see a better version of ours making itself seen and being effective in something like this, and sort of proving that there isn’t necessarily a conflict between having a right conferring, open society, with a strong voice of the people, and having something that is competent and serves its people well, and is capable in a crisis. They should not be mutually exclusive, and if we make them so, then we do so at great peril, I think.

Emilia Javorsky: That same worry keeps me up at night. I’ll try an offer an optimistic take on it.

Anthony Aguirre: Please.

Emilia Javorsky: Which is that authoritarian regimes are also the type that are not noted for their openness, and their transparency, and their ability to share realtime data on what’s happening within their borders. And so I think when we think about this pandemic or global catastrophic risk more broadly, the we is inherently the global community. That’s the nature of a global catastrophic risk. I think part of what has happened in this particular pandemic is it hit in the time where the spirit of multilateralism and global cooperation is arguably, in modern memory, partially the weakest its been. And so I think that the other way to look at it is, how do we cultivate systems of government that are capable of working together and acting on a global scale, and understanding that pandemics and global catastrophic risk is not confined to national borders. And how do you develop the data sharing, the information sharing, and also the ability to respond to that data in realtime at a global scale?

The strongest argument for forms of government that comes out of this is a pivot towards one that is much more open, transparent, and cooperative than perhaps we’ve been seeing as of late.

Anthony Aguirre: Well, I hope that is the lesson that’s taken. I really do.

Emilia Javorsky: I hope so, too. That’s the best perspective I can offer on it, because I too, am a fan of democracy and human rights. I believe these are generally good things.

Lucas Perry: So wrapping things up here, let’s try to get some perspective and synthesis of everything that we’ve learned from the COVID-19 crisis and what we can do in the future, what we’ve learned about humanity’s weaknesses and strengths. So, if you were to have a short pitch each to world leaders about lessons from COVID-19, what would that be? We can start with Anthony.

Anthony Aguirre: This crisis has thrust a lot of leaders and policy makers into the situation where they’re realizing that they have really high stakes decisions to make, and simply not the information that they need to make them well. They don’t have the expertise on hand. They don’t have solid predictions and modeling on hand. They don’t have the tools to fold those things together to understand what the results of their decisions will be and make the best decision.

So I think, I would suggest strongly that policy makers put in place those sorts of systems, how am I going to get reliable information from experts that allows me to understand from them, and model what is going to happen given different choices that I could make and make really good decisions so that when a crisis like this hits, we don’t find ourselves in the situation of simply not having the tools at our disposal to handle the crisis. And then I’d say having put those things in place, don’t wait for a crisis to use them. Just use those things all the time and make good decisions for society based on technology and expertise and understanding that we now are able to put in place together as a society, rather than whatever decision making processes we’ve generated socially and historically and so on. We actually can do a lot better and have a really, really well run society if we do so.

Lucas Perry: All right, and Emilia?

Emilia Javorsky: Yeah, I want to echo Anthony’s sentiment there with the need for evidence based realtime data at scale. That’s just so critical to be able to orchestrate any kind of meaningful response. And also to be able to act as Anthony eludes to, before you get to the point of a crisis, because there was a lot of early indicators here that could have prevented this situation that we’re in today. I would add that the next step in that process is also developing mechanisms to be able to respond in realtime at a global scale, and I think we are so caught up in sort of moments of an us verse them, whether that be on a domestic or international level, but the spirit of multilateralism is just at an all-time low.

I think we’ve been sorely reminded that when there’s global level threats, they require a global level response. No matter how much people want to be insular and think that their countries have borders, the fact of the matter is is that they do not. And we’re seeing the interdependency of our global system. So I think that in addition to building those data structures to get information to policy makers, there also needs to be a sort of supply chain and infrastructure built, and decision making structure to be able to respond to that information in real time.

Lucas Perry: You mentioned information here. One of the things that you did want to talk about on the podcast was information problems and how information is currently extremely partisan.

Emilia Javorsky: It’s less so that it’s partisan, and more so that it’s siloed and biased and personalized. I think one aspect of information that’s been very difficult in this current information environment, is the ability to communicate to a large audience accurate information, because the way that we communicate information today is mainly through click bait style titles. When people are mainly consuming information in a digital format, and it’s highly personalized, it’s highly tailored to their preferences, both in terms of the news outlets that they innately turn to for information, but also their own personal algorithms that know what kind of news to show you, whether it be in your social feeds or what have you.

I think when the structure of how we disseminate information is so personalized and partisan, it becomes very difficult to bring through all of that noise to communicate to people accurate balanced, measured, information. Because even when you do, it’s human nature that that’s not the types of things people are innately going to seek out. So what in times like this are mechanisms of disseminating information that we can think about that supersede all of that individualized media, and really get through to say, “All right, everyone needs to be on the same page and be operating off the best state of information that we have at this point. And this is what that is.”

Lucas Perry: All right, wonderful. I think that helps to more fully unpack this data structure point that Anthony and you were making. So yeah, thank you both so much for your time, and for helping us to reflect on lessons from COVID-19.

FLI Podcast: The Precipice: Existential Risk and the Future of Humanity with Toby Ord

Toby Ord’s “The Precipice: Existential Risk and the Future of Humanity” has emerged as a new cornerstone text in the field of existential risk. The book presents the foundations and recent developments of this budding field from an accessible vantage point, providing an overview suitable for newcomers. For those already familiar with existential risk, Toby brings new historical and academic context to the problem, along with central arguments for why existential risk matters, novel quantitative analysis and risk estimations, deep dives into the risks themselves, and tangible steps for mitigation. “The Precipice” thus serves as both a tremendous introduction to the topic and a rich source of further learning for existential risk veterans. Toby joins us on this episode of the Future of Life Institute Podcast to discuss this definitive work on what may be the most important topic of our time.

Topics discussed in this episode include:

  • An overview of Toby’s new book
  • What it means to be standing at the precipice and how we got here
  • Useful arguments for why existential risk matters
  • The risks themselves and their likelihoods
  • What we can do to safeguard humanity’s potential

Timestamps: 

0:00 Intro 

03:35 What the book is about 

05:17 What does it mean for us to be standing at the precipice? 

06:22 Historical cases of global catastrophic and existential risk in the real world

10:38 The development of humanity’s wisdom and power over time  

15:53 Reaching existential escape velocity and humanity’s continued evolution

22:30 On effective altruism and writing the book for a general audience 

25:53 Defining “existential risk” 

28:19 What is compelling or important about humanity’s potential or future persons?

32:43 Various and broadly appealing arguments for why existential risk matters

50:46 Short overview of natural existential risks

54:33 Anthropogenic risks

58:35 The risks of engineered pandemics 

01:02:43 Suggestions for working to mitigate x-risk and safeguard the potential of humanity 

01:09:43 How and where to follow Toby and pick up his book

 

This podcast is possible because of the support of listeners like you. If you found this conversation to be meaningful or valuable consider supporting it directly by donating at futureoflife.org/donate. Contributions like yours make these conversations possible.

All of our podcasts are also now on Spotify and iHeartRadio! Or find us on SoundCloudiTunesGoogle Play and Stitcher.

You can listen to the podcast above or read the transcript below. 

Lucas Perry: Welcome to the Future of Life Institute Podcast. I’m Lucas Perry. This episode is with Toby Ord and covers his new book “The Precipice: Existential Risk and the Future of Humanity.” This is a new cornerstone piece in the field of existential risk and I highly recommend this book for all persons of our day and age. I feel this work is absolutely critical reading for living an informed, reflective, and engaged life in our time. And I think even for those well acquainted with this topic area will find much that is both useful and new in this book. Toby offers a plethora of historical and academic context to the problem, tons of citations and endnotes, useful definitions, central arguments for why existential risk matters that can be really helpful for speaking to new people about this issue, and also novel quantitative analysis and risk estimations, as well as what we can actually do to help mitigate these risks. So, if you’re a regular listener to this podcast, I’d say this is a must add to your science, technology, and existential risk bookshelf. 

The Future of Life Institute is a non-profit and this podcast is funded and supported by listeners like you. So if you find what we do on this podcast to be important and beneficial, please consider supporting the podcast by donating at futureoflife.org/donate. If you support any other content creators via services like Patreon, consider viewing a regular subscription to FLI in the same light. You can also follow us on your preferred listening platform, like on Apple Podcasts or Spotify, by searching for us directly or following the links on the page for this podcast found in the description.

Toby Ord is a Senior Research Fellow in Philosophy at Oxford University. His work focuses on the big picture questions facing humanity. What are the most important issues of our time? How can we best address them?

Toby’s earlier work explored the ethics of global health and global poverty, demonstrating that aid has been highly successful on average and has the potential to be even more successful if we were to improve our priority setting. This led him to create an international society called Giving What We Can, whose members have pledged over $1.5 billion to the most effective charities helping to improve the world. He also co-founded the wider effective altruism movement, encouraging thousands of people to use reason and evidence to help others as much as possible.

His current research is on the long-term future of humanity,  and the risks which threaten to destroy our entire potential.

Finally, the Future of Life Institute podcasts have never had a central place for conversation and discussion about the episodes and related content. In order to facilitate such conversation, I’ll be posting the episodes to the LessWrong forum at Lesswrong.com where you’ll be able to comment and discuss the episodes if you so wish. The episodes more relevant to AI alignment will be crossposted from LessWrong to the Alignment Forum as well at alignmentforum.org.  

And so with that, I’m happy to present Toby Ord on his new book “The Precipice.”

We’re here today to discuss your new book, The Precipice: Existential Risk and the Future of Humanity. Tell us a little bit about what the book is about.

Toby Ord: The future of humanity, that’s the guiding idea, and I try to think about how good our future could be. That’s what really motivates me. I’m really optimistic about the future we could have if only we survive the risks that we face. There have been various natural risks that we have faced for as long as humanity’s been around, 200,000 years of Homo sapiens or you might include an even broader definition of humanity that’s even longer. That’s 2000 centuries and we know that those natural risks can’t be that high or else we wouldn’t have been able to survive so long. It’s quite easy to show that the risks should be lower than about 1 in 1000 per century.

But then with humanity’s increasing power over that time, the exponential increases in technological power. We reached this point last century with the development of nuclear weapons, where we pose a risk to our own survival and I think that the risks have only increased since then. We’re in this new period where the risk is substantially higher than these background risks and I call this time the precipice. I think that this is a really crucial time in the history and the future of humanity, perhaps the most crucial time, this few centuries around now. And I think that if we survive, and people in the future, look back on the history of humanity, schoolchildren will be taught about this time. I think that this will be really more important than other times that you’ve heard of such as the industrial revolution or even the agricultural revolution. I think this is a major turning point for humanity. And what we do now will define the whole future.

Lucas Perry: In the title of your book, and also in the contents of it, you developed this image of humanity to be standing at the precipice, could you unpack this a little bit more? What does it mean for us to be standing at the precipice?

Toby Ord: I sometimes think of humanity has this grand journey through the wilderness with dark times at various points, but also moments of sudden progress and heady views of the path ahead and what the future might hold. And I think that this point in time is the most dangerous time that we’ve ever encountered, and perhaps the most dangerous time that there will ever be. So I see it in this central metaphor of the book, humanity coming through this high mountain pass and the only path onwards is this narrow ledge along a cliff side with this steep and deep precipice at the side and we’re kind of inching our way along. But we can see that if we can get past this point, there’s ultimately, almost no limits to what we could achieve. Even if we can’t precisely estimate the risks that we face, we know that this is the most dangerous time so far. There’s every chance that we don’t make it through.

Lucas Perry: Let’s talk a little bit then about how we got to this precipice and our part in this path. Can you provide some examples or a story of global catastrophic risks that have happened and near misses of possible existential risks that have occurred so far?

Toby Ord: It depends on your definition of global catastrophe. One of the definitions that’s on offer is 10%, or more of all people on the earth at that time being killed in a single disaster. There is at least one time where it looks like we’ve may have reached that threshold, which was the Black Death, which killed between a quarter and a half of people in Europe and may have killed many people in South Asia and East Asia as well and the Middle East. It may have killed one in 10 people across the whole world. Although because our world was less connected than it is today, it didn’t reach every continent. In contrast, the Spanish Flu 1918 reached almost everywhere across the globe, and killed a few percent of people.

But in terms of existential risk, none of those really posed an existential risk. We saw, for example, that despite something like a third of people in Europe dying, that there wasn’t a collapse of civilization. It seems like we’re more robust than some give us credit for, but there’ve been times where there hasn’t been an actual catastrophe, but there’s been near misses in terms of the chances.

There are many cases actually connected to the Cuban Missile Crisis, a time of immensely high tensions during the Cold War in 1962. I think that the closest we have come is perhaps the events on a submarine that was unknown to the U.S. that it was carrying a secret nuclear weapon and the U.S. Patrol Boats tried to force it to surface by dropping what they called practice depth charges, but the submarine thought that there were real explosives aimed at hurting them. The submarine was made for the Arctic and so it was overheating in the Caribbean. People were dropping unconscious from the heat and the lack of oxygen as they tried to hide deep down in the water. And during that time the captain, Captain Savitsky, ordered that this nuclear weapon be fired and the political officer gave his consent as well.

On any of the other submarines in this flotilla, this would have been enough to launch this torpedo that then would have been a tactical nuclear weapon exploding and destroying the fleet that was oppressing them, but on this one, it was lucky that the flotilla commander was also on board this submarine, Captain Vasili Arkhipov and so, he overruled this and talked Savitsky down from this. So this was a situation at the height of this tension where a nuclear weapon would have been used. And we’re not quite sure, maybe Savitsky would have decided on his own not to do it, maybe he would have backed down. There’s a lot that’s not known about this particular case. It’s very dramatic.

But Kennedy had made it very clear that any use of nuclear weapons against U.S. Armed Forces would lead to an all-out full scale attack on the Soviet Union, so they hadn’t anticipated that tactical weapons might be used. They assumed it would be a strategic weapon, but it was their policy to respond with a full scale nuclear retaliation and it looks likely that that would have happened. So that’s the case where ultimately zero people were killed in that event. The submarine eventually surfaced and surrendered and then returned to Moscow where people were disciplined, but it brought us very close to this full scale nuclear war.

I don’t mean to imply that that would have been the end of humanity. We don’t know whether humanity would survive the full scale nuclear war. My guess is that we would survive, but that’s its own story and it’s not clear.

Lucas Perry: Yeah. The story to me has always felt a little bit unreal. It’s hard to believe we came so close to something so bad. For listeners who are not aware, the Future of Life Institute gives out a $50,000 award each year, called the Future of Life Award to unsung heroes who have contributed greatly to the existential security of humanity. We actually have awarded Vasili Arkhipov’s family with the Future of Life Award, as well as Stanislav Petrov and Matthew Meselson. So if you’re interested, you can check those out on our website and see their particular contributions.

And related to nuclear weapons risk, we also have a webpage on nuclear close calls and near misses where there were accidents with nuclear weapons which could have led to escalation or some sort of catastrophe. Is there anything else here you’d like to add in terms of the relevant historical context and this story about the development of our wisdom and power over time?

Toby Ord: Yeah, that framing, which I used in the book comes from Carl Sagan in the ’80s when he was one of the people who developed the understanding of nuclear winter and he realized that this could pose a risk to humanity on the whole. The way he thought about it is that we’ve had this massive development over the hundred billion human lives that have come before us. This succession of innovations that have accumulated building up this modern world around us.

If I look around me, I can see almost nothing that wasn’t created by human hands and this, as we all know, has been accelerating and often when you try to measure exponential improvements in technology over time, leading to the situation where we have the power to radically reshape the Earth’s surface, both say through our agriculture, but also perhaps in a moment through nuclear war. This increasing power has put us in a situation where we hold our entire future in the balance. A few people’s actions over a few minutes could actually potentially threaten that entire future.

In contrast, humanity’s wisdom has grown only falteringly, if at all. Many people would suggest that it’s not even growing. And by wisdom here, I mean, our ability to make wise decisions for human future. I talked about this in the book under the idea about civilizational virtues. So if you think of humanity as a group of agents, in the same way that we think of say nation states as group agents, we talk about is it in America’s interest to promote this trade policy or something like that? We can think of what’s in humanity’s interests and we find that if we think about it this way, humanity is crazily impatient and imprudent.

If you think about the expected lifespan of humanity, a typical species lives for about a million years. Humanity is about 200,000 years old. We have something like 800,000 or a million or more years ahead of us if we play our cards right and we don’t lead to our own destruction. The analogy would be 20% of the way through our life, like an adolescent who’s just coming into his or her own power, but doesn’t have the wisdom or the patience to actually really pay any attention to this possible whole future ahead of them and so they’re just powerful enough to get themselves in trouble, but not yet wise enough to avoid that.

If you continue this analogy, what is often hard for humanity at the moment to think more than a couple of election cycles ahead at best, but that would correspond say eight years to just the next eight hours within this person’s life. For the kind of short term interests during the rest of the day, they put the whole rest of their future at risk. And so I think that that helps to see what this lack of wisdom looks like. It’s not that it’s just a highfalutin term of some sort, but you can kind of see what’s going on is that the person is incredibly imprudent and impatient. And I think that many others virtues or vices that we think of in an individual human’s life can be applied in this context and are actually illuminating about where we’re going wrong.

Lucas Perry: Wonderful. Part of the dynamic here in this wisdom versus power race seems to be one of the solutions being slowing down power seems untenable or that it just wouldn’t work. So it seems more like we have to focus on amplifying wisdom. Is this also how you view the dynamic?

Toby Ord: Yeah, that is. I think that if humanity was more coordinated, if we were able to make decisions in a unified manner better than we actually can. So, if you imagine this was a single player game, I don’t think it would be that hard. You could just be more careful with your development of power and make sure that you invest a lot in institutions, and in really thinking carefully about things. I mean, I think that the game is ours to lose, but unfortunately, we’re less coherent than that and if one country decides to hold off on developing things, then other countries might run ahead and produce similar amount of risk.

Theres this kind of the tragedy of the commons at this higher level and so I think that it’s extremely difficult in practice for humanity to go slow on progress of technology. And I don’t recommend that we try. So in particular, there’s only at the moment, only a small number of people who really care about these issues and are really thinking about the long-term future and what we could do to protect it. And if those people were to spend their time arguing against progress of technology, I think that it would be a really poor use of their energies and probably just annoy and alienate the people they were trying to convince. And so instead, I think that the only real way forward is to focus on improving wisdom.

I don’t think that’s impossible. I think that humanity’s wisdom, as you could see from my comment before about how we’re kind of disunified, partly, it involves being able to think better about things as individuals, but it also involves being able to think better collectively. And so I think that institutions for overcoming some of these tragedies of the commons or prisoner’s dilemmas at this international level, are an example of the type of thing that will make humanity make wiser decisions in our collective interest.

Lucas Perry: It seemed that you said by analogy, that humanity’s lifespan would be something like a million years as compared with other species.

Toby Ord: Mm-hmm (affirmative).

Lucas Perry: That is likely illustrative for most people. I think there’s two facets of this that I wonder about in your book and in general. The first is this idea of reaching existential escape velocity, where it would seem unlikely that we would have a reason to end in a million years should we get through the time of the precipice and the second is I’m wondering your perspective on Nick Bostrom calls what matters here in the existential condition, Earth-originating intelligent life. So, it would seem curious to suspect that even if humanity’s existential condition were secure that we would still be recognizable as humanity in some 10,000, 100,000, 1 million years’ time and not something else. So, I’m curious to know how the framing here functions in general for the public audience and then also being realistic about how evolution has not ceased to take place.

Toby Ord: Yeah, both good points. I think that the one million years is indicative of how long species last when they’re dealing with natural risks. It’s I think a useful number to try to show why there are some very well-grounded scientific reasons for thinking that a million years is entirely in the ballpark of what we’d expect if we look at other species. And even if you look at mammals or other hominid species, a million years still seems fairly typical, so it’s useful in some sense for setting more of a lower bound. There are species which have survived relatively unchanged for much longer than that. One example is the horseshoe crab, which is about 450 million years old whereas complex life is only about 540 million years old. So that’s something where it really does seem like it is possible to last for a very long period of time.

If you look beyond that the Earth should remain habitable for something in the order of 500 million or a billion years for complex life before it becomes too hot due to the continued brightening of our sun. If we took actions to limit that brightening, which look almost achievable with today’s technology, we would only need to basically shade the earth by about 1% of the energy coming at it and increase that by 1%, I think it’s every billion years, we will be able to survive as long as the sun would for about 7 billion more years. And I think that ultimately, we could survive much longer than that if we could reach our nearest stars and set up some new self-sustaining settlement there. And then if that could then spread out to some of the nearest stars to that and so on, then so long as we can reach about seven light years in one hop, we’d be able to settle the entire galaxy. There are stars in the galaxy that will still be burning in about 10 trillion years from now and there’ll be new stars for millions of times as long as that.

We could have this absolutely immense future in terms of duration and the technologies that are beyond our current reach and if you look at the energy requirements to reach nearby stars, they’re high, but they’re not that high compared to say, the output of the sun over millions of years. And if we’re talking about a scenario where we’d last millions of years anyway, it’s unclear why it would be difficult with the technology would reach them. It seems like the biggest challenge would be lasting that long in the first place, not getting to the nearest star using technology for millions of years into the future with millions of years of stored energy reserves.

So that’s the kind of big picture question about the timing there, but then you also ask about would it be humanity? One way to answer that is, unless we go to a lot of effort to preserve Homo sapiens as we are now then it wouldn’t be Homo sapiens. We might go to that effort if we decide that it’s really important that it be Homo sapiens and that we’d lose something absolutely terrible. If we were to change, we could make that choice, but if we decide that it would be better to actually allow evolution to continue, or perhaps to direct it by changing who we are with genetic engineering and so forth, then we could make that choice as well. I think that that is a really critically important choice for the future and I hope that we make it in a very deliberate and careful manner rather than just going gung-ho and letting people do whatever they want, but I do think that we will develop into something else.

But in the book, my focus is often on humanity in this kind of broad sense. Earth-originating intelligent life would kind of be a gloss on it, but that has the issue that suppose humanity did go extinct and suppose we got lucky and some other intelligent life started off again, I don’t want to count that in what I’m talking about, even though it would technically fit into Earth-originating intelligent life. Sometimes I put it in the book as humanity or our rightful heirs something like that. Maybe we would create digital beings to replace us, artificial intelligences of some sort. So long as they were the kinds of beings that could actually fulfill the potential that we have, they could realize one of the best trajectories that we could possibly reach, then I would count them. It could also be that we create something that succeeds us, but has very little value, then I wouldn’t count it.

So yeah, I do think that we may be greatly changed in the future. I don’t want that to distract the reader, if they’re not used to thinking about things like that because they might then think, “Well, who cares about that future because it will be some other things having the future.” And I want to stress that there will only be some other things having the future if we want it to be, if we make that choice. If that is a catastrophic choice, then it’s another existential risk that we have to deal with in the future and which we could prevent. And if it is a good choice and we’re like the caterpillar that really should become a butterfly in order to fulfill its potential, then we need to make that choice. So I think that is something that we can leave to future generations that it is important that they make the right choice.

Lucas Perry: One of the things that I really appreciate about your book is that it tries to make this more accessible for a general audience. So, I actually do like it when you use lower bounds on humanity’s existential condition. I think talking about billions upon billions of years can seem a little bit far out there and maybe costs some weirdness points and as much as I like the concept of Earth-originating intelligent life, I also think it costs some weirdness points.

And it seems like you’ve taken some effort to sort of make the language not so ostracizing by decoupling it some with effective altruism jargon and the kind of language that we might use in effective altruism circles. I appreciate that and find it to be an important step. The same thing I feel feeds in here in terms of talking about descendant scenarios. It seems like making things simple and leveraging human self-interest is maybe important here.

Toby Ord: Thanks. When I was writing the book, I tried really hard to think about these things, both in terms of communications, but also in terms of trying to understand what we have been talking about for all of these years when we’ve been talking about existential risk and similar ideas. Often when in effective altruism, there’s a discussion about the different types of cause areas that effective altruists are interested in. There’s people who really care about global poverty, because we can help others who are much poorer than ourselves so much more with our money, and also about helping animals who are left out of the political calculus and the economic calculus and we can see why it is that they’re interests are typically neglected and so we look at factory farms, and we can see how we could do so much good.

And then also there’s this third group of people and then the conversation drifts off a bit, it’s like who have this kind of idea about the future and it’s kind of hard to describe and how to kind of wrap up together. So I’ve kind of seen that as one of my missions over the last few years is really trying to work out what is it that that third group of people are trying to do? My colleague, Will MacAskill, has been working on this a lot as well. And what we see is that this other group of effective altruists are this long-termist group.

The first group is thinking about this cosmopolitan aspect as much as me and it’s not just people in my country that matter, it’s people across the whole world and some of those could be helped much more. And the second group is saying, it’s not just humans that could be helped. If we widen things up beyond the species boundary, then we can see that there’s so much more we could do for other conscious beings. And then this third group is saying, it’s not just our time that we can help, there’s so much we can do to help people perhaps across this entire future of millions of years or further into the future. And so the difference there, the point of leverage is this difference between what fraction of the entire future is our present generation is perhaps just a tiny fraction. And if we can do something that will help that entire future, then that’s where this could be really key in terms of doing something amazing with our resources and our lives.

Lucas Perry: Interesting. I actually had never thought of it that way. And I think it puts it really succinctly the differences between the different groups that people focused on global poverty are reducing spatial or proximity bias in people’s focus on ethics or doing good. Animal farming is a kind of anti-speciesism, broadening our moral circle of compassion to other species and then the long-termism is about reducing time-based ethical bias. I think that’s quite good.

Toby Ord: Yeah, that’s right. In all these cases, you have to confront additional questions. It’s not just enough to make this point and then it follows that things are really important. You need to know, for example, that there really are ways that people can help others in distant countries and that the money won’t be squandered. And in fact, for most of human history, there weren’t ways that we could easily help people in other countries just by writing out a check to the right place.

When it comes to animals, there’s a whole lot of challenging questions there about what is the effects of changing your diet or the effects of donating to a group that prioritize animals in campaigns against factory farming or similar and when it comes to the long-term future, there’s this real question about “Well, why isn’t it that people in the future would be just as able to protect themselves as we are? Why wouldn’t they be even more well-situated to attend to their own needs?” Given the history of economic growth and this kind of increasing power of humanity, one would expect them to be more empowered than us, so it does require an explanation.

And I think that the strongest type of explanation is around existential risk. Existential risks are things that would be an irrevocable loss. So, as I define them, which is a simplification, I think of it as the destruction of humanity’s long-term potential. So I think of our long term potential as you could think of this set of all possible futures that we could instantiate. If you think about all the different collective actions of humans that we could take across all time, this kind of sets out this huge kind of cloud of trajectories that humanity could go in and I think that this is absolutely vast. I think that there are ways if we play our cards right of lasting for millions of years or billions or trillions and affecting billions of different worlds across the cosmos, and then doing all kinds of amazing things with all of that future. So, we’ve got this huge range of possibilities at the moment and I think that some of those possibilities are extraordinarily good.

If we were to go extinct, though, that would collapse this set of possibilities to a much smaller set, which contains much worse possibilities. If we went extinct, there would be just one future, whatever it is that would happen without humans, because there’d be no more choices that humans could make. If we had an irrevocable collapse of civilization, something from which we could never recover, then that would similarly reduce it to a very small set of very meager options. And it’s possible as well that we could end up locked into some dystopian future, perhaps through economic or political systems, where we end up stuck in some very bad corner of this possibility space. So that’s our potential. Our potential is currently the value of the best realistically realizable worlds available to us.

If we fail in an existential catastrophe, that’s the destruction of almost all of this value, and it’s something that you can never get back, because it’s our very potential that would be being destroyed. That then has an explanation as to why it is that people in the future wouldn’t be better able to solve their own problems because we’re talking about things that could fail now, that helps explain why it is that there’s room for us to make such a contribution.

Lucas Perry: So if we were to very succinctly put the recommended definition or framing on existential risk that listeners might be interested in using in the future when explaining this to new people, what is the sentence that you would use?

Toby Ord: An existential catastrophe is the destruction of humanity’s long-term potential, and an existential risk is the risk of such a catastrophe.

Lucas Perry: Okay, so on this long-termism point, can you articulate a little bit more about what is so compelling or important about humanity’s potential into the deep future and which arguments are most compelling to you with a little bit of a framing here on the question of whether or not the long-termist’s perspective is compelling or motivating for the average person like, why should I care about people who are far away in time from me?

Toby Ord: So, I think that a lot of people if pressed and they’re told “does it matter equally much if a child 100 years in the future suffers as a child at some other point in time?” I think a lot of people would say, “Yeah, it matters just as much.” But that’s not how we normally think of things when we think about what charity to donate to or what policies to implement, but I do think that it’s not that foreign of an idea. In fact, the weird thing would be why it is that people in virtue of the fact that they live in different times matter different amounts.

A simple example of that would be suppose you do think that things further into the future matter less intrinsically. Economists sometimes represent this by a pure rate of time preference. It’s a component of a discount rate, which is just to do with things mattering less in the future, whereas most of the discount rate is actually to do with the fact that money is more important to have earlier which is actually a pretty solid reason, but that component doesn’t affect any of these arguments. It’s only this little extra aspect about things matter less just because we’re in the future. Suppose you have that 1% discount rate of that form. That means that someone’s older brother matters more than their younger brother, that their life is equally long and has the same kinds of experiences is fundamentally more important for their older child than the younger child, things like that. This just seems kind of crazy to most people, I think.

And similarly, if you have these exponential discount rates, which is typically the only kind that economists consider, it has these consequences that what happens in 10,000 years is way more important than what happens in 11,000 years. People don’t have any intuition like that at all, really. Maybe we don’t think that much about what happens in 10,000 years, but 11,000 is pretty much the same as 10,000 from our intuition, but these other views say, “Wow. No, it’s totally different. It’s just like the difference between what happens next year and what happens in a thousand years.”

It generally just doesn’t capture our intuitions and I think that what’s going on is not so much that we have a kind of active intuition that things that happen further into the future matter less and in fact, much less because they would have to matter a lot less to dampen the fact that we can have millions of years of future. Instead, what’s going on is that we just aren’t thinking about it. We’re not really considering that our actions could have irrevocable effects over the long distant future. And when we do think about that, such as within environmentalism, it’s a very powerful idea. The idea that we shouldn’t sacrifice, we shouldn’t make irrevocable changes to the environment that could damage the entire future just for transient benefits to our time. And people think, “Oh, yeah, that is a powerful idea.”

So I think it’s more that they’re just not aware that there are a lot of situations like this. It’s not just the case of a particular ecosystem that could be an example of one of these important irrevocable losses, but there could be these irrevocable losses at this much grander scale affecting everything that we could ever achieve and do. I should also explain there that I do talk a lot about humanity in the book. And the reason I say this is not because I think that non-human animals don’t count or they don’t have intrinsic value, I do. It’s because instead, only humanity is responsive to reasons and to thinking about this. It’s not the case that chimpanzees will choose to save other species from extinction and will go out and work out how to safeguard them from natural disasters that could threaten their ecosystems or things like that.

We’re the only ones who are even in the game of considering moral choices. So in terms of the instrumental value, humanity has this massive instrumental value, because what we do could affect, for better or for worse, the intrinsic value of all of the other species. Other species are going to go extinct in about a billion years, basically, all of them when the earth becomes uninhabitable. Only humanity could actually extend that lifespan. So there’s this kind of thing where humanity ends up being key because we are the decision makers. We are the relevant agents or any other relevant agents will spring from us. That will be things that our descendants or things that we create and choose how they function. So, that’s the kind of role that we’re playing.

Lucas Perry: So if there are people who just simply care about the short term, if someone isn’t willing to buy into these arguments about the deep future or realizing the potential of humanity’s future, like “I don’t care so much about that, because I won’t be alive for that.” There’s also an argument here that these risks may be realized within their lifetime or within their children’s lifetime. Could you expand that a little bit?

Toby Ord: Yeah, in the precipice, when I try to think about why this matters. I think the most obvious reasons are rooted in the present. The fact that it will be terrible for all of the people who are alive at the time when the catastrophe strikes. That needn’t be the case. You could imagine things that meet my definition of an existential catastrophe that it would cut off the future, but not be bad for the people who were alive at that time, maybe we all painlessly disappear at the end of our natural lives or something. But in almost all realistic scenarios that we’re thinking about, it would be terrible for all of the people alive at that time, they would have their lives cut short and witness the downfall of everything that they’ve ever cared about and believed in.

That’s a very obvious natural reason, but the reason that moves me the most is thinking about our long-term future, and just how important that is. This huge scale of everything that we could ever become. And you could think of that in very numerical terms or you could just think back over time and how far humanity has come over these 200,000 years. Imagine that going forward and how small a slice of things our own lives are and you can come up with very intuitive arguments to exceed that as well. It doesn’t have to just be multiply things out type argument.

But then I also think that there are very strong arguments that you could also have rooted in our past and in other things as well. Humanity has succeeded and has got to where we are because of this partnership of the generations. Edmund Burke had this phrase. It’s something where, if we couldn’t promulgate our ideas and innovations to the next generation, what technological level would be like. It would be like it was in the Paleolithic time, even a crude iron shovel would be forever beyond our reach. It was only through passing down these innovations and iteratively improving upon them, we could get billions of people working in cooperation over deep time to build this world around us.

If we think about the wealth and prosperity that we have the fact that we live as long as we do. This is all because this rich world was created by our ancestors and handed on to us and we’re the trustees of this vast inheritance and if we would have failed, if we’d be the first of 10,000 generations to fail to pass this on to our heirs, we will be the worst of all of these generations. We’d have failed in these very important duties and these duties could be understood as some kind of reciprocal duty to those people in the past or we could also consider it as duties to the future rooted in obligations to people in the past, because we can’t reciprocate to people who are no longer with us. The only kind of way you can get this to work is to pay it forward and have this system where we each help the next generation with the respect for the past generations.

So I think there’s another set of reasons more deontological type reasons for it and you could all have the reasons I mentioned in terms of civilizational virtues and how that kind of approach rooted in being a more virtuous civilization or species and I think that that is a powerful way of seeing it as well, to see that we’re very impatient and imprudent and so forth and we need to become more wise or alternatively, Max Tegmark has talked about this and Martin Rees, Carl Sagan and others have seen it as something based on a cosmic significance of humanity, that perhaps in all of the stars and all of the galaxies of the universe, perhaps this is the only place where there is either life at all or we’re the only place where there’s intelligent life or consciousness. There’s different versions of this and that could make this exceptionally important place and this very rare thing that could be forever gone.

So I think that there’s a whole lot of different reasons here and I think that previously, a lot of the discussion has been in a very technical version of the future directed one where people have thought, well, even if there’s only a tiny chance of extinction, our future could have 10 to the power of 30 people in it or something like that. There’s something about this argument that some people find it compelling, but not very many. I personally always found it a bit like a trick. It is a little bit like an argument that zero equals one where you don’t find it compelling, but if someone says point out the step where it goes wrong, you can’t see a step where the argument goes wrong, but you still think I’m not very convinced, there’s probably something wrong with this.

And then people who are not from the sciences, people from the humanities find it an actively alarming argument that anyone who would make moral decisions on the grounds of an argument like that. What I’m trying to do is to show that actually, there’s this whole cluster of justifications rooted in all kinds of principles that many people find reasonable and you don’t have to accept all of them by any means. The idea here is that if any one of these arguments works for you, then you can see why it is that you have reasons to care about not letting our future be destroyed in our time.

Lucas Perry: Awesome. So, there’s first this deontological argument about transgenerational duties to continue propagating the species and the projects and value which previous generations have cultivated. We inherit culture and art and literature and technology, so there is a duties-based argument to continue the stewardship and development of that. There is this cosmic significance based argument that says that consciousness may be extremely precious and rare, and that there is great value held in the balance here at the precipice on planet Earth and it’s important to guard and do the proper stewardship of that.

There is this short-term argument that says that there is some reasonable likelihood I think, total existential risk for the next century you put at one in six, which we can discuss a little bit more later, so that would also be very bad for us and our children and short-term descendants should that be realized in the next century. Then there is this argument about the potential of humanity in deep time. So I think we’ve talked a bit here about there being potentially large numbers of human beings in the future or our descendants or other things that we might find valuable, but I don’t think that we’ve touched on the part and change of quality.

There are these arguments on quantity, but there’s also I think, I really like how David Pearce puts it where he says, “One day we may have thoughts as beautiful as sunsets.” So, could you expand a little bit here this argument on quality that I think also feeds in and then also with regards to the digitalization aspect that may happen, that there are also arguments around subjective time dilation, which may lead to more better experience into the deep future. So, this also seems to be another important aspect that’s motivating for some people.

Toby Ord: Yeah. Humanity has come a long way and various people have tried to catalog the improvements in our lives over time. Often in history, this is not talked about, partly because history is normally focused on something of the timescale of a human life and things don’t change that much on that timescale, but when people are thinking about much longer timescales, I think they really do. Sometimes this is written off in history as Whiggish history, but I think that that’s a mistake.

I think that if you were to summarize the history of humanity in say, one page, I think that the dramatic increases in our quality of life and our empowerment would have to be mentioned. It’s so important. You probably wouldn’t mention the Black Death, but you would mention this. Yet, it’s very rarely talked about within history, but there are people talking about it and there are people who have been measuring these improvements. And I think that you can see how, say in the last 200 years, lifespans have more than doubled and in fact, even in the poorest countries today, lifespans are longer than they were in the richest countries 200 years ago.

We can now almost all read whereas very few people could read 200 years ago. We’re vastly more wealthy. If you think about this threshold we currently use of extreme poverty, it used to be the case 200 years ago that almost everyone was below that threshold. People were desperately poor and now almost everyone is above that threshold. There’s still so much more that we could do, but there have been these really dramatic improvements.

Some people seem to think that that story of well-being in our lives getting better, increasing freedoms, increasing empowerment of education and health, they think that that story runs somehow counter to their concern about existential risk that one is an optimistic story and one’s a gloomy story. Ultimately, what I’m thinking is that it’s because these trends seem to point towards very optimistic futures that would make it all the more important to ensure that we survive to reach such futures. If all the trends suggested that the future was just going to inevitably move towards a very dreary thing that had hardly any value in it, then I wouldn’t be that concerned about existential risk, so I think these things actually do go together.

And it’s not just in terms of our own lives that things have been getting better. We’ve been making major institutional reforms, so while there is regrettably still slavery in the world today, there is much less than there was in the past and we have been making progress in a lot of ways in terms of having a more representative and more just and fair world and there’s a lot of room to continue in both those things. And even then, a world that’s kind of like the best lives lived today, a world that has very little injustice or suffering, that’s still only a lower bound on what we could achieve.

I think one useful way to think about this is in terms of your peak experiences. These moments of luminous joy or beauty, the moments that you’ve been happiest, whatever they may be and you think about how much better they are than the typical moments. My typical moments are by no means bad, but I would trade hundreds or maybe thousands for more of these peak experiences, and that’s something where there’s no fundamental reason why we couldn’t spend much more of our lives at these peaks and have lives which are vastly better than our lives are today and that’s assuming that we don’t find even higher peaks and new ways to have even better lives.

It’s not just about the well-being in people’s lives either. If you have any kind of conception about the types of value that humanity creates, so much of our lives will be in the future, so many of our achievements will be in the future, so many of our societies will be in the future. There’s every reason to expect that these greatest successes in all of these different ways will be in this long future as well. There’s also a host of other types of experiences that might become possible. We know that humanity only has some kind of very small sliver of the space of all possible experiences. We see in a set of colors, this three-dimensional color space.

We know that there are animals that see additional color pigments, that can see ultraviolet, can see parts of reality that we’re blind to. Animals with magnetic sense that can sense what direction north is and feel the magnetic fields. What’s it like to experience things like that? We could go so much further exploring this space. If we can guarantee our future and then we can start to use some of our peak experiences as signposts to what might be experienceable, I think that there’s so much further that we could go.

And then I guess you mentioned the possibilities of digital things as well. We don’t know exactly how consciousness works. In fact, we know very little about how it works. We think that there’s some suggestive reasons to think that minds including consciousness are computational things such that we might be able to realize them digitally and then there’s all kinds of possibilities that would follow from that. You could slow yourself down like slow down the rate at which you’re computed in order to see progress zoom past you and kind of experience a dizzying rate of change in the things around you. Fast forwarding through the boring bits and skipping to the exciting bits one’s life if one was digital could potentially be immortal, have backup copies, and so forth.

You might even be able to branch into being two different people, have some choice coming up as to say whether to stay on earth or to go to this new settlement in the stars, and just split with one copy go into this new life and one staying behind or a whole lot of other possibilities. We don’t know if that stuff is really possible, but it’s just to kind of give a taste of how we might just be seeing this very tiny amount of what’s possible at the moment.

Lucas Perry: This is one of the most motivating arguments for me, the fact that the space of all possible minds is probably very large and deep and that the kinds of qualia that we have access to are very limited and the possibility of well-being not being contingent upon the state of the external world which is always in flux and is always impermanent, we’re able to have a science of well-being that was sufficiently well-developed such that well-being was information and decision sensitive, but not contingent upon the state of the external world that seems like a form of enlightenment in my opinion.

Toby Ord: Yeah. Some of these questions are things that you don’t often see discussed in academia, partly because there isn’t really a proper discipline that says that that’s the kind of thing you’re allowed to talk about in your day job, but it is the kind of thing that people are allowed to talk about in science fiction. Many science fiction authors have something more like space opera or something like that where the future is just an interesting setting to play out the dramas that we recognize.

But other people use the setting to explore radical, what if questions, many of which are very philosophical and some of which are very well done. I think that if you’re interested in these types of questions, I would recommend people read Diaspora by Greg Egan, which I think is the best and most radical exploration of this and at the start of the book, it’s a setting in a particular digital system with digital minds substantially in the future from where we are now that have been running much faster than the external world. Their lives lived thousands of times faster than the people who’ve remained flesh and blood, so culturally that vastly further on, and then you get to witness what it might be like to undergo various of these events in one’s life. And in the particular setting it’s in. It’s a world where physical violence is against the laws of physics.

So rather than creating utopia by working out how to make people better behaved, the longstanding project have tried to make us all act nicely and decently to each other. That’s clearly part of what’s going on, but there’s this extra possibility that most people hadn’t even thought about, where because it’s all digital. It’s kind of like being on a web forum or something like that, where if someone attempts to attack you, you can just make them disappear, so that they can no longer interfere with you at all. And it explores what life might be like in this kind of world where the laws of physics are consent based and you can just make it so that people have no impact on you if you’re not enjoying the kind of impact that they’re having is a fascinating setting to explore radically different ideas about the future, which very much may not come to pass.

But what I find exciting about these types of things is not so much that they’re projections of where the future will be, but that if you take a whole lot of examples like this, they span a space that’s much broader than you were initially thinking about for your probability distribution over where the future might go and they help you realize that there are radically different ways that it could go. This kind of expansion of your understanding about the space of possibilities, which is where I think it’s best as opposed to as a direct prediction that I would strongly recommend some Greg Egan for anyone who wants to get really into that stuff.

Lucas Perry: You sold me. I’m interested in reading it now. I’m also becoming mindful of our time here and have a bunch more questions I would like to get through, but before we do that, I also want to just throw out here. I’ve had a bunch of conversations recently on the question of identity and open individualism and closed individualism and empty individualism are some of the views here.

For the long-termist perspective, I think that it’s pretty much very or deeply informative for how much or how little one may care about the deep future or digital minds or our descendants in a million years or humans that are around a million years later. I think for many people who won’t be motivated by these arguments, they’ll basically just feel like it’s not me, so who cares? And so I feel like these questions on personal identity really help tug and push and subvert many of our commonly held intuitions about identity. So, sort of going off of your point about the potential of the future and how it’s quite beautiful and motivating.

A little funny quip or thought there is I’ve sprung into Lucas consciousness and I’m quite excited, whatever “I” means, for there to be like awakening into Dyson sphere consciousness in Andromeda or something, and maybe a bit of a wacky or weird idea for most people, but thinking more and more endlessly about the nature of personal identity makes thoughts like these more easily entertainable.

Toby Ord: Yeah, that’s interesting. I haven’t done much research on personal identity. In fact, the types of questions I’ve been thinking about when it comes to the book are more on how radical change would be needed before it’s no longer humanity, so kind of like the identity of humanity across time as opposed to the identity for a particular individual across time. And because I’m already motivated by helping others and I’m kind of thinking more about the question of why just help others in our own time as opposed to helping others across time. How do you direct your altruism, your altruistic impulses?

But you’re right that they could also be possibilities to do with individuals lasting into the future. There’s various ideas about how long we can last with lifespans extending very rapidly. It might be that some of the people who are alive now actually do directly experience some of this long-term future. Maybe there are things that could happen where their identity wouldn’t be preserved, because it’d be too radical a break. You’d become two different kinds of being and you wouldn’t really be the same person, but if being the same person is important to you, then maybe you could make smaller changes. I’ve barely looked into this at all. I know Nick Bostrom has thought about it more. There’s probably lots of interesting questions there.

Lucas Perry: Awesome. So could you give a short overview of natural or non-anthropogenic risks over the next century and why they’re not so important?

Toby Ord: Yeah. Okay, so the main natural risks I think we’re facing are probably asteroid or comet impacts and super volcanic eruptions. In the book, I also looked at stellar explosions like supernova and gamma ray bursts, although since I estimate the chance of us being wiped out by one of those in the next 100 years to be one in a billion, we don’t really need to worry about those.

But asteroids, it does appear that the dinosaurs were destroyed 65 million years ago by a major asteroid impact. It’s something that’s been very well studied scientifically. I think the main reason to think about it is A, because it’s very scientifically understood and B, because humanity has actually done a pretty good job on it. We only worked out 40 years ago that the dinosaurs were destroyed by an asteroid and that they could be capable of causing such a mass extinction. In fact, it was only in 1960, 60 years ago that we even confirmed that craters on the Earth’s surface were caused by asteroids. So we knew very little about this until recently.

And then we’ve massively scaled up our scanning of the skies. We think that in order to cause a global catastrophe, the asteroid would probably need to be bigger than a kilometer across. We’ve found about 95% of the asteroids between 1 and 10 kilometers across, and we think we’ve found all of the ones bigger than 10 kilometers across. We therefore know that since none of the ones were found are on a trajectory to hit us within the next 100 years that it looks like we’re very safe from asteroids.

Whereas super volcanic eruptions are much less well understood. My estimate for those for the chance that we could be destroyed in the next 100 years by one is about one in 10,000. In the case of asteroids, we have looked into it so carefully and we’ve managed to check whether any are coming towards us right now, whereas it can be hard to get these probabilities further down until we know more, so that’s why my what about the super volcanic corruptions is where it is. That the Toba eruption was some kind of global catastrophe a very long time ago, though the early theories that it might have caused a population bottleneck and almost destroyed humanity, they don’t seem to hold up anymore. It is still illuminating of having continent scale destruction and global cooling.

Lucas Perry: And so what is your total estimation of natural risk in the next century?

Toby Ord: About one in 10,000. All of these estimates are in order of magnitude estimates, but I think that it’s about the same level as I put the super volcanic eruption and the other known natural risks I would put as much smaller. One of the reasons that we can say these low numbers is because humanity has survived for 2000 centuries so far, and related species such as Homo erectus have survived for even longer. And so we just know that there can’t be that many things that could destroy all humans on the whole planet from these natural risks,

Lucas Perry: Right, the natural conditions and environment hasn’t changed so much.

Toby Ord: Yeah, that’s right. I mean, this argument only works if the risk has either been constant or expectably constant, so it could be that it’s going up and down, but we don’t know which then it will also work. The problem is if we have some pretty good reasons to think that the risks could be going up over time, then our long track record is not so helpful. And that’s what happens when it comes to what you could think of as natural pandemics, such as the coronavirus.

This is something where it’s got into humanity through some kind of human action, so it’s not exactly natural how it actually got into humanity in the first place and then its spread through humanity through airplanes, traveling to different continents very quickly, is also not natural and is a faster spread than you would have had over this long-term history of humanity. And thus, these kind of safety arguments don’t count as well as they would for things like asteroid impacts.

Lucas Perry: This class of risks then is risky, but less risky than the human-made risks, which are a result of technology, the fancy x-risk jargon for this is anthropogenic risks. Some of these are nuclear weapons, climate change, environmental damage, synthetic bio-induced pandemics or AI-enabled pandemics, unaligned artificial intelligence, dystopian scenarios and other risks. Could you say a little bit about each of these and why you view unaligned artificial intelligence as the biggest risk?

Toby Ord: Sure. Some of these anthropogenic risks we already face. Nuclear war is an example. What is particularly concerning is a very large scale nuclear war, such as between the U.S. and Russia and nuclear winter models have suggested that the soot from burning buildings could get lifted up into the stratosphere which is high enough that it wouldn’t get rained out, so it could stay in the upper atmosphere for a decade or more and cause widespread global cooling, which would then cause massive crop failures, because there’s not enough time between frosts to get a proper crop, and thus could lead to massive starvation and a global catastrophe.

Carl Sagan suggested it could potentially lead to our extinction, but the current people working on this, while they are very concerned about it, don’t suggest that it could lead to human extinction. That’s not really a scenario that they find very likely. And so even though I think that there is substantial risk of nuclear war over the next century, either an accidental nuclear war being triggered soon or perhaps a new Cold War, leading to a new nuclear war, I would put the chance that humanity’s potential is destroyed through nuclear war at about one in 1000 over the next 100 years, which is about where I’d put it for climate change as well.

There is debate as to whether climate change could really cause human extinction or a permanent collapse of civilization. I think the answer is that we don’t know. Similar with nuclear war, but they’re both such large changes to the world, these kind of unprecedentedly rapid and severe changes that it’s hard to be more than 99% confident that if that happens that we’d make it through and so this is difficult to eliminate risk that remains there.

In the book, I look at the very worst climate outcomes, how much carbon is there in the methane clathrates under the ocean and in the permafrost? What would happen if it was released? How much warming would there be? And then what would happen if you had very severe amounts of warming such as 10 degrees? And I try to sketch out what we know about those things and it is difficult to find direct mechanisms that suggests that we would go extinct or that we would collapse our civilization in a way from which you could never be restarted again, despite the fact that civilization arose five times independently in different parts of the worlds already, so we know that it’s not like a fluke to get it started again. So it’s difficult to see the direct reasons why it could happen, but we don’t know enough to be sure that it can’t happen. In my sense, that’s still an existential risk.

Then I also have a kind of catch all for other types of environmental damage, all of these other pressures that we’re putting on the planet. I think that it would be too optimistic to be sure that none of those could potentially cause a collapse from which we can never recover as well. Although when I look at particular examples that are suggested, such as the collapse of pollinating insects and so forth, for the particular things that are suggested, it’s hard to see how they could cause this, so it’s not that I am just seeing problems everywhere, but I do think that there’s something to this general style of argument that unknown effects of the stressors we’re putting on the planet could be the end for us.

So I’d put all of those kind of current types of risks at about one in 1,000 over the next 100 years, but then it’s the anthropogenic risks from technologies that are still on the horizon that scare me the most and this would be in keeping with this idea of humanity’s continued exponential growth in power where you’d expect the risks to be escalating every century. And I think that the ones that I’m most concerned about, in particular, engineered pandemics and the risk of unaligned artificial intelligence.

Lucas Perry: All right. I think listeners will be very familiar with many of the arguments around why unaligned artificial intelligence is dangerous, so I think that we could skip some of the crucial considerations there. Could you touch a little bit then on the risks of engineered pandemics, which may be more new and then give a little bit of your total risk estimate for this class of risks.

Toby Ord: Ultimately, we do have some kind of a safety argument in terms of the historical record when it comes to these naturally arising pandemics. There are ways that they could be more dangerous now than they could have been in the past, but there are also many ways in which they’re less dangerous. We have antibiotics. We have the ability to detect in real time these threats, sequence the DNA of the things that are attacking us, and then use our knowledge of quarantine and medicine in order to fight them. So we have reasons to look to our safety on that.

But there are cases of pandemics or pandemic pathogens being created to be even more spreadable or even more deadly than those that arise naturally because the natural ones are not being optimized to be deadly. The deadliness is only if that’s in service of them spreading and surviving and normally killing your host is a big problem for that. So there’s room there for people to try to engineer things to be worse than the natural ones.

One case is scientists looking to fight disease, like Ron Fouchier with the bird flu, deliberately made a more infectious version of it that could be transmitted directly from mammal to mammal. He did that because he was trying to help, but it was, I think, very risky and I think a very bad move and most of the scientific community didn’t think it was a good idea. He did it in a bio safety level three enhanced lab, which is not the highest level of biosecurity, that’s BSL four, and even at the highest level, there have been an escape of a pathogen from a BSL four facility. So these labs aren’t safe enough, I think, to be able to work on newly enhanced things that are more dangerous than anything that nature can create in a world where so far the biggest catastrophes that we know of were caused by pandemics. So I think that it’s pretty crazy to be working on such things until we have labs from which nothing has ever escaped.

But that’s not what really worries me. What worries me more is bio weapons programs and there’s been a lot of development of bio weapons in the 20th Century, in particular. The Soviet Union reportedly had 20 tons of smallpox that they had manufactured for example, and they had an accidental release of smallpox, which killed civilians in Russia. They had an accidental release of anthrax, blowing it out across the whole city and killing many people, so we know from cases like this, that they had a very large bioweapons program. And the Biological Weapons Convention, which is the leading institution at an international level to prohibit bio weapons is chronically underfunded and understaffed. The entire budget of the BWC is less than that of a typical McDonald’s.

So this is something where humanity doesn’t have its priorities in order. Countries need to work together to step that up and to give it more responsibilities, to actually do inspections and make sure that none of them are using bio weapons. And then I’m also really concerned by the dark side of the democratization of biotechnology. The fact that rapid developments that we make with things like Gene Drives and CRISPR. These two huge breakthroughs. They’re perhaps Nobel Prize worthy. That in both cases within two years, they are replicated by university students in science competitions.

So we now have a situation where two years earlier, there’s like one person in the world who could do it or no one who could do it, then one person and then within a couple of years, we have perhaps tens of thousands of people who could do it, soon millions. And so if that pool of people eventually includes people like those in the Aum Shinrikyo cults that was responsible for the Sarin gas in the Tokyo subway, who actively one of their goals was to destroy everyone in the world. Once enough people can do these things and could make engineered pathogens, you’ll get someone with this terrible but massively rare motivation, or perhaps even just a country like North Korea who wants to have a kind of blackmail policy to make sure that no one ever invades. That’s why I’m worried about that. These rapid advances are empowering us to make really terrible weapons.

Lucas Perry: All right, so wrapping things up here. How do we then safeguard the potential for humanity and Earth-originating intelligent life? You seem to give some advice on high level strategy, policy and individual level advice, and this is all contextualized within this grand plan for humanity, which is that we reach existential security by getting to a place where existential risk is decreasing every century that we then enter a period of long reflection to contemplate and debate what is good and how we might explore the universe and optimize it to express that good and then that we execute that and achieve our potential. So again, how do we achieve all this, how do we mitigate x-risk, how do we safeguard the potential of humanity?

Toby Ord: That’s an easy question to end on. So what I tried to do in the book is to try to treat this at a whole lot of different levels. You kind of refer to the most abstract level to some extent, the point of that abstract level is to show that we don’t need to get ultimate success right now, we don’t need to solve everything, we don’t need to find out what the fundamental nature of goodness is, and what worlds would be the best. We just need to make sure we don’t end up in the ones which are clearly among the worst.

The point of looking further onwards with the strategy is just to see that we can set some things aside for later. Our task now is to reach what I call existential security and that involves this idea that will be familiar to many people to do with existential risk, which is to look at particular risks and to work out how to manage them, and to avoid falling victim to them, perhaps by being more careful with technology development, perhaps by creating our protective technologies. For example, better bio surveillance systems to understand if bio weapons have been launched into the environment, so that we could contain them much more quickly or to develop say a better work on alignment with AI research.

But it also involves not just fighting fires, but trying to become the kind of society where we don’t keep lighting these fires. I don’t mean that we don’t develop the technologies, but that we build in the responsibility for making sure that they do not develop into existential risks as part of the cost of doing business. We want to get the fruits of all of these technologies, both for the long-term and also for the short-term, but we need to be aware that there’s this shadow cost when we develop new things, and we blaze forward with technology. There’s shadow cost in terms of risk, and that’s not normally priced in. We just kind of ignore that, but eventually it will come due. If we keep developing things that produce these risks, eventually, it’s going to get us.

So what we need to do to develop our wisdom, both in terms of changing our common sense conception of morality, to take this long-term future seriously or our debts to our ancestors seriously, and we also need the international institutions to help avoid some of these tragedies of the commons and so forth as well, to find these cases where we’d all be prepared to pay the cost to get the security if everyone else was doing it, but we’re not prepared to just do it unilaterally. We need to try to work out mechanisms where we can all go into it together.

There are questions there in terms of policy. We need more policy-minded people within the science and technology space. People with an eye to the governance of their own technologies. This can be done within professional societies, but also we need more technology-minded people in the policy space. We often are bemoan the fact that a lot of people in government don’t really know much about how the internet works or how various technologies work, but part of the problem is that the people who do know about how these things work, don’t go into government. It’s not just that you can blame the people in government for not knowing about your field. People who know about this field, maybe some of them should actually work in policy.

So I think we need to build that bridge from both sides and I suggest a lot of particular policy things that we could do. A good example in terms of how concrete and simple it can get is that we renew the New START Disarmament Treaty. This is due to expire next year. And as far as I understand, the U.S. government and Russia don’t have plans to actually renew this treaty, which is crazy, because it’s one of the things that’s most responsible for the nuclear disarmament. So, making sure that we sign that treaty again, it is a very actionable point that people can kind of motivate around and so on.

And I think that there’s stuff for everyone to do. We may think that existential risk is too abstract and can’t really motivate people in the way that some other causes can, but I think that would be a mistake. I’m trying to sketch a vision of it in this book that I think can have a larger movement coalesce around it and I think that if we look back a bit when it came to nuclear war, the largest protest in America’s history at that time was against nuclear weapons in Central Park in New York and it was on the grounds that this could be the end of humanity. And that the largest movement at the moment, in terms of standing up for a cause is on climate change and it’s motivated by exactly these ideas about irrevocable destruction of our heritage. It really can motivate people if it’s expressed the right way. And so that actually fills me with hope that things can change.

And similarly, when I think about ethics, and I think about how in the 1950s, there was almost no consideration of the environment within their conception of ethics. It just was considered totally outside of the domain of ethics or morality and not really considered much at all. And the same with animal welfare, it was scarcely considered to be an ethical question at all. And now, these are both key things that people are taught in their moral education in school. And we have an entire ministry for the environment and that was within 10 years of Silent Spring coming out, I think all, but one English speaking country had a cabinet level position on the environment.

So, I think that we really can have big changes in our ethical perspective, but we need to start an expansive conversation about this and start unifying these things together not to be just like the anti-nuclear movement and the anti-climate change movement where it’s fighting a particular fire, but to be aware that if we want to actually get out there preemptively for these things that we need to expand that to this general conception of existential risk and safeguarding humanity’s long-term potential, but I’m optimistic that we can do that.

That’s why I think my best guess is that there’s a one in six chance that we don’t make it through this Century, but the other way around, I’m saying there’s a five in six chance that I think we do make it through. If we really played our cards right, we could make it a 99% chance that we make it through this Century. We’re not hostages to fortune. We humans get to decide what the future of humanity will be like. There’s not much risk from external forces that we can’t deal with such as the asteroids. Most of the risk is of our own doing and we can’t just sit here and bemoan the fact we’re in some difficult prisoner’s dilemma with ourselves. We need to get out and solve these things and I think we can.

Lucas Perry: Yeah. This point on moving from these particular motivation and excitement around climate change and nuclear weapons issues to a broader civilizational concern with existential risk seems to be a crucial and key important step in developing the kind of wisdom that we talked about earlier. So yeah, thank you so much for coming on and thanks for your contribution to the field of existential risk with this book. It’s really wonderful and I recommend listeners read it. If listeners are interested in that, where’s the best place to pick it up? How can they follow you?

Toby Ord: You could check out my website at tobyord.com. You could follow me on Twitter @tobyordoxford or I think the best thing is probably to find out more about the book at theprecipice.com. On that website, we also have links as to where you can buy it in your country, including at independent bookstores and so forth.

Lucas Perry: All right, wonderful. Thanks again, for coming on and also for writing this book. I think that it’s really important for helping to shape the conversation in the world and understanding around this issue and I hope we can keep nailing down the right arguments and helping to motivate people to care about these things. So yeah, thanks again for coming on.

Toby Ord: Well, thank you. It’s been great to be here.

AI Alignment Podcast: On Lethal Autonomous Weapons with Paul Scharre

 Topics discussed in this episode include:

  • What autonomous weapons are and how they may be used
  • The debate around acceptable and unacceptable uses of autonomous weapons
  • Degrees and kinds of ways of integrating human decision making in autonomous weapons 
  • Risks and benefits of autonomous weapons
  • An arms race for autonomous weapons
  • How autonomous weapons issues may matter for AI alignment and long-term AI safety

Timestamps: 

0:00 Intro

3:50 Why care about autonomous weapons?

4:31 What are autonomous weapons? 

06:47 What does “autonomy” mean? 

09:13 Will we see autonomous weapons in civilian contexts? 

11:29 How do we draw lines of acceptable and unacceptable uses of autonomous weapons? 

24:34 Defining and exploring human “in the loop,” “on the loop,” and “out of loop” 

31:14 The possibility of generating international lethal laws of robotics

36:15 Whether autonomous weapons will sanitize war and psychologically distance humans in detrimental ways

44:57 Are persons studying the psychological aspects of autonomous weapons use? 

47:05 Risks of the accidental escalation of war and conflict 

52:26 Is there an arms race for autonomous weapons? 

01:00:10 Further clarifying what autonomous weapons are

01:05:33 Does the successful regulation of autonomous weapons matter for long-term AI alignment considerations?

01:09:25 Does Paul see AI as an existential risk?

 

We hope that you will continue to join in the conversations by following us or subscribing to our podcasts on Youtube, Spotify, SoundCloud, iTunes, Google Play, StitcheriHeartRadio, or your preferred podcast site/application. You can find all the AI Alignment Podcasts here.

You can listen to the podcast above or read the transcript below. 

Lucas Perry: Welcome to the AI Alignment Podcast. I’m Lucas Perry. Today’s conversation is with Paul Scharre and explores the issue of lethal autonomous weapons. And so just what is the relation of lethal autonomous weapons and the related policy and governance issues to AI alignment and long-term AI risk? Well there’s a key question to keep in mind throughout this entire conversation and it’s that: if we cannot establish a governance mechanism as a global community on the concept that we should not let AI make the decision to kill, then how can we deal with more subtle near term issues and eventual long term safety issues about AI systems? This question is aimed at exploring the idea that autonomous weapons and their related governance represent a possibly critical first step on the international cooperation and coordination of global AI issues. If we’re committed to developing beneficial AI and eventually beneficial AGI then how important is this first step in AI governance and what precedents and foundations will it lay for future AI efforts and issues? So it’s this perspective that I suggest keeping in mind throughout the conversation. And many thanks to FLI’s Emilia Javorsky for much help on developing the questions for this podcast. 

Paul Scharre is a Senior Fellow and Director of the Technology and National Security Program at the Center for a New American Security. He is the award-winning author of Army of None: Autonomous Weapons and the Future of War, which won the 2019 Colby Award and was named one of Bill Gates’ top five books of 2018.

Mr. Scharre worked in the Office of the Secretary of Defense (OSD) where he played a leading role in establishing policies on unmanned and autonomous systems and emerging weapons technologies. Mr. Scharre led the DoD working group that drafted DoD Directive 3000.09, establishing the Department’s policies on autonomy in weapon systems. Mr. Scharre also led DoD efforts to establish policies on intelligence, surveillance, and reconnaissance (ISR) programs and directed energy technologies. He was involved in the drafting of policy guidance in the 2012 Defense Strategic Guidance, 2010 Quadrennial Defense Review, and Secretary-level planning guidance. His most recent position was Special Assistant to the Under Secretary of Defense for Policy. Prior to joining the Office of the Secretary of Defense, Mr. Scharre served as a special operations reconnaissance team leader in the Army’s 3rd Ranger Battalion and completed multiple tours to Iraq and Afghanistan.

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And with that, here’s my conversion with Paul Scharre. 

All right. So we’re here today to discuss your book, Army of None, and issues related to autonomous weapons in the 21st century. To start things off here, I think we can develop a little bit of the motivations for why this matters. Why should the average person care about the development and deployment of lethal autonomous weapons?

Paul Scharre: I think the most basic reason is because we all are going to live in the world that militaries are going to be deploying future weapons. Even if you don’t serve in the military, even if you don’t work on issues surrounding say, conflict, this kind of technology could affect all of us. And so I think we all have a stake in what this future looks like.

Lucas Perry: Let’s clarify a little bit more about what this technology actually looks like then. Often in common media, and for most people who don’t know about lethal autonomous weapons or killer robots, the media often portrays it as a terminator like scenario. So could you explain why this is wrong, and what are more accurate ways of communicating with the public about what these weapons are and the unique concerns that they pose?

Paul Scharre: Yes, I mean, the Terminator is like the first thing that comes up because it’s such a common pop culture reference. It’s right there in people’s minds. So I think go ahead and for the listeners, imagine that humanoid robot in the Terminator, and then just throw that away, because that’s not what we’re talking about. Let me make a different comparison. Self-driving cars. We are seeing right now the evolution of automobiles that with each generation of car incorporate more autonomous features: parking, intelligent cruise control, automatic braking. These increasingly autonomous features in cars that are added every single year, a little more autonomy, a little more autonomy, are taking us down at some point in time to a road of having fully autonomous cars that would drive themselves. We have something like the Google car where there’s no steering wheel at all. People are just passengers along for the ride. We’re seeing something very similar happen in the military with each generation of robotic systems and we now have air and ground and undersea robots deployed all around the world in over 100 countries and non state groups around the globe with some form of drones or robotic systems, and with each generation they’re becoming increasingly autonomous.

Now, the issue surrounding autonomous weapons is, what happens when a predator drone has as much autonomy as a self-driving car? What happens when you have a weapon that’s out in the battlefield, and it’s making its own decisions about whom to kill? Is that something that we’re comfortable with? What are the legal and moral and ethical ramifications of this? And the strategic implications? What might they do for the balance of power between nations, or stability among countries? These are really the issues surrounding autonomous weapons, and it’s really about this idea that we might have, at some point of time and perhaps the not very distant future, machines making their own decisions about whom to kill on the battlefield.

Lucas Perry: Could you unpack a little bit more about what autonomy really is or means because it seems to me that it’s more like an aggregation of a bunch of different technologies like computer vision and image recognition, and other kinds of machine learning that are aggregated together. So could you just develop a little bit more about where we are in terms of the various technologies required for autonomy?

Paul Scharre: Yes, so autonomy is not really a technology, it’s an attribute of a machine or of a person. And autonomy is about freedom. It’s the freedom that a machine or a person is given to perform some tasks in some environment for some period of time. As people, we have very little autonomy as children and more autonomy as we grow up, we have different autonomy in different settings. In some work environments, there might be more constraints put on you; what things you can and cannot do. And it’s also environment-specific and task-specific. You might have autonomy to do certain things, but not other things. It’s the same with machines. We’re ultimately talking about giving freedom to machines to perform certain actions under certain conditions in certain environments.

There are lots of simple forms of autonomy that we interact with all the time that we sort of take for granted. A thermostat is a very simple autonomous system, it’s a machine that’s given a freedom to decide… decide, let’s put that in air quotes, because we come back to what it means for machines to decide. But basically, the thermostat is given the ability to turn on and off the heat and air conditioning based on certain parameters that a human sets, a desired temperature, or if you have a programmable thermostat, maybe the desired temperature at certain times a day or days of the week, is a very bounded kind of autonomy. And that’s what we’re talking about for any of these machines. We’re not talking about freewill, or whether the machine develops consciousness. That’s not a problem today, maybe someday, but certainly not with the machines we’re talking about today. It’s a question really of, how much freedom do we want to give machines, or in this case, weapons operating on the battlefield to make certain kinds of choices?

Now we’re still talking about weapons that are designed by people, built by people, launched by people, and put into the battlefields to perform some mission, but there might be a little bit less human control than there is today. And then there are a whole bunch of questions that come along with that, like, is it going to work? Would it be effective? What happens if there are accidents? Are we comfortable with seeding that degree of control over to the machine?

Lucas Perry: You mentioned the application of this kind of technology in the context of battlefields. Is there also consideration and interest in the use of lethal autonomous weapons in civilian contexts?

Paul Scharre: Yes, I mean, I think there’s less energy on that topic. You certainly see less of a poll from the police community. I mean, I don’t really run into people in a police or Homeland Security context, saying we should be building autonomous weapons. Well, you will hear that from militaries. Oftentimes, groups that are concerned about the humanitarian consequences of autonomous weapons will raise that as a concern. There’s both what might militaries do in the battlefield, but then there’s a concern about proliferation. What happens when the technology proliferates, and it’s being used for internal security issues, could be a dictator, using these kinds of weapons to repress the population. That’s one concern. And that’s, I think, a very, very valid one. We’ve often seen one of the last checks against dictators, is when they tell their internal security forces to fire on civilians, on their own citizens. There have been instances where the security forces say, “No, we won’t.” That doesn’t always happen. Of course, tragically, sometimes security forces do attack their citizens. We saw in the massacre in Tiananmen Square that Chinese military troops are willing to murder Chinese citizens. But we’ve seen other instances, certainly in the fall of the Eastern Bloc at the end of the Cold War, that security forces… these are our friends, these are our family. We’re not going to kill them.

And autonomous weapons could take away one of those checks on dictators. So I think that’s a very valid concern. And that is a more general concern about the proliferation of military technology into policing even here in America. We’ve seen this in the last 20 years, is a lot of military tech ends up being used by police forces in ways that maybe isn’t appropriate. And so that’s, I think, a very valid and legitimate sort of concern about… even if this isn’t kind of the intended use, what would that look like and what are the risks that could come with that, and how should we think about those kinds of issues as well?

Lucas Perry: All right. So we’re developing autonomy in systems and there’s concern about how this autonomy will be deployed in context where lethal force or force may be used. So the question then arises and is sort of the question at the heart of lethal autonomous weapons: Where is it that we will draw a line between acceptable and unacceptable uses of artificial intelligence in autonomous weapons or in the military, or in civilian policing? So I’m curious to know how you think about where to draw those lines or that line in particular, and how you would suggest to any possible regulators who might be listening, how to think about and construct lines of acceptable and unacceptable uses of AI.

Paul Scharre: That’s a great question. So I think let’s take a step back first and sort of talk about, what would be the kinds of things that would make uses acceptable or unacceptable. Let’s just talk about the military context just to kind of bound the problem for a second. So in the military context, you have a couple reasons for drawing lines, if you will. One is legal issues, legal concerns. We have a legal framework to think about right and wrong in war. It’s called the laws of war or international humanitarian law. And it lays out a set of parameters for what is acceptable and what… And so that’s one of the places where there has been consensus internationally, among countries that come together at the United Nations through the Convention on Certain Conventional Weapons, the CCW, the process, we’ve had conversations going on about autonomous weapons.

One of the points of consensus among nations is that existing international humanitarian law or the laws of war would apply to autonomous weapons. And that any uses of autonomy in weapons, those weapons have to be used in a manner that complies with the laws of war. Now, that may sound trivial, but it’s a pretty significant point of agreement and it’s one that places some bounds on things that you can or cannot do. So, for example, one of the baseline principles of the laws of war is the principle of distinction. Military forces cannot intentionally target civilians. They can only intentionally target other military forces. And so any use of force these people to comply with this distinction, so right off the bat, that’s a very important and significant one when it comes to autonomous weapons. So if you have to use a weapon that could not be used in a way to comply with this principle of distinction, it would be illegal under the laws war and you wouldn’t be able to build it.

And there are other principles as well, principles about proportionality, and ensuring that any collateral damage that affects civilians or civilian infrastructure is not disproportionate to the military necessity of the target that is being attacked. There are principles about avoiding unnecessary suffering of combatants. Respecting anyone who’s rendered out of combat or the appropriate term is “hors de combat,” who surrendered have been incapacitated and not targeting them. So these are like very significant rules that any weapon system, autonomous weapon or not, has to comply with. And any use of any weapon has to comply with, any use of force. And so that is something that constrains considerably what nations are permitted to do in a lawful fashion. Now do people break the laws of war? Well, sure, that happens. We’re seeing that happen in Syria today, Bashar al-Assad is murdering civilians, there are examples of Rogue actors and non state terrorist groups and others that don’t care about respecting the laws of war. But those are very significant bounds.

Now, one could also say that there are more bounds that we should put on autonomous weapons that might be moral or ethical considerations that exist outside the laws of war, that aren’t written down in a formal way in the laws of war, but they’re still important and I think those often come to the fore with this topic. And there are other ones that might apply in terms of reasons why we might be concerned about stability among nations. But the laws of war, at least a very valuable starting point for this conversation about what is acceptable and not acceptable. I want to make clear, I’m not saying that the laws of war are insufficient, and we need to go beyond them and add in additional constraints. I’m actually not saying that. There are people that make that argument, and I want to give credit to their argument, and not pretend it doesn’t exist. I want the listeners to sort of understand the full scope of arguments about this technology. But I’m not saying myself that’s the case necessarily. But I do think that there are concerns that people raise.

For example, people might say it’s wrong for a machine to decide whom to kill, it’s wrong for a machine to make the decision about life and death. Now I think that’s an interesting argument. Why? Why is it wrong? Is it because we think the machine might get the answer wrong, it might perform not as well as the humans because I think that there’s something intrinsic about weighing the value of life and death that we want humans to do, and appreciating the value of another person’s life before making one of these decisions. Those are all very valid counter arguments that exist in this space.

Lucas Perry: Yes. So thanks for clarifying that. For listeners, it’s important here to clarify the difference where some people you’re saying would find the laws of war to be sufficient in the case of autonomous weapons, and some would not.

Paul Scharre: Yes, I mean, this is a hotly debated issue. I mean, this is in many ways, the crux of the issue surrounding autonomous weapons. I’m going to oversimplify a bit because you have a variety of different views on this, but you certainly have some people whose views are, look, we have a set of structures called the laws of war that tell us what right and wrong looks like and more. And most of the things that people are worried about are already prohibited under the laws of war. So for example, if what you’re worried about is autonomous weapons, running amok murdering civilians, that’s illegal under the laws of war. And so one of the points of pushback that you’ll sometimes get from governments or others to the idea of creating like an ad hoc treaty that would ban autonomous weapons or some class of autonomous weapons, is look, some of the things people worry about like they’re already prohibited under the laws of war, passing another law to say the thing that’s already illegal is now illegal again doesn’t add any value.

There’s group of arguments that says the laws of war dictate effects in the battlefield. So they dictate sort of what the end effect is, they don’t really affect the process. And there’s a line of reasoning that says, that’s fine. The process doesn’t matter. If someday we could use autonomous weapons in a way that was more humane and more precise than people, then we should use them. And just the same way that self-driving cars will someday save lives on roads by avoiding accidents, maybe we could build autonomous weapons that would avoid mistakes in war and accidentally targeting civilians, and therefore we should use them. And let’s just focus on complying better with the laws of war. That’s one school of thought.

Then there’s a whole bunch of reasons why you might say, well, that’s not enough. One reason might be, well, militaries’ compliance with the laws of war. Isn’t that great? Actually, like people talk a good game, but when you look at military practice, especially if the rules for using weapon are kind of convoluted, you have to take a bunch of additional steps in order to use it in a way that’s lawful, that kind of goes out the window in conflict. Real world and tragic historical example of this was experienced throughout the 20th century with landmines where land mines were permitted to be used lawfully, and still are, if you’re not a signatory to the Ottawa Convention, they’re permitted to be used lawfully provided you put in a whole bunch of procedures to make sure that minefields are marked and we know the location of minefields, so they can be demined after conflict.

Now, in practice, countries weren’t doing this. I mean, many of them were just scattering mines from the air. And so we had this horrific problem of millions of mines around the globe persisting after a conflict. The response was basically this global movement to ban mines entirely to say, look, it’s not that it’s inconceivable to use mines in a way that you mean, but it requires a whole bunch of additional efforts, that countries aren’t doing, and so we have to take this weapon away from countries because they are not actually using it in a way that’s responsible. That’s a school of thought with autonomous weapons. Is look, maybe you can conjure up thought experiments about how you can use autonomous weapons in these very specific instances, and it’s acceptable, but once you start any use, it’s a slippery slope, and next thing you know, it’ll be just like landmines all over again, and they’ll be everywhere and civilians will be being killed. And so the better thing to do is to just not let this process even start, and not letting militaries have access to the technology because they won’t use it responsibly, regardless of whether it’s theoretically possible. That’s a pretty reasonable and defensible argument. And there are other arguments too.

One could say, actually, it’s not just about avoiding civilian harm, but there’s something intrinsic about weighing the value of an enemy soldier’s life, that we want humans involved in that process. And that if we took humans away from that process, we’ll be losing something that sure maybe it’s not written down in the laws of war, but maybe it’s not written down because it was always implicit that humans will always be making these choices. And now that it’s decision in front of us, we should write this down, that humans should be involved in these decisions and should be weighing the value of the human life, even an enemy soldier. Because if we give that up, we might give up something that is a constraint on violence and war that holds back some of the worst excesses of violence, we might even can make something about ourselves. And this is, I think, a really tricky issue because there’s a cost to humans making these decisions. It’s a very real cost. It’s a cost in post traumatic stress that soldiers face and moral injury. It’s a cost in lives that are ruined, not just the people that are killed in a battlefield, but the people have to live with that violence afterwards, and the ramifications and even the choices that they themselves make. It’s a cost in suicides of veterans, and substance abuse and destroyed families and lives.

And so to say that we want humans to stay still evolved to be more than responsible for killing, is to say I’m choosing that cost. I’m choosing to absorb and acknowledge and take on the cost of post traumatic stress and moral injury, and also the burdens that come with war. And I think it’s worth reflecting on the fact that the burdens of war are distributed very unequally, not just between combatants, but also on the societies that fight. As a democratic nation in the United States, we make a decision as a country to go to war, through our elected representatives. And yet, it’s a very tiny slice of the population that bears the burden for that war, not just putting themselves at risk, but also carrying the moral burden of that afterwards.

And so if you say, well, I want there to be someone who’s going to live with that trauma for the rest of your life. I think that’s an argument that one can make, but you need to acknowledge that that’s real. And that’s not a burden that we all share equally, it’s a burden we’re placing on young women and men that we send off to fight on our behalf. The flip side is if we didn’t do that, if we fought a war and no one felt the moral burden of killing, no one slept uneasy at night afterwards, what would they say about us as a society? I think these are difficult questions. I don’t have easy answers to that. But I think these are challenging things for us to wrestle with.

Lucas Perry: Yes, I mean, there’s a lot there. I think that was a really good illustration of the different points of views on this. I hadn’t heard or considered much the implications of post traumatic stress. And I think moral burden, you called it that would be a factor in what autonomous weapons would relieve in countries which have the power to develop them. Speaking personally, I think I find the arguments most compelling about the necessity of having human beings integrated in the process of decision making with regards to killing, because if you remove that, then you’re removing the deep aspect of humanity, which sometimes does not follow the laws of war, which we currently don’t have complex enough preference learning techniques and machine learning techniques to actually train autonomous weapon systems in everything that human beings value and care about, and that there are situations where deviating from following the laws of war may be the best thing to do. I’m not sure if you have any thoughts about this, but I think you did a good job of illustrating all the different positions, and that’s just my initial reaction to it.

Paul Scharre: Yes, these are tricky issues. And so I think one of the things I want to try to do for listeners is try to lay out the landscape of what these arguments are, and some of the pros and cons of them because I think sometimes they will often oversimplify on all sides. The other people will be like, well, we should have humans involved in making these decisions. Well, humans involved where? If I get into a self-driving car that has no steering wheel, it’s not true that there’s no human involvement. The type of human involvement has just changed in terms of where it exists. So now, instead of manually driving the car, I’m still choosing the car’s destination, I’m still telling the car where I want to go. You’re going to get into the car and car take me wherever you want to go. So the type of human involvement is changed.

So what kind of human relationship do we want with decisions about life and death in the battlefield? What type of human involvement is right or necessary or appropriate and for what reason? For a legal reason, for a moral reason. These are interesting challenges. We haven’t had to confront anymore. These arguments I think unfairly get simplified on all sides. Conversely, you hear people say things like, it doesn’t matter, because these weapons are going to get built anyway. It’s a little bit overly simplistic in the sense that there are examples of successes in arms control. It’s hard to pull off. There are many examples of failures as well, but there are places where civilized nations have walked back from some technologies to varying degrees of success, whether it’s chemical weapons or biological weapons or other things. So what is success look like in constraining a weapon? Is it no one ever uses the weapon? Is it most nations don’t use it? It’s not used in certain ways. These are complicated issues.

Lucas Perry: Right. So let’s talk a little bit here about integrating human emotion and human reasoning and humanity itself into the autonomous weapon systems and the life or death decisions that they will be making. So hitting on a few concepts here, if you could help explain what people mean when they say human in the loop, and human on the loop, and how this relates to the integration of human control and human responsibility and human accountability in the use of autonomous weapons.

Paul Scharre: Let’s unpack some of this terminology. Broadly speaking, people tend to use the terms human in the loop, on the loop, or out of the loop to refer to semi autonomous weapons human is in the loop, which means that for any really semi autonomous process or system, the machine is taking an action and then it pauses and waits for humans to take a positive action before proceeding. A good example of a human in the loop system is the automated backups on your computer when they require you to push a button to say okay to do the backup now. They’re waiting music in action before proceeding. In a human on the loop system, or one where the supervisor control is one of the human doesn’t have to take any positive action for the system to proceed. The human can intervene, so the human can sit back, and if you want to, you can jump in.

Example of this might be your thermostat. When you’re in a house, you’ve already set the parameters, it’ll turn on the heat and air conditioning on its own, but if you’re not happy with the outcome, you could change it. Now, when you’re out of the house, your thermostat is operating in a fully autonomous fashion in this respect where humans out of the loop. You don’t have any ability to intervene for some period of time. It’s really all about time duration. For supervisory control, how much time does the human have to identify something is wrong and then intervene? So for example, things like the Tesla autopilots. That’s one where the human is in a supervisory control capacity. So the autopilot function in a car, the human doesn’t have to do anything, car’s driving itself, but they can intervene.

The problem with some of those control architectures is the time that you are permitting people to identify that there’s a problem, figure out what’s going on, decide to take action, intervene, really realistic before harm happens. Is it realistic that a human can be not paying attention, and then all of a sudden, identify that the car is in trouble and leap into action to avoid an accident when you’re speeding on the highway 70 miles an hour? And then you can see quite clearly in a number of fatal accidents with these autopilots, that that’s not feasible. People actually aren’t capable of doing that. So you’ve got to think about sort of what is the role of the human in this process? This is not just a semi autonomous or supervised autonomous or fully autonomous process. It’s one where the human is involved in some varying capacity.

And what are we expecting the human to do? Same thing with something that’s fully autonomous. We’re talking about a system that’s operating on its own for some period of time. How long before it checks back in with a person? What information is that person given? And what is their capacity to intervene or how bad could things go wrong when the person is not involved? And when we talk about weapons specifically. There are lots of weapons that operate in a semi autonomous fashion today where the human is choosing the target, but there’s a lot of automation in IDing targets presenting information to people in actually carrying out an attack, once the human has chosen a target, there are many, many weapons that are what the military calls fire and forget weapon, so once it’s launched, it’s not coming back. Those have been widely used for 70 years since World War Two. So that’s not new.

There are a whole bunch of weapons that operate in a supervisory autonomy mode, where humans on the loop. These are generally used in a more limited fashion for immediate localized defense of air bases or ships or ground vehicles defending against air or missile or rocket attack, particularly when the speed of these attacks might overwhelm people’s ability to respond. For humans to be in the loop, for humans to push a button, every time there’s a missile coming in, you could have so many missiles coming in so fast that you have to just simply activate an automatic defensive mode that will shoot down all have the missiles based on some pre-programmed parameters that humans put into the system. This exists today. The systems have been around for decades since the 1980s. And there were widespread use with at least 30 countries around the globe. So this is a type of weapon system that’s already in operation. These supervisory autonomous weapons. What really would be new would be fully autonomous weapons that operate on their own, whereas humans are still building them and launching them, but humans put them into operation, and then there’s some period of time where they were able to search a target area for targets and they were able to find these targets, and then based on some programming that was designed by people, identify the targets and attack them on their own.

Lucas Perry: Would you consider that out of the loop for that period of time?

Paul Scharre: Exactly. So over that period of time, humans are out of the loop on that decision over which targets they’re attacking. That would be potentially largely a new development in war. There are some isolated cases of some weapon systems that cross this line, by in large that would be new. That’s at least the starting point of what people might be concerned about. Now, you might envision things that are more advanced beyond that, but that’s sort of the near term development that could be on the horizon in the next five to 15 years, telling the weapon system, go into this area, fly around or search around underwater and find any ships of this type and attack them for some period of time in space. And that changes the human’s relationship with the use of force a little bit. It doesn’t mean the humans not involved at all, but the humans not quite as involved as they used to be. And is that something we’re comfortable with? And what are the implications of that kind of shift in warfare.

Lucas Perry: So the relevant things here are how this helps to integrate human control and human responsibility and human accountability into autonomous weapons systems. And just hearing you speak about all of that, it also seems like very relevant questions have to do with human psychology, about what human beings are actually likely to be able to do. And then also, I think you articulately put the practical question of whether or not people will be able to react to certain threats given certain situations. So in terms of trying to understand acceptable and unacceptable uses of autonomous weapons, that seems to supervene upon a lot of these facets of benefits and disadvantages of in the loop, on the loop, and out of the loop for different situations and different risks, plus how much we’re willing to automate killing and death and remove human decision making from some of these situations or not.

Paul Scharre: Yes, I mean, I think what’s challenging in this space is that it would be nice, it would be ideal if we could sort of reach agreement among nations for sort of a lethal laws of robotics, and Isaac Asimov’s books about robots you think of these three laws of robotics. Well, those laws aren’t going to work because one of them is not harming a human being and it’s not going to work in the military context, but could there be some agreement among countries for lethal laws of robots that would govern the behavior of autonomous systems in war, and it might sort of say, these are the things that are acceptable or not? Maybe. Maybe that’s possible someday. I think we’re not there yet at least, there are certainly not agreement as widespread disagreement among nations about what approach to take. But the good starting position of trying to understand what are the goals we want to achieve. And I think you’re right that we need to keep the human sort of front and center. But I this this is like a really important asymmetry between humans and machines that’s worth highlighting, which is to say that the laws of war government effects in the battlefield, and then in that sentence, the laws of war, don’t say the human has to pick every target, the laws of war say that the use of force must be executed according to certain principles of distinction and proportionality and other things.

One important asymmetry in the laws of war, however, is that machines are not legal agents. Only humans have legal agents. And so it’s ultimately humans that are responsible for complying with the laws of war. You can’t put a machine on trial for a war crime. It doesn’t make sense. It doesn’t have intentionality. So it’s ultimately a human responsibility to ensure this kind of compliance with the laws of war. It’s a good starting point then for conversation to try to understand if we start from that proposition that it’s a human responsibility to ensure compliance with the laws of war, then what follows from that? What balances that place on human involvement? One of the early parts of the conversations on autonomous weapons internationally came from this very technological based conversation. To say, well, based on the technology, draw these lines, you should put these limits in place. The problem with that approach is not that you can’t do it.

The problem is the state of the technology when? 2014 when discussions on autonomous weapons started at the very beginning of the deep learning revolution, today, in 2020, our estimate of whether technology might be in five years or 10 years or 50 years? The technology moving so quickly than any technologically based set of rules about how we should approach this problem and what is the appropriate use of machines versus human decision making in the use of force. Any technologically based answer is one that we may look back in 10 years or 20 years and say is wrong. We could get it wrong in the sense that we might be leaving valuable technological opportunities on the table and we’re banning technology that if we used it actually might make war more humane and reduce civilian casualties, or we might be permitting technologies that turned out in retrospect to be problematic, and we shouldn’t have done that.

And one of the things we’ve seen historically when you look at attempts to ban weapons is that ones that are technologically based don’t always fare very well over time. So for example, the early bans on poison gas banned the use of poison gas that are launched from artillery shells. It allowed actually poison gas administered via canisters, and so the first use of poison gas in World War One by the Germans was canister based, they actually just laid out little canisters and then open the valves. Now that turns out to be not very practical way of using poison gas in war, because you have someone basically on your side standing over this canister, opening a valve and then getting gassed. And so it’s a little bit tricky, but technically permissible.

One of the things that can be challenging is it’s hard to foresee how the technology is going to evolve. A better approach and one that we’ve seen the dialogue internationally sort of shift towards is our human-centered approach. To start from the position of the human and say, look, if we had all the technology in the world and war, what decisions would we want humans to make and why? Not because the technology cannot make decisions, but because it should not. I think it’s actually a very valuable starting place to understand a conversation, because the technology is moving so quickly.

What role do we want humans to play in warfare, and why do we think this is the case? Are there some tasks in war, or some decisions that we think are fundamentally human that should be decisions that only humans should make and we shouldn’t hand off to machines? I think that’s a really valuable starting position then to try to better interrogate how do we want to use this technology going forward? Because the landscape of technological opportunity is going to keep expanding. And so what do we want to do with this technology? How do we want to use it? And are there ways that we can use this technology that keeps humans in control of the use of force in the battlefield? Keep humans legally and morally and ethically responsible, but may make war more humane in the process, that may make war more precise, that may reduce civilian casualties without losing our humanity in the process.

Lucas Perry: So I guess the thought experiment, there would be like, if we had weapons that let us just delete people instantly without consequences, how would we want human decision making to be integrated with that? Reflecting on that also makes me consider this other point that I think is also important for my considerations around lethal autonomous weapons, which is the necessity of integrating human experience in the consequences of war, the pain and the suffering and the carnage and the PTSD as being almost necessary vehicles to some extent to make us tired of it to integrate how horrible it is. So I guess I would just be interested in integrating that perspective into it not just being about humans making decisions and the decisions being integrated in the execution process, but also about the experiential ramifications of being in relation to what actually happens in war and what violence is like and what happens in violence.

Paul Scharre: Well, I think that we want to unpack a little bit some of the things you’re talking about. Are we talking about ensuring that there is an accurate representation to the people carrying out the violence about what’s happening on the other end, that we’re not sanitizing things. And I think that’s a fair point. When we begin to put more psychological barriers between the person making the decision and the effects, it might be easier for them to carry out larger scale attacks, versus actually making war and more horrible. Now that’s a line of reasoning, I suppose, to say we should make war more horrible, so there’ll be less of it. I’m not sure we might get the outcome that there is less of it. We just might have more horrible war, but that’s a different issue. Those are more difficult questions.

I will say that I often hear philosophers raising things about skin in the game. I rarely hear them being raised by people who have had skin in the game, who have experienced up close in a personal way the horrors of war. And I’m less convinced that there’s a lot of good that comes from the tragedy of war. I think there’s value in us trying to think about how do we make war less terrible? How do we reduce civilian casualties? How do we have less war? But this often comes up in the context of technologies like we should somehow put ourselves at risk. No military does that, no military has ever done that in human history. The whole purpose of militaries getting technology in training is to get an advantage on the adversary. It’s not a fair fight. It’s not supposed to be, it’s not a boxing match. So these are things worth exploring. We need to come from the standpoint of the reality of what war is and not from a philosophical exercise about war might be, but deal with the realities of what actually occurs in the battlefield.

Lucas Perry: So I think that’s a really interesting point. And as someone with a background and interest in philosophy, it’s quite funny. So you do have experience in war, right?

Paul Scharre: Yes, I’ve fought in Iraq and Afghanistan.

Lucas Perry: Then it’s interesting for me, if you see this distinction between people who are actually veterans, who have experienced violence and carnage and tragedies of war, and the perspective here is that PTSD and associated trauma with these kinds of experiences, you find that they’re less salient for decreasing people’s willingness or decision to engage in further war. Is that your claim?

Paul Scharre: I don’t know. No, I don’t know. I don’t know the answer to that. I don’t know. That’s some difficult question for political scientists to figure out about voting preferences of veterans. All I’m saying is that I hear a lot of claims in this space that I think are often not very well interrogated or not very well explored. And there’s a real price that people pay for being involved. Now, people want to say that we’re willing to bear that price for some reason, like okay, but I think we should acknowledge it.

Lucas Perry: Yeah, that make sense. I guess the thing that I was just pointing at was it would be psychologically interesting to know if philosophers are detached from the experience, maybe they don’t actually know about the psychological implications of being involved in horrible war. And if people who are actually veterans disagree with philosophers about the importance of there being skin in the game, if philosophers say that skin in the game reduces willingness to be in war, if the claim is that that wouldn’t actually decrease their willingness to go to war. I think that seems psychologically very important and relevant, because there is this concern about how autonomous weapons and integrating human decision making to lethal autonomous weapons would potentially sanitize war. And so there’s the trade off between the potential mitigating effects of being involved in war, and then also the negative effects which are incurred by veterans who would actually have to be exposed by it and bring the trauma back for communities to have deeper experiential relation with.

Paul Scharre: Yes, and look, we don’t do that, right? We had a whole generation of veterans come back from Vietnam and we as society listen to the stories and understand them and understand, no. I have heard over the years people raise this issue whether it’s drones, autonomous weapons, this issue of having skin in the game either physically being at risk or psychologically. And I’ve rarely heard it raised by people who it’s been them who’s on the line. People often have very gut emotional reactions to this topic. And I think that’s valuable because it’s speaking to something that resonates with people, whether it’s an emotional reaction opposed to autonomous weapons, and that you often get that from many people that go, there’s something about this. It doesn’t feel right. I don’t like this idea. Or people saying, the opposite reaction. Other people that say that “wouldn’t this make war great, it’s more precise and more humane,” and which my reaction is often a little bit like… have you ever interacted with a computer? They break all the time. What are you talking about?

But all of these things I think they’re speaking to instincts that people have about this technology, but it’s worth asking questions to better understand, what is it that we’re reacting to? Is it an assumption about the technologies, is it an assumption about the nature of war? One of the concerns I’ve heard raised is like this will impersonalize war and create more distance between people killing. If you sort of buy that argument, that impersonal war is a bad thing, then you would say the greatest thing would be deeply personal war, like hand to hand combat. It appears to harken back to some glorious age of war when people looked each other in the eye and hacked each other to bits with swords, like real humans. That’s not that that war never occurred in human history. In fact, we’ve had conflicts like that, even in recent memory that involve hand to hand weapons. They tend not to be very humane conflicts. When we see civil violence, when people are murdering each other with machetes or garden tools or other things, it tends to be horrific communal violence, mass atrocities in Rwanda or Cambodia or other places. So I think it’s important to deal with the reality of what war is and not some fantasy.

Lucas Perry: Yes, I think that that makes a lot of sense. It’s really tricky. And the psychology around this I think is difficult and probably not studied enough.

Paul Scharre: There’s real war that occurs in the world, and then there’s the fantasy of war that we, as a society, tell ourselves when we go to movie theaters, and we watch stories about soldiers who are heroes, who conquer the bad guys. We’re told a fantasy, and it’s a fantasy as a society that allows society to perpetuate wars, that allows us to send young men and women off to die. And it’s not to say that there are no circumstances in which a nation might need to go to war to defend itself or its interest, but we sort of dress war up in these pretty clothes, and let’s not confuse that with the reality of what actually occurs. People said, well, through autonomous weapons, then we won’t have people sort of weighing the value of life and death. I mean, it happens sometimes, but it’s not like every time someone dies in war, that there was this thoughtful exercise where a committee sat around and said, “Do we really need to kill this person? Is it really appropriate?” There’s a lot of dehumanization that goes on on the battlefield. So I think this is what makes this issue very challenging. Many of the objections to autonomous weapons are objections to war. That’s what people are actually objecting to.

The question isn’t, is war bad? Of course war’s terrible? The question is sort of, how do we find ways going forward to use technology that may make war more precise and more humane without losing our humanity in the process, and are ways to do that? It’s a challenging question. I think the answer is probably yes, but it’s one that’s going to require a lot of interrogation to try to get there. It’s a difficult issue because it’s also a dynamic process where there’s an interplay between competitors. If we get this wrong, we can easily end up in a situation where there’s less human control, there’s more violence and war. There are lots of opportunities to make things worse as well.

If we could make war perfect, that would be great, in terms of no civilian suffering and reduce the suffering of enemy combatants and the number of lives lost. If we could push a button and make war go away, that would be wonderful. Those things will all be great. The more practical question really is, can we improve upon the status quo and how can we do so in a thoughtful way, or at least not make things worse than today? And I think those are hard enough problems to try to address.

Lucas Perry: I appreciate that you bring a very holistic, well-weighed perspective to the varying sides of this issue. So these are all very big and difficult. Are you aware of people actually studying whether some of these effects exist or not, and whether they would actually sanitize things or not? Or is this basically all just coming down to people’s intuitions and simulations in their head?

Paul Scharre: Some of both. There’s really great scholarship that’s being done on autonomous weapons, certainly there’s a robust array of legal based scholarship, people trying to understand how the law of war might interface with autonomous weapons. But there’s also been worked on by thinking about some of these human psychological interactions, Missy Cummings, who’s at Duke who runs the humans and automation lab down has done some work on human machine interfaces on weapon systems to think through some of these concerns. I think probably less attention paid to the human machine interface dimension of this and the human psychological dimension of it. But there’s been a lot of work done by people like Heather Roth, people at Article 36, and others thinking about concepts of meaningful human control and what might look like in weapon systems.

I think one of the things that’s challenging across the board in this issue is that it is a politically contentious topic. You have kind of levels of this debate going on, you have scholars trying to sort of understand the issue maybe, and then you also have a whole array of politically motivated groups, international organizations, civil society organizations, countries, duking it out basically, at the UN and in the media about where we should go with this technology. As you get a lot of motivated reasoning on all sides about what should the answer be. So for example, one of the things that fascinates me is i’ll often hear people say, autonomous weapons are terrible, and they’ll have a terrible outcome, and we need to ban them now. And if we just pass a treaty and we have enough political will we could ban them. I’ll also hear people say a ban would be pointless, it wouldn’t work. And anyways, wouldn’t autonomous weapons be great? There are other possible beliefs. One could say that a ban is feasible, but the weapons aren’t that big of a deal. So it just seems to me like there’s a lot of politically motivated reasoning that goes on this debate, which makes it very challenging.

Lucas Perry: So one of the concerns around autonomous weapons has to do with accidental escalation of warfare and conflict. Could you explore this point and explain what some strategies might be to prevent accidental escalation of warfare as AI is increasingly being used in the military?

Paul Scharre: Yes, so I think in general, you could bucket maybe concerns about autonomous weapons into two categories. One is a concern that they may not function very well and could have accidents, those accidents could lead to civilian casualties, that could lead to accidental escalation among nations and a crisis, military force forces operating in close proximity to one another and there could be accidents. This happens with people. And you might worry about actions with autonomous systems and maybe one shoots down an enemy aircraft and there’s an escalation and people are killed. And then how do you unwind that? How do you communicate to your adversary? We didn’t mean to do that. We’re sorry. How do you do that in a period of tension? That’s a particular challenge.

There’s a whole other set of challenges that come from the weapons might work. And that might get to some of these deeper questions about the role of humans in decision making about life and death. But this issue of accidental escalation kind of comes into the category of they don’t work very well, then they’re not reliable. And this is the case for a lot of AI and autonomous technology today, which isn’t to say it doesn’t work at all, if it didn’t work at all, it would be much easier. There’d be no debates about bias and facial recognition systems if they never identify faces. There’d be no debates about safety with self-driving cars if the car couldn’t go anywhere. The problem is that a lot of these AI based systems work very well in some settings, and then if the settings change ever so slightly, they don’t work very well at all anymore. And the performance can drop off very dramatically, and they’re not very robust to changes in environmental conditions. So this is a huge problem for the military, because in particular, the military doesn’t get to test its systems in its actual operating environment.

So you can take a car, and you can take it on the roads, and you can test it in an actual driving environment. And we’ve seen car companies rack up 10 million miles or more of driving data. And then they can go back and they can run simulations. So Waymo has said that they run 10 million miles of simulated driving every single day. And they can simulate in different lighting conditions, in different environmental conditions. Well, the military can build simulations too, but simulations of what? What will the next war look like? Well we don’t know because we haven’t fought it yet. The good news is that war’s very rare, which is great. But that also means that for these kinds of systems, we don’t necessarily know the operating conditions that they’ll be in, and so there is this real problem of this risk of accidents. And it’s exacerbated in the fact that this is also a very adversarial environment. So you actually have an enemy who’s trying to trick your system and manipulate it. That’s adds another layer of complications.

Driving is a little bit competitive, maybe somebody doesn’t want to let you into the lane, but the pedestrians aren’t generally trying to get hit by cars. That’s a whole other complication in the military space. So all of that leads to concerns that the systems may do okay in training, and then we take them out in the real world, and they fail and they fail a pretty bad way. If it’s a weapon system that is making its own decisions about whom to kill, it could be that it fails in a benign way, then it targets nothing. And that’s a problem for the military who built it, or fails in a more hazardous way, in a dangerous way and attacks the wrong targets. And when we’re talking about an autonomous weapon, the essence of this autonomous weapon is making its own decisions about which targets to attack and then carrying out those attacks. If you get that wrong, those could be pretty significant consequences with that. One of those things could be civilian harm. And that’s a major concern. There are processes in place for printing that operationally and test and evaluation, are those sufficient? I think they’re good reasons to say that maybe they’re not sufficient or not completely sufficient, and they need to be revised or improved.

And I’ll point out, we can come back to this that the US Defense Department actually has a more stringent procedure in place for reviewing autonomous weapons more than other weapons, beyond what the laws of war have, the US is one of the few countries that has this. But then there’s also question about accidental escalation, which also could be the case. Would that lead to like an entire war? Probably not. But it could make things a lot harder to defuse tensions in a crisis, and that could be problematic. So we just had an incident not too long ago, where the United States carried out an attack against the very senior Iranian General, General Soleimani, who’s the head of the Iranian Quds Force and killed him in a drone strike. And that was an intentional decision made by a person somewhere in the US government.

Now, did they fully think that through? I don’t know, that’s a different question. But a human made that decision in any case. Well, that’s a huge escalation of hostilities between the US and Iraq. And there was a lot of uncertainty afterwards about what would happen and Iran launched some ballistic missiles against US troops in Iraq. And whether that’s it, or there’s more retaliation to come, I think we’ll see. But it could be a much more challenging situation, if you had a situation in the future where an autonomous weapon malfunctioned and took some action. And now the other side might feel compelled to respond. They might say, well, we have to, we can’t let this go. Because humans emotions are on the line and national pride and prestige, and they feel like they need to maintain a principle of deterrence and they need to retaliate it. So these could all be very complicated things if you had an accident with an autonomous weapon.

Lucas Perry: Right. And so an adjacent issue that I’d like to explore now is how a potential arms race can have interplay with issues around accidental escalation of conflict. So is there already an arms race brewing for autonomous weapons? If so, why and what could potentially be done to deescalate such a situation?

Paul Scharre: If there’s an arms race, it’s a very strange one because no one is building the weapons. We see militaries advancing in robotics and autonomy, but we don’t really see sort of this rush to build autonomous weapons. I struggle to point to any programs that I’m aware of in militaries around the globe that are clearly oriented to build fully autonomous weapons. I think there are lots of places where much like these incremental advancements of autonomy in cars, you can see more autonomous features in military vehicles and drones and robotic systems and missiles. They’re adding more autonomy. And one might be violently concerned about where that’s going. But it’s just simply not the case that militaries have declared their intention. We’re going to build autonomous weapons, and here they are, and here’s our program to build them. I would struggle to use the term arms race. It could happen, maybe worth a starting line of an arms race. But I don’t think we’re in one today by any means.

It’s worth also asking, when we say arms race, what do we mean and why do we care? This is again, one of these terms, it’s often thrown around. You’ll hear about this, the concept of autonomous weapons or AI, people say we shouldn’t have an arms race. Okay. Why? Why is an arms race a bad thing? Militaries normally invest in new technologies to improve their national defense. That’s a normal activity. So if you say arms race, what do you mean by that? Is it beyond normal activity? And why would that be problematic? In the political science world, the specific definitions vary, but generally, an arms race is viewed as an increase in defense spending overall, or in a particular technology area above normal levels of modernizing militaries. Now, usually, this is problematic for a couple of reasons. One could be that it ends up just in a massive national expenditure, like during the case of the Cold War, nuclear weapons, that doesn’t really yield any military value or increase anyone’s defense or security, it just ends up net flushing a lot of money down the drain. That’s money that could be spent elsewhere for pre K education or healthcare or something else that might be societally beneficial instead of building all of these weapons. So that’s one concern.

Another one might be that we end up in a world that the large number of these weapons or the type of their weapons makes it worse off. Are we really better off in a world where there are 10s of thousands of nuclear weapons on hair-trigger versus a few thousand weapons or a few hundred weapons? Well, if we ever have zero, all things being equal, probably fewer nuclear weapons is better than more of them. So that’s another kind of concern whether in terms of violence and destructiveness of war, if a war breakout or the likelihood of war and the stability of war. This is an A in an area where certainly we’re not in any way from a spending standpoint, in an arms race for autonomous weapons or AI today, when you look at actual expenditures, they’re a small fraction of what militaries are spending on, if you look at, say AI or autonomous features at large.

And again for autonomous weapons, there really aren’t at least openly declared programs to say go build a fully autonomous weapon today. But even if that were the case, why is that bad? Why would a world where militaries are racing to build lots of atomic weapons be a bad thing? I think it would be a bad thing, but I think it’s also worth just answering that question, because it’s not obvious to everyone. This is something that’s often missing in a lot of these debates and dialogues about autonomous weapons, people may not share some of the underlying assumptions. It’s better to bring out these assumptions and explain, I think this would be bad for these reasons, because maybe it’s not intuitive to other people that they don’t share those reasons and articulating them could increase understanding.

For example, the FLI letter on autonomous weapons from a few years ago said, “the key question for humanity today is whether to start a global AI arms race or prevent it from starting. If any major military power pushes ahead with AI weapon development, the global arms race is virtually inevitable. And the endpoint of this technological trajectory is obvious. Autonomous weapons will become the Kalashnikovs of tomorrow.” I like the language, it’s very literary, “the Kalashnikovs of tomorrow.” Like it’s a very concrete image. But there’s a whole bunch of assumptions packed into those few sentences that maybe don’t work in the letter that’s intended to like sort of galvanize public interest and attention, but are worth really unpacking. What do we mean when we say autonomous weapons are the Kalashnikovs of tomorrow and why is that bad? And what does that mean? Those are, I think, important things to draw out and better understand.

It’s particularly hard for this issue because the weapons don’t exist yet. And so it’s not actually like debates around something like landlines. We could point to the mines and say like “this is a landmine, we all agree this is a landmine. This is what it’s doing to people.” And everyone could agree on what the harm is being caused. The people might disagree on what to do about it, but there’s agreement on what the weapon is and what the effect is. But for autonomous weapons, all these things are up to debate. Even the term itself is not clearly defined. And when I hear people describe it, people can be describing a whole range of things. Some people when they say the word autonomous weapon, they’re envisioning a Roomba with a gun on it. And other people are envisioning the Terminator. Now, both of those things are probably bad ideas, but for very different reasons. And that is important to draw out in these conversations. When you say autonomous weapon, what do you mean? What are you envisioning? What are you worried about? Worried about certain types of scenarios or certain types of effects?

If we want to get to the place where we really as a society come together and grapple with this challenge, I think first and foremost, a better communication is needed and people may still disagree, but it’s much more helpful. Stuart Russell from Berkeley has talked a lot about dangers of small anti-personnel autonomous weapons that would widely be the proliferated. He made the Slaughterbots video that’s been seen millions of times on YouTube. That’s a very specific image. It’s an image that’s very concrete. So then you can say, when Stuart Russell is worried about autonomous weapons, this is what he’s worried about. And then you can start to try to better understand the assumptions that go into that.

Now, I don’t share Stuart’s concerns, and we’ve written about it and talked about before, but it’s not actually because we disagree about the technology, I would agree that that’s very doable with existing technology. We disagree about the social responses to that technology, and how people respond, and what are the countermeasures and what are ways to prevent proliferation. So we, I think, disagree on some of the political or social factors that surround kind of how people approach this technology and use it. Sometimes people actually totally agree on the risks and even maybe the potential futures, they just have different values. And there might be some people who their primary value is trying to have fewer weapons in the world. Now that’s a noble goal. And they’re like, hey, anyway that we can have fewer weapons, fewer advanced technologies, that’s better. That’s very different from someone who’s coming from a position of saying, my goal is to improve my own nation’s defense. That’s a totally different value system. A total different preference. And they might be like, I also value what you say, but I don’t value it as much. And I’m going to take actions that advance these preferences. It’s important to really sort of try to better draw them out and understand them in this debate, if we’re going to get to a place where we can, as a society come up with some helpful solutions to this problem.

Lucas Perry: Wonderful. I’m totally on board with that. Two questions and confusions on my end. The first is, I feel a bit confused when you say these weapons don’t exist already. It seems to me more like autonomy exists on a spectrum and is the integration of many different technologies and decision making in systems. It seems to me there is already a certain degree of autonomy, there isn’t Terminator level autonomy, or specify an objective and the autonomous system can just basically go execute that, that seems to require very high level of generality, but there seems to already exist a level of autonomy today.

And so in that video, Stuart says that slaughterbots in particular represent a miniaturization and integration of many technologies, which already exist today. And the second thing that I’m confused about is when you say that it’s unclear to you that militaries are very interested in this or that there currently is an arms race. It seems like yes, there isn’t an arms race, like there was with nuclear weapons where it’s very clear, and they’re like Manhattan projects around this kind of technology, but given the strategic advantage conferred by this technology now and likely soon, it seems to me like game theoretically, from the position of militaries around the world that have the capacity to invest in these things, that it is inevitable given their battlefield importance that there would be massive ramping up or investments, or that there already is great interest in developing the autonomy and the subtechnologies required for developing fully autonomous systems.

Paul Scharre: Those are great questions and right on point. And I think the central issues in both of your questions are when we say these weapons or when I say these things, I should be more precise. When we say autonomous weapons, what do we mean exactly? And this is one of the things that can be tricky in this space, because there are not these universally agreed upon definitions. There are certainly many weapons systems used widely around the globe today that incorporate some autonomous features. Many of these are fire and forget weapons. When someone launches them, they’re not coming back. They have in that sense, autonomy to carry out their mission. But autonomy is relatively limited and narrowly bounded, and humans, for the most part are choosing the targets. So you can think of kind of maybe these three classes of weapons, these semi autonomous weapons, where humans are choosing the targets, but there’s lots of autonomy surrounding that decision, queuing information to people, flying the munition once the person launches it. That’s one type of weapon, widely used today by really every advanced military.

Another one is the supervised autonomous weapons that are used in these relatively limited settings for defensive purposes, where there is kind of this automatic mode that people can turn them on and activate them to defend the ship or the ground base or the vehicle. And these are really needed for these situations where the incoming threats are too fast for humans to respond. And these again are widely used around the globe and have been in place for decades. And then there are what we could call fully autonomous weapons, where the human’s launching them and human programs in the parameters, but they have some freedom to fly a search pattern over some area and then once they find a target, attack it on their own. For the most part, with some exceptions, those weapons are not widely used today. There have been some experimental systems that have been designed. There have been some put into operation in the past. The Israeli harpy drone is an example of this that is still in operation today. It’s been around since the ’90s, so it’s not really very new. And it’s been sold to a handful of countries, India, Turkey, South Korea, China, and the Chinese have reportedly reverse engineered their own version of this.

But it’s not like when widespread. So it’s not like a major component of militaries order of that. I think you see militaries investing in robotic systems, but the bulk of their fleets are still human occupied platforms, robotics are largely an adjunct to them. And in terms of spending, while there is increased spending on robotics, most of the spending is still going towards more traditional military platforms. The same is also true about the degree of autonomy, most of these robotic systems are just remote controlled, and they have very limited autonomy today. Now we’re seeing more autonomy over time in both robotic vehicles and in missiles. But militaries have a strong incentive to keep humans involved.

It is absolutely the case that militaries want technologies that will give them an advantage on the battlefield. But part of achieving an advantage means your systems work, they do what you want them to do, the enemy doesn’t hack them and take them over, you have control over them. All of those things point to more human control. So I think that’s the thing where you actually see militaries trying to figure out where’s the right place on the spectrum of autonomy? How much autonomy is right, and that line is going to shift over time. But it’s not the case that they necessarily want just full autonomy because what does that mean, then they do want weapon systems to sort of operate under some degree of human direction and involvement. It’s just that what that looks like may evolve over time as the technology advances.

And there are also, I should add, other bureaucratic factors that come into play that militaries investments are not entirely strategic. There’s bureaucratic politics within organizations. There’s politics more broadly with the domestic defense industry interfacing with the political system in that country. They might drive resources in certain directions. There’s some degree of inertia of course in any system that are also factors in play.

Lucas Perry: So I want to hit here a little bit on longer term perspectives. So the Future of Life Institute in particular is interested in mitigating existential risks. We’re interested in the advanced risks from powerful AI technologies where AI not aligned with human values and goals and preferences and intentions can potentially lead us to suboptimal equilibria that were trapped in permanently or could lead to human extinction. And so other technologies we care about are nuclear weapons and synthetic-bio enabled by AI technologies, etc. So there is this view here that if we cannot establish a governance mechanism as a global community on the concept that we should not let AI make the decision to kill then how can we deal with more subtle near term issues and eventual long term safety issues around the powerful AI technologies? So there’s this view of ensuring beneficial outcomes around lethal autonomous weapons or at least beneficial regulation or development of that technology, and the necessity of that for longer term AI risk and value alignment with AI systems as they become increasingly intelligent. I’m curious to know if you have a view or perspective on this.

Paul Scharre: This is the fun part of the podcast with the Future of Life because this rarely comes up in a lot of the conversations because I think in a lot of the debates, people are focused on just much more near term issues surrounding autonomous weapons or AI. I think that if you’re inclined to see that there are longer term risks for more advanced developments in AI, then I think it’s very logical to say that there’s some value in humanity coming together to come up with some set of rules about autonomous weapons today, even if the specific rules don’t really matter that much, because the level of risk is maybe not as significant, but the process of coming together and agreeing on some set of norms and limits on particularly military applications in AI is probably beneficial and may begin to create the foundations for future cooperation. The stakes for autonomous weapons might be big, but are certainly not existential. I think in any reasonable interpretation of autonomous weapons might do really, unless you start thinking about autonomy wired into, like nuclear launch decisions which is basically nuts. And I don’t think it’s really what’s on the table for realistically what people might be worried about.

When we try to come together as a human society to grapple with problems, we’re basically forced to deal with the institutions that we have in place. So for example, for autonomous weapons, we’re having debates in the UN Convention on Certain Conventional Weapons to CCW. Is that the best form for talking about autonomous weapons? Well, it’s kind of the form that exists for this kind of problem set. It’s not bad. It’s not perfect in some respects, but it’s the one that exists. And so if you’re worried about future AI risk, creating the institutional muscle memory among the relevant actors in society, whether it’s nation states, AI scientists, members of civil society, militaries, if you’re worried about military applications, whoever it is, to come together, to have these conversations, and to come up with some answer, and maybe set some agreements, some limits is probably really valuable actually because it begins to establish the right human networks for collaboration and cooperation, because it’s ultimately people, it’s people who know each other.

So oh, “I worked with this person on this last thing.” If you look at, for example, the international movement that The Campaign to Stop Killer Robots is spearheading, that institution or framework, those people, those relationships are born out of past successful efforts to ban landmines and then cluster munitions. So there’s a path dependency, and human relationships and bureaucracies, institutions that really matters. Coming together and reaching any kind of agreement, actually, to set some kind of limits is probably really vital to start exercising those muscles today.

Lucas Perry: All right, wonderful. And a final fun FLI question for you. What are your views on long term AI safety considerations? Do you view AI eventually as an existential risk and do you integrate that into your decision making and thinking around the integration of AI and military technology?

Paul Scharre: Yes, it’s a great question. It’s not something that comes up a lot in the world that I live in, in Washington in the policy world, people don’t tend to think about that kind of risk. I think it’s a concern. It’s a hard problem because we don’t really know how the technology is evolving. And I think that one of the things is challenging with AI is our frame for future more advanced AI. Often the default frame is sort of thinking about human like intelligence. When people talk about future AI, people talk about terms like AGI, or high level machine intelligence or human like intelligence, we don’t really know how the technology is evolving.

I think one of the things that we’re seeing with AI machine learning that’s quite interesting is that it often is evolving in ways that are very different from human intelligence, in fact, very quite alien and quite unusual. And I’m not the first person to say this, but I think that this is valid that we are, I think, on the verge of a Copernican revolution in how we think about intelligence, that rather than thinking of human intelligence as the center of the universe, that we’re realizing that humans are simply one type of intelligence among a whole vast array and space of possible forms of intelligence, and we’re creating different kinds, they may have very different intelligence profiles, they may just look very different, they may be much smarter than humans in some ways and dumber in other ways. I don’t know where things are going. I think it’s entirely possible that we move forward into a future where we see many more forms of advanced intelligent systems. And because they don’t have the same intelligence profile as human beings, we continue to kick the can down the road into being true intelligence because it doesn’t look like us. It doesn’t think like us. It thinks differently. But these systems may yet be very powerful in very interesting ways.

We’ve already seen lots of AI systems, even very simple ones exhibit a lot of creativity, a lot of interesting and surprising behavior. And as we begin to see the sort of scope of their intelligence widen over time, I think there are going to be risks that come with that. They may not be the risks that we were expecting, but I think over time, there going to be significant risks, and in some ways that our anthropocentric view is, I think, a real hindrance here. And I think it may lead us to then underestimate risk from things that don’t look quite like humans, and maybe miss some things that are very real. I’m not at all worried about some AI system one day becoming self aware, and having human level sentience, that does not keep me up at night. I am deeply concerned about advanced forms of malware. We’re not there today yet. But you could envision things over time that are adapting and learning and begin to populate the web, like there are people doing interesting ways of thinking about systems that have misaligned goals. It’s also possible to envision systems that don’t have any human directed goals at all. Viruses don’t. They replicate. They’re effective at replicating, but they don’t necessarily have a goal in the way that we think of it other than self replication.

If you have systems that are capable of replicating, of accumulating resources, of adapting, over time, you might have all of the right boxes to check to begin to have systems that could be problematic. They could accumulate resources that could cause problems. Even if they’re not trying to pursue either a goal that’s misaligned with human interest or even any goal that we might recognize. They simply could get out in the wild, if they’re effective at replication and acquiring resources and adapting, then they might survive. I think we’re likely to be surprised and continue to be surprised by how AI systems evolve, and where that might take us. And it might surprise us in ways that are humbling for how we think about human intelligence. So one question I guess is, is human intelligence a convergence point for more intelligent systems? As AI systems become more advanced, and they become more human like, or less human like and more alien.

Lucas Perry: Unless we train them very specifically on human preference hierarchies and structures.

Paul Scharre: Right. Exactly. Right. And so I’m not actually worried about a system that has the intelligence profile of humans, when you think about capacity in different tasks.

Lucas Perry: I see what you mean. You’re not worried about an anthropomorphic AI, you’re worried about a very powerful, intelligent, capable AI, that is alien and that we don’t understand.

Paul Scharre: Right. They might have cross domain functionality, it might have the ability to do continuous learning. It might be adaptive in some interesting ways. I mean, one of the interesting things we’ve seen about the field of AI is that people are able to tackle a whole variety of problems with some very simple methods and algorithms. And this seems for some reason offensive to some people in the AI community, I don’t know why, but people have been able to use some relatively simple methods, with just huge amounts of data and compute, it’s like a variety of different kinds of problems, some of which seem very complex.

Now, they’re simple compared to the real world, when you look at things like strategy games like StarCraft and Dota 2, like the world looks way more complex, but these are still really complicated kind of problems. And systems are basically able to learn totally on their own. That’s not general intelligence, but it starts to point towards the capacity to have systems that are capable of learning a whole variety of different tasks. They can’t do this today, continuously without suffering the problem of catastrophic forgetting that people are working on these things as well. The problems today are the systems aren’t very robust. They don’t handle perturbations in the environment very well. People are working on these things. I think it’s really hard to see how this evolves. But yes, in general, I think that our fixation on human intelligence as the pinnacle of intelligence, or even the goal of what we’re trying to build, and the sort of this anthropocentric view is, I think, probably one that’s likely to lead us to maybe underestimate some kinds of risks.

Lucas Perry: I think those are excellent points and I hope that mindfulness about that is able to proliferate in government and in actors who have power to help mitigate some of these future and short term AI risks. I really appreciate your perspective and I think you bring a wholesomeness and a deep authentic entertaining of all the different positions and arguments here on the question of autonomous weapons and I find that valuable. So thank you so much for your time and for helping to share information about autonomous weapons with us.

Paul Scharre: Thank you and thanks everyone for listening. Take care.

End of recorded material

FLI Podcast: Distributing the Benefits of AI via the Windfall Clause with Cullen O’Keefe

As with the agricultural and industrial revolutions before it, the intelligence revolution currently underway will unlock new degrees and kinds of abundance. Powerful forms of AI will likely generate never-before-seen levels of wealth, raising critical questions about its beneficiaries. Will this newfound wealth be used to provide for the common good, or will it become increasingly concentrated in the hands of the few who wield AI technologies? Cullen O’Keefe joins us on this episode of the FLI Podcast for a conversation about the Windfall Clause, a mechanism that attempts to ensure the abundance and wealth created by transformative AI benefits humanity globally.

Topics discussed in this episode include:

  • What the Windfall Clause is and how it might function
  • The need for such a mechanism given AGI generated economic windfall
  • Problems the Windfall Clause would help to remedy 
  • The mechanism for distributing windfall profit and the function for defining such profit
  • The legal permissibility of the Windfall Clause 
  • Objections and alternatives to the Windfall Clause

Timestamps: 

0:00 Intro

2:13 What is the Windfall Clause? 

4:51 Why do we need a Windfall Clause? 

06:01 When we might reach windfall profit and what that profit looks like

08:01 Motivations for the Windfall Clause and its ability to help with job loss

11:51 How the Windfall Clause improves allocation of economic windfall 

16:22 The Windfall Clause assisting in a smooth transition to advanced AI systems

18:45 The Windfall Clause as assisting with general norm setting

20:26 The Windfall Clause as serving AI firms by generating goodwill, improving employee relations, and reducing political risk

23:02 The mechanism for distributing windfall profit and desiderata for guiding it’s formation 

25:03 The windfall function and desiderata for guiding it’s formation 

26:56 How the Windfall Clause is different from being a new taxation scheme

30:20 Developing the mechanism for distributing the windfall 

32:56 The legal permissibility of the Windfall Clause in the United States

40:57 The legal permissibility of the Windfall Clause in China and the Cayman Islands

43:28 Historical precedents for the Windfall Clause

44:45 Objections to the Windfall Clause

57:54 Alternatives to the Windfall Clause

01:02:51 Final thoughts

 

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You can listen to the podcast above or read the transcript below. 

Lucas Perry: Welcome to the Future of Life Institute Podcast. I’m Lucas Perry. Today’s conversation is with Cullen O’Keefe about a recent report he was the lead author on called The Windfall Clause: Distributing the Benefits of AI for the Common Good. For some quick background, the agricultural and industrial revolutions unlocked new degrees and kinds of abundance, and so too should the intelligence revolution currently underway. Developing powerful forms of AI will likely unlock levels of abundance never before seen, and this comes with the opportunity of using such wealth in service of the common good of all humanity and life on Earth but also with the risks of increasingly concentrated power and resources in the hands of the few who wield AI technologies. This conversation is about one possible mechanism, the Windfall Clause, which attempts to ensure that the abundance and wealth likely to be created by transformative AI systems benefits humanity globally.

For those not familiar with Cullen, Cullen is a policy researcher interested in improving the governance of artificial intelligence using the principles of Effective Altruism.  He currently works as a Research Scientist in Policy at OpenAI and is also a Research Affiliate with the Centre for the Governance of AI at the Future of Humanity Institute.

The Future of Life Institute is a non-profit and this podcast is funded and supported by listeners like you. So if you find what we do on this podcast to be important and beneficial, please consider supporting the podcast by donating at futureoflife.org/donate. You can also follow us on your preferred listening platform, like on Apple Podcasts or Spotify, by searching for us directly or following the links on the page for this podcast found in the description.

And with that, here is Cullen O’Keefe on the Windfall Clause.

We’re here today to discuss this recent paper, that you were the lead author on called the Windfall Clause: Distributing the Benefits of AI for the Common Good. Now, there’s a lot there in the title, so we can start of pretty simply here with, what is the Windfall Clause and how does it serve the mission of distributing the benefits of AI for the common good?

Cullen O’Keefe: So the Windfall Clause is a contractual commitment AI developers can make, that basically stipulates that if they achieve windfall profits from AI, that they will donate some percentage of that to causes that benefit everyone.

Lucas Perry: What does it mean to achieve windfall profits?

Cullen O’Keefe: The answer that we give is that when a firm’s profits grow in excess of 1% of gross world product, which is just the sum of all countries GDP, then that firm has hit windfall profits. We use this slightly weird measurement of profits is a percentage of gross world product, just to try to convey the notion that the thing that’s relevant here is not necessarily the size of profits, but really the relative size of profits, relative to the global economy.

Lucas Perry: Right. And so an important background framing and assumption here seems to be the credence that one may have in transformative AI or in artificial general intelligence or in superintelligence, creating previously unattainable levels of wealth and value and prosperity. I believe that in terms of Nick Bostrom’s Superintelligence, this work in particular is striving to serve the common good principal, that superintelligence or AGI should be created in the service of and the pursuit of the common good of all of humanity and life on Earth. Is there anything here that you could add about the background to the inspiration around developing the Windfall Clause.

Cullen O’Keefe: Yeah. That’s exactly right. The phrase Windfall Clause actually comes from Bostrom’s book. Basically, the idea was something that people inside of FHI were excited about for a while, but really hadn’t done anything with because of some legal uncertainties. Basically, the fiduciary duty question that I examined in the third section of the report. When I was an intern there in the summer of 2018, I was asked to do some legal research on this, and ran away with it from there. My legal research pretty convincingly showed that it should be legal as a matter of corporate law, for a corporation to enter in to such a contract. In fact, I don’t think it’s a particularly hard case. I think it looks like things that operations do a lot already. And I think some of the bigger questions were around the implications and design of the Windfall Clause, which is also addressed in the report.

Lucas Perry: So, we have this common good principal, which serves as the moral and ethical foundation. And then the Windfall Clause it seems, is an attempt at a particular policy solution for AGI and superintelligence, serving the common good. With this background, could you expand a little bit more on why is that we need a Windfall Clause?

Cullen O’Keefe: I guess I wouldn’t say that we need a Windfall Clause. The Windfall Clause might be one mechanism that would solve some of these problems. The primary way in which cutting edge AI is being develop is currently in private companies. And the way that private companies are structured is perhaps not maximally conducive to the common good principal. This is not due to corporate greed or anything like that. It’s more just a function of the roles of corporations in our society, which is that they’re primarily vehicles for generating returns to investors. One might think that those tools that we currently have for taking some of the returns that are generated for investors and making sure that they’re distributed in a more equitable and fair way, are inadequate in the face of AGI. And so that’s kind of the motivation for the Windfall Clause.

Lucas Perry: Maybe if you could speak a little bit to the surveys of researchers of credence’s and estimates about when we might get certain kinds of AI. And then what windfall in the context of an AGI world actually means.

Cullen O’Keefe: The surveys of AGI timelines, I think this is an area with high uncertainty. We cite Katja Grace’s survey of AI experts, which is a few years old at this point. I believe that the median timeline that AI experts gave in that was somewhere around 2060, of attaining AGI as defined in a specific way by that paper. I don’t have opinions on whether that timeline is realistic or unrealistic. We just take it as a baseline, as the best specific timeline that has at least some evidence behind it. And what was the second question?

Lucas Perry: What other degrees of wealth might be brought about via transformative AI.

Cullen O’Keefe: The short and unsatisfying answer to this, is that we don’t really know. I think that the amount of economic literature really focusing on AGI in particular is pretty minimal. Some more research on this would be really valuable. A company earning profits that are defined as windfall via the report, would be pretty unprecedented in history, so it’s a very hard situation to imagine. Forecasts about the way that AI will contribute to growth are pretty variable. I think we don’t really have a good idea of what that might mean. And I think especially because the interface between economists and people thinking about AGI has been pretty minimal. A lot of the thinking has been more focused on more mainstream issues. If the strongest version of AGI were to come, the economic gains could be pretty huge. There’s a lot on the line that circumstance.

Part of what motivated the Windfall Clause, is trying to think of mechanisms that could withstand this uncertainty about what the actual economics of AGI will be like. And that’s kind of what the contingent commitment and progressively scaling commitment of the Windfall Clause is supposed to accomplish.

Lucas Perry: All right. So, now I’m going to explore here some of these other motivations that you’ve written in your report. There is the need to address loss of job opportunities. The need to improve the allocation of economic windfall, which if we didn’t do anything right now, there would actually be no way of doing that other than whatever system of taxes we would have around that time. There’s also this need to smooth the transition to advanced AI. And then there is this general norm setting strategy here, which I guess is an attempt to imbue and instantiate a kind of benevolent ethics based on the common good principle. Let’s start of by hitting on addressing the loss of job opportunities. How might transformative AI lead to the loss of job opportunities and how does the Windfall Clause help to remedy that?

Cullen O’Keefe: So I want to start of with a couple of caveats. So number one, I’m not an economist. Second is, I’m very wary of promoting Luddite views. It’s definitely true that in the past, technological innovation has been pretty universally positive in the long run, notwithstanding short term problems with transitions. So, it’s definitely by no means inevitable that advances in AI will lead to joblessness or decreased earnings. That said, I do find it pretty hard to imagine a scenario in which we achieve very general purpose AI systems, like AGI. And there are still bountiful opportunities for human employment. I think there might be some jobs which have human only employment or something like that. It’s kind of unclear, in an economy with AGI or something else resembling it, why there would be a demand for humans. There might be jobs I guess, in which people are inherently uncomfortable having non-humans. Good examples of this would be priests or clergy, probably most religions will not want to automate their clergy.

I’m not a theologian, so I can’t speak to the proper theology of that, but that’s just my intuition. People also mentioned things like psychiatrists, counselors, teachers, child care, stuff like that. That doesn’t look as automatable. And then the human meaning aspect of this, John Danaher, philosopher, recently released a book called Automation and Utopia, talking about how for most people work is the primary source of meaning. It’s certainly what they do with the great plurality of their waking hours. And I think for people like me and you, we’re lucky enough to like our jobs a lot, but for many people work is mostly a source of drudgery. Often unpleasant, unsafe, etcetera. But if we find ourselves in world in which work is largely automated, not only will we have to deal with the economic issues relating to how people who can no longer offer skills for compensation, will feed themselves and their families. But also how they’ll find meaning in life.

Lucas Perry: Right. If the category and meaning of jobs changes or is gone altogether, the Windfall Clause is also there to help meet fundamental universal basic human needs, and then also can potentially have some impact on this question of value and meaning. If the Windfall Clause allows you to have access to hobbies and nice vacations and other things that give human beings meaning.

Cullen O’Keefe: Yeah. I would hope so. It’s not a problem that we explicitly address in the paper. I think this is kind of in the broader category of what to actually do with the windfall, once it’s donated. You can think of this as like the bottom of the funnel. Whereas the Windfall Clause report is more focused at the top of the funnel, getting companies to actually commit to such a thing. And I think there’s a huge rich area of work to think about, what do we actually do with the surplus from AGI, once it manifests. And assuming that we can get it in to the coffers of a public minded organization. It’s something that I’m lucky enough to think about in my current job at OpenAI. So yeah, making sure that both material needs and psychological higher needs are taken care of. That’s not something I have great answers for yet.

Lucas Perry: So, moving on here to the second point. We also need a Windfall Clause or function or mechanism, in order to improve the allocation of economic windfall. So, could you explain that one?

Cullen O’Keefe: You can imagine a world in which employment kind of looks the same as it is today. Most people have jobs, but a lot of the gains are going to a very small group of people, namely shareholders. I think this is still a pretty sub-optimal world. There are diminishing returns on money for happiness. So all else equal and ignoring incentive effects, progressively distributing money seems better than not. Primarily firms looking to develop the AI are based in a small set of countries. In fact, within those countries, the group of people who are heavily invested in those companies is even smaller. And so in a world, even where employment opportunities for the masses are pretty normal, we could still expect to see pretty concentrated accrual of benefits, both within nations, but I think also very importantly, across nations. This seems pretty important to address and the Windfall Clause aims to do just that.

Lucas Perry: A bit of speculation here, but we could have had a kind of Windfall Clause for the industrial revolution, which probably would have made much of the world better off and there wouldn’t be such unequal concentrations of wealth in the present world.

Cullen O’Keefe: Yeah. I think that’s right. I think there’s sort of a Rawlsian or Harsanyian motivation there, that if we didn’t know whether we would be in an industrial country or a country that is later to develop, we would probably want to set up a system that has a more equal distribution of economic gains than the one that we have today.

Lucas Perry: Yeah. By Rawlsian, you meant the Rawls’ veil of ignorance, and then what was the other one you said?

Cullen O’Keefe: Harsanyi is another philosopher who is associated with the veil of ignorance idea and he argues, I think pretty forcefully, that actually the agreement that you would come to behind the veil of ignorance, is one that maximizes expected utility, just due to classic axioms of rationality. What you would actually want to do is maximize expected utility, whereas John Rawls has this idea that you would want to maximize the lot of the worst off, which Harsanyi argues doesn’t really follow from the veil of ignorance, and decision theoretic best practices.

Lucas Perry: I think that the veil of ignorance, which for listeners who don’t know what that is, it’s if you can imagine yourself not knowing how you were going to be born as in the world. You should make ethical and political and moral and social systems, with that view in mind. And if you do that, you will pretty honestly and wholesomely come up with something to your best ability, that is good for everyone. From behind that veil of ignorance, of knowing who you might be in the world, you can produce good ethical systems. Now this is relevant to the Windfall Clause, because going through your paper, there’s the tension between arguing that this is actually something that is legally permissible and that institutions and companies would want to adopt, which is in clear tension with maximizing profits for shareholders and the people with wealth and power in those companies. And so there’s this fundamental tension behind the Windfall Clause, between the incentives of those with power to maintain and hold on to the power and wealth, and the very strong and important ethical and normative views and compunctions, that say that this ought to be distributed to the welfare and wellbeing of all sentient beings across the planet.

Cullen O’Keefe: I think that’s exactly right. I think part of why I and others at the Future of Humanity Institute were interested in this project, is that we know a lot of people working in AI at all levels. And I think a lot of them do want to do the genuinely good thing. But feel the constraints of economics but also of fiduciary duties. We didn’t have any particular insights in to that with this piece, but I think part of the motivation is just that we want to put resources out there for any socially conscious AI developers to say, “We want to make this commitment and we feel very legally safe doing so,” for the reasons that I lay out.

It’s a separate question whether it’s actually in their economic interest to do that or not. But at least we think they have the legal power to do so.

Lucas Perry: Okay. So maybe we can get in to and explore the ethical aspect of this more. I think we’re very lucky to have people like you and your fellow colleagues who have the ethical compunction to follow through and be committed to something like this. But for the people that don’t have that, I’m interested in discussing more later about what to do with them. So, in terms of more of the motivations here, the Windfall Clause is also motivated by this need for a smooth transition to transformative AI or AGI or superintelligence or advanced AI. So what does that mean?

Cullen O’Keefe: As I mentioned, it looks like economic growth from AI will probably be a good thing if we manage to avoid existential and catastrophic risks. That’s almost tautological I suppose. But just as in the industrial revolution where you had a huge spur of economic growth, but also a lot of turbulence. So part of the idea of the Windfall Clause is basically to funnel some of that growth in to a sort of insurance scheme that can help make that transition smoother. An un-smooth transition would be something like a lot of countries are worried they’re not going to see any appreciable benefit from AI and indeed, might lose out a lot because a lot of their industries would be off shored or re-shored and a lot of their people would no longer be economically competitive for jobs. So, that’s the kind of stability that I think we’re worried about. And the Windfall Clause is basically just a way of saying, you’re all going to gain significantly from this advance. Everyone has a stake in making this transition go well.

Lucas Perry: Right. So I mean there’s a spectrum here and on one end of the spectrum there is say a private AI lab or company or actor, who is able to reach AGI or transformative AI first and who can muster or occupy some significant portion of the world GDP. That could be anywhere from one to 99 percent. And there could or could not be mechanisms in place for distributing that to the citizens of the globe. And so one can imagine, as power is increasingly concentrated in the hands of the few, that there could be quite a massive amount of civil unrest and problems. It could create very significant turbulence in the world, right?

Cullen O’Keefe: Yeah. Exactly. And it’s our hypothesis that having credible mechanisms ex-ante to make sure that approximately everyone gains from this, will make people and countries less likely to take destabilizing actions. It’s also a public good of sorts. You would expect that it would be in everyone’s interest for this to happen, but it’s never individually rational to commit that much to making it happen. Which is why it’s a traditional role for governments and for philanthropy to provide those sort of public goods.

Lucas Perry: So that last point here then on the motivations for why we need a Windfall Clause, would be general norm setting. So what do you have to say about general norm setting?

Cullen O’Keefe: This one is definitely a little more vague than some of the others. But if you think about what type of organization you would like to see develop AGI, it seems like one that has some legal commitment to sharing those benefits broadly is probably correlated with good outcomes. And in that sense, it’s useful to be able to distinguish between organizations that are credibly committed to that sort of benefit, from ones that say they want that sort of broad benefit but are not necessarily committed to making it happen. And so in the Windfall Clause report, we are basically trying to say, it’s very important to take norms about the development of AI seriously. One of the norms that we’re trying to develop is the common good principal. And even better is when you and develop those norms through high cost or high signal value mechanisms. And if we’re right that a Windfall Clause can be made binding, then the Windfall Clause is exactly one of them. It’s a pretty credible way for an AI developer to demonstrate their commitment to the common good principal and also show that they’re worthy of taking on this huge task of developing AGI.

The Windfall Clause makes the performance or adherence to the common good principal a testable hypothesis. It’s sets kind of a base line against which commitments to the common good principal can be measured.

Lucas Perry: Now there are also here in your paper, firm motivations. So, incentives for adopting a Windfall Clause from the perspective of AI labs or AI companies, or private institutions which may develop AGI or transformative AI. And your three points here for firm motivations are that it can generate general goodwill. It can improve employee relations and it could reduce political risk. Could you hit on each of these here for why firms might be willing to adopt the Windfall Clause?

Cullen O’Keefe: Yeah. So just as a general note, we do see private corporations giving money to charity and doing other pro-social actions that are beyond their legal obligations, so nothing here is particularly new. Instead, it’s just applying traditional explanations for why companies engage in, what’s sometimes called corporate social responsibility or CSR. And see whether that’s a plausible explanation for why they might be amenable to a Windfall Clause. The first one that we mentioned in the report, is just generating general goodwill, and I think it’s plausible that companies will want to sign a Windfall Clause because it brings some sort of reputational benefit with consumers or other intermediary businesses.

The second one we talk about is managing employee relationships. In general, we see that tech employees have had a lot of power to shape the behavior of their employers. Fellow FLI podcast guest Haydn Belfield just wrote a great paper, saying AI specifically. Tech talent is in very high demand and therefore they have a lot of bargaining power over what their firms do and I think it’s potentially very promising that tech employers lobby for commitments like the Windfall Clause.

The third is termed in a lot of legal and investment circles, as political risk, so that’s basically the risk of governments or activists doing things that hurt you, such as tighter regulation or expropriation, taxation, things like that. And corporate social responsibility, including philanthropy, is just a very common way for firms to manage that. And could be the case for AI firms as well.

Lucas Perry: How strong do you think these motivations listed here are, and what do you think will be the main things that drive firms or institutions or organizations to adopt the Windfall Clause?

Cullen O’Keefe: I think it varies from firm to firm. I think a big one that’s not listed here is how management likes the idea of a Windfall Clause. Obviously, they’re the ones ultimately making the decisions, so that makes sense. I think employee buy-in and enthusiasm about the Windfall Clause or similar ideas will ultimately be a pretty big determinate about whether this actually gets implemented. That’s why I would love to hear and see engagement around this topic from people in the technology industry.

Lucas Perry: Something that we haven’t talked about yet is the distribution mechanism. And in your paper, you come up with desiderata and important considerations for an effective and successful distribution mechanism. Philanthropic effectiveness, security from improper influences, political legitimacy and buy in from AI labs. So, these are just guiding principals for helping to develop the mechanism for distribution. Could you comment on what the mechanism for distribution is or could be and how these desiderata will guide the formation of that mechanism?

Cullen O’Keefe: A lot of this thinking is guided by a few different things. One is just involvement in the effective altruism community. I as a member of that community, spend a lot of time thinking about how to make philanthropy work well. That said, I think that the potential scale of the Windfall Clause requires thinking about factors other than effectiveness, in the way that effectiveness altruists think of that. Just because the scale of potential resources that you’re dealing here, begins to look less and less like traditional philanthropy and more and more like psuedo or para-government institution. And so that’s why I think things like accountability and legitimacy become extra important in the Windfall Clause context. And then firm buy-in I mentioned, just because part of the actual process of negotiating an eventual Windfall Clause would presumably be coming up with distribution mechanism that advances some of the firms objectives of getting positive publicity or goodwill from agreeing to the Windfall Clause, both with their consumers and also with employers and governments.

And so they’re key stakeholders in coming up with that process as well. This all happens in the backdrop of a lot of popular discussion about the role of philanthropy in society, such as recent criticism of mega-philanthropy. I take those criticisms pretty seriously and want to come up with a Windfall Clause distribution mechanism that manages those better than current philanthropy. It’s a big task in itself and one that needs to be taken pretty seriously.

Lucas Perry: Is the windfall function synonymous with the windfall distribution mechanism?

Cullen O’Keefe: No. So, the windfall function, it’s the mathematical function that determines how much money, signatories to the Windfall Clause are obligated to give.

Lucas Perry: So, the windfall function will be part of the windfall contract, and the windfall distribution mechanism is the vehicle or means or the institution by which that output of the function is distributed?

Cullen O’Keefe: Yeah. That’s exactly right. Again, I like to think of this as top of the funnel, bottom of the funnel. So the windfall function is kind of the top of the funnel. It defines how much money has to go in to the Windfall Clause system and then the bottom of the funnel is like the output, what actually gets done with the windfall, to advance the goals of the Windfall Clause.

Lucas Perry: Okay. And so here you have some desiderata for this function, in particular transparency, scale sensitivity, adequacy, pre-windfall commitment, incentive alignment and competitiveness. Are there any here that you want to comment on with regards to the windfall function.

Cullen O’Keefe: Sure. If you look at the windfall function, it looks kind of like a progressive tax system. You fall in to some bracket and the bracket that you’re in determines the marginal percentage of money that you owe. So, in a normal income tax scheme, the bracket is determined by your gross income. In the Windfall Clause scheme, the bracket is determined by a slightly modified thing, which is profits as a percent of gross world product, which we started off talking about.

We went back and forth for a few different ways that this could look, but we ultimately decided upon a simpler windfall function that looks much like an income tax scheme, because we thought it was pretty transparent and easy to understand. And for a project as potentially important as the Windfall Clause, we thought that was pretty important that people be able to understand the contract that’s being negotiated, not just the signatories.

Lucas Perry: Okay. And you’re bringing up this point about taxes. One thing that someone might ask is, “Why do we need a whole Windfall Clause when we could just have some kind of tax on benefits accrued from AI?” But the very important feature to be mindful here, about the Windfall Clause, is that it does something that taxing cannot do, which is redistribute funding from tech heavy first world countries to people around the world, rather than just to the government of the country able to tax them. So that also seems to be a very important consideration here for why the Windfall Clause is important, rather than just some new tax scheme.

Cullen O’Keefe: Yeah. Absolutely. And in talking to people about the Windfall Clause, this is one of the top concerns that comes up. So, you’re right to emphasize it. I agree that the potential for international distribution is one of the main reasons that I personally are more excited about the Windfall Clause than standard corporate taxation. Other reasons are just that it seems just more tractable to negotiate this individually with firms, a number of firms potentially in a position of developing advanced AI is pretty small now and might continue to be small for the foreseeable future. So the number of potential entities that you have persuaded to agree to this might be pretty small.

There’s also the possibility that we mention, but don’t propose an exact mechanism for in the paper of allowing taxation to supersede the Windfall Clause. So, if a government came up with a better taxation scheme, you might either release the signatories from the Windfall Clause or just have the windfall function compensate for that by reducing or eliminating total obligation. Of course, it gets tricky because then you would have to decide which types of taxes would you do that for, if you want to maintain the international motivations of the Windfall Clause. And you would also have to kind of figure out what the optimal tax rate is, which is obviously no small task. So those are definitely complicated questions, but at least in theory, there’s the possibility for accommodating those sorts of ex-post taxation efforts in a way that doesn’t burden firms too much.

Lucas Perry: Do you have any more insights or positives or negatives to comment here about the windfall function. It seems like in the paper, it is as you mention, open for a lot more research. Do you have directions for further investigation of the windfall function?

Cullen O’Keefe: Yeah. It’s one of the things that we lead out with, and it’s actually as you’re saying. This is primarily supposed illustrative and not the right windfall function. I’d be very surprised if this was ultimately the right way to do this. Just because the possibility in this space is so big and we’ve explored so little of it. One of the ideas that I am particularly excited about, and I think more and more might ultimately be the right thing to do, is instead of having a profits based trigger for the windfall function, instead having a market tap based trigger. And there are just basic accounting reasons why I’m more excited about this. Tracking profits is not as straight forward as it seems, because firms can do stuff with their money. They can spend more of it and reallocate it in certain ways. Whereas it’s much harder and they have less incentive to downward manipulate their stock price or market capitalization. So I’d be interested in potentially coming up with more value based approaches to the windfall function rather than our current one, which is based on profits.

That said, there is a ton of other variables that you could tweak here, and would be very excited to work with people or see other proposals of what this could look like.

Lucas Perry: All right. So this is an open question about how the windfall function will exactly look. Can you provide any more clarity on the mechanism for distribution, keeping mind here the difficulty of creating an effective way of distributing the windfall, which you list as the issues of effectiveness, accountability, legitimacy and firm buy-in?

Cullen O’Keefe: One concrete idea that I actually worked closely with FLI on, specifically with Anthony Aguirre and Jared Brown, was the windfall trust idea, which is basically to create a trust or kind of psuedo-trust that makes every person in world or as many people as we can, reach equal beneficiaries of a trust. So, in this structure, which is on page 41 of the report if people are interested in seeing it. It’s pretty simple. The idea is that the successful developer would satisfy their obligations by paying money to a body called the Windfall Trust. For people who don’t know what trust is, it’s a specific type of legal entity. And then all individuals would be either or actual or potential beneficiaries of the Windfall Trust, and would receive equal funding flows from that. And could even receive equal input in to how the trust is managed, depending on how the trust was set up.

Trusts are also exciting because they are very flexible mechanisms that you can arrange the governance of in many different ways. And then to make this more manageable, obviously a single trust with eight billion beneficiaries seems hard to manage, so you take a single trust for every 100,000 people or whatever number you think is manageable. I’m kind of excited about that idea, I think it hits a lot of the desiderata pretty well and could be a way in which a lot of people could see benefit from the windfall.

Lucas Perry: Are there any ways of creating proto-windfall clauses or proto-windfall trusts to sort of test the idea before transformative AI comes on the scene?

Cullen O’Keefe: I would be very excited to do that. I guess one thing I should say, OpenAI where I currently work, has a structure called a capped-profit structure, which is similar in many ways to the Windfall Clause. Our structure is such that profits above a certain cap that can be returned to investors, go to a non-profit, which is the OpenAI non-profit, which then has to use those funds for charitable purposes. But I would be very excited to see new companies and potentially companies aligned with the mission of the FLI podcast, to experiment with structures like this. In the fourth section of the report, we talk all about different precedents that exist already, and some of these have different features that are close to the Windfall Clause. And I’d be interested in someone putting all those together for their start-up or their company and making a kind of pseudo-windfall clause.

Lucas Perry: Let’s get in to the legal permissibility of the Windfall Clause. Now you said that this is actually one of the reasons why you first got in to this, was because it got tabled because people were worried about the fiduciary responsibilities that companies would have. Let’s start by reflecting on whether or not this is legally permissible in America, and then think about China, because these are the two biggest AI players today.

Cullen O’Keefe: Yeah. There’s actually a slight wrinkle there that we might also have to talk about, the Cayman Islands. But we’ll get to that. I guess one interesting fact about the Windfall Clause report, is that it’s slightly weird that I’m the person that ended up writing this. You might think an economist should be the person writing this, since it deals so much with labor economics and inequality, etcetera, etcetera. And I’m not an economist by any means. The reason that I got swept up in this is because of the legal piece. So I’ll first give a quick crash course in corporate law, because I think it’s an area than not a lot of people understand and it’s also important for this.

Corporations are legal entities. They are managed by a board of directors for the benefit of the shareholders, who are the owners of the firm. And accordingly, since the directors have the responsibility of managing a thing which is owned in part by other people, they owe certain duties to the shareholders. There are known as fiduciary duties. The two primary ones are the duty of loyalty and the duty of care. So, duty of loyalty, we don’t really talk about a ton in this piece, just the duty to manage the corporation for the benefit of the corporation itself, and not for the personal gain of the directors.

The duty of care is kind of what it sounds like, just the duty to take adequate care that the decisions made for the corporation by the board of directors will benefit the corporation. The reason that this is important for the purposes of a Windfall Clause and also for the endless speculation of corporate law professors and theorists, is when you engage in corporate philanthropy, it kind of looks like you’re doing something that is not for the benefit of the corporation. By definition, giving money to charity is primarily a philanthropic act or at least that’s kind of the prima facie case for why that might be a problem from the standpoint of corporate law. Because this is other people’s money largely, and the corporation is giving it away, seemingly not for the benefit of the corporation itself.

There actually hasn’t been that much case law, so actual court decisions on this issue. I found some of them across the US. As a side note, we primarily talk about Delaware law, because Delaware is the state in which the plurality of American corporations are incorporated for historical reasons. Their corporate law is by far the most influential in the United States. So, even though you have this potential duty of care issue, with making corporate donations, the standard by which directors are judged is the business judgment rule. Quoting from the American Law Institute, a summary of the business judgment rule is, “A director or officer who makes a business judgment in good faith, fulfills the duty of care if the director or officer, one, is not interested,” that means there is no conflict of interest, “In the subject of the business judgment. Two, is informed with respect to the business judgment to the extent that the director or officer reasonably believes to be appropriate under the circumstances. And three, rationally believes that the business judgment is in the best interests of the corporation.” So this is actually a pretty forgiving standard. It’s basically just use your best judgement standard, which is why it’s very hard for shareholders to successfully make a case that a judgement was a violation of the business judgement rules. It’s very rare for such challenges to actually succeed.

So a number of cases have examined the relationship of the business judgement rule to corporate philanthropy. They basically universally held that this is a permissible invocation or permissible example of the business judgement rule. That there are all these potential benefits that philanthropy could give to the corporation, therefore corporate directors decision to authorize corporate donations would be generally upheld under the business judgement rule, provided all these other things are met.

Lucas Perry: So these firm motivations that we touched on earlier were generating goodwill towards the company, improving employee relations and then reducing political risk I guess is also like having good faith with politicians who are, at the end of the day, hopefully being held accountable by their constituencies.

Cullen O’Keefe: Yeah, exactly. So these are all things that could plausibly, financially benefit the corporation in some form. So in this sense, corporate philanthropy looks less like a donation and more like an investment in the firm’s long term profitability, given all these soft factors like political support and employee relations. Another interesting wrinkle to this, if you read the case law of these corporate donation cases, they’re actually quite funny. The only case I quote from would be Sullivan v. Hammer. A corporate director wanted to make a corporate donation to an art museum, that had his name and kind of served basically as his personal art collection, more or less. And the court kind of said, this is still okay under business judgement rule. So, that was a pretty shocking example of how lenient this standard is.

Lucas Perry: So then they synopsis version here, is that the Windfall Clause is permissible in the United States, because philanthropy in the past has been seen as still being in line with fiduciary duties. And the Windfall Clause would do the same.

Cullen O’Keefe: Yeah, exactly. The one interesting wrinkle about the Windfall Clause that might distinguish it from most corporate philanthropy but though definitely not all, is that it has this potentially very high ex-post cost, even though it’s ex-ante cost might be quite low. So in a situation which a firm actually has to pay out the Windfall Clause, it’s very, very costly to the firm. But the business judgement rule, there’s actually a post to protect these exact types of decisions, because the things that courts don’t want to do is be second guessing every single corporate decision with the benefit of hindsight. So instead, they just instruct people to look at the ex-ante cost benefit analysis, and defer to that, even if ex-post it turns out to have been a bad decision.

There’s an analogy that we draw to stock option compensation, which is very popular, where you give an employee a block of stock options, that at the time is not very valuable because it’s probably just in line with the current value of the stock. But ex-post might be hugely valuable and this how a lot of early employees of companies get wildly rich, well beyond what they would have earned at fair market and cash value ex-ante. That sort of ex-ante reasoning is really the important thing, not the fact that it could be worth a lot ex-post.

One of the interesting things about the Windfall Clause is that it is a contract through time, and potentially over a long time. A lot of contracts that we make are pretty short term focus. But the Windfall Clause is in agreement now to do stuff, is stuff happens in the future, potentially in the distant future, which is part of the way the windfall function is designed. It’s designed to be relevant over a long period of time especially given the uncertainty that we started off talking about, with AI timelines. The important thing that we talked about was the ex-ante cost which means the cost to the firm in expected value right now. Which is basically the probability that this ever gets triggered, and if it does get triggered, how much will it be worth, all discounted by the time value of money etcetera.

One thing that I didn’t talk about is that there’s some language in some court cases about limiting the amount of permissible corporate philanthropy to a reasonable amount, which is obviously not a very helpful guide. But there’s a court case saying that this should be determined by looking to the charitable giving deduction, which is I believe about 10% right now.

Lucas Perry: So sorry, just to get the language correct. It’s the ex-post cost is very high because after the fact you have to pay huge percentages of your profit?

Cullen O’Keefe: Yeah.

Lucas Perry: But it still remains feasible that a court might say that this violates fiduciary responsibilities right?

Cullen O’Keefe: There’s always the possibility that a Delaware court would invent or apply new doctrine in application to this thing, that looks kind of weird from their perspective. I mean, this is a general question of how binding precedent is, which is an endless topic of conversation for lawyers. But if they were doing what I think they should do and just straight up applying precedent, I don’t see a particular reason why this would be decided differently than any of the other corporate philanthropy cases.

Lucas Perry: Okay. So, let’s talk a little bit now about the Cayman Islands and China.

Cullen O’Keefe: Yeah. So a number of significant Chinese tech companies are actually incorporated in the Cayman Islands. It’s not exactly clear to me why this is the case, but it is.

Lucas Perry: Isn’t it for hiding money off-shore?

Cullen O’Keefe: So I’m not sure if that’s why. I think even if taxation is a part of that, I think it also has to do with capital restrictions in China, and also they want to attract foreign investors which is hard if they’re incorporated in China. Investors might not trust Chinese corporate law very much. This is just my speculation right now, I don’t actually know the answer to that.

Lucas Perry: I guess the question then just is, what is the US and China relationship with the Cayman Islands? What is it used for? And then is the Windfall Clause permissible in China?

Cullen O’Keefe: Right. So, the Cayman Islands is where the big three Chinese tech firms, Alibaba, Baidu and Tencent are incorporated. I’m not a Caymanian lawyer by any means, nor am I an expert in China law, but basically from my outsider reading of this law, applying my general legal knowledge, it appears that similar principals of corporate law apply in the Cayman Islands which is why it might be a popular spot for incorporation. They have a rule that looks like the business judgement rule. This is in footnote 120 if anyone wants to dig in to it in the report. So, for the Caymanian corporations, it looks like it should be okay for the same reason. China being a self proclaimed socialist country, also has a pretty interesting corporate law that actually not only allows but appears to encourage firms to engage in corporate philanthropy. From the perspective of their law, at least it looks potentially more friendly than even Delaware law, so kind of a-fortiori should be permissible there.

That said, obviously there’s potential political reality to be considered there, especially also the influence of the Chinese government on state owned enterprises, so I don’t want to be naïve as to just thinking what the law says is what is actually politically feasible there. But all that caveating aside, as far as the law goes, the People’s Republic of China looks potentially promising for a Windfall Clause.

Lucas Perry: And that again matter, because China is currently second to the US in AI and are thus also likely potentially able to reach windfall via transformative AI in the future.

Cullen O’Keefe: Yeah. I think that’s the general consensus, is that after the United States, China seems to be the most likely place to develop AGI for transformative AI. You can listen and read a lot of the work by my colleague Jeff Ding on this, who recently appeared on 80,000 Hours podcast, talking about China’s AI dream and has a report by the same name, from FHI, that I would highly encourage everyone to read.

Lucas Perry: All right. Is it useful here to talk about historical precedents?

Cullen O’Keefe: Sure. I think one that’s potentially interesting is that a lot of sovereign nations have actually dealt with this problem of windfall governance before. It’s actually like natural resource based states. So Norway is kind of the leading example of this. They had a ton of wealth from oil, and had to come up with a way of distributing that wealth in a fair way. And as a sovereign wealth fund as a result, as do a lot of countries and provides for all sorts of socially beneficial applications.

Google actually when it IPO’d, gave one percent of its equity to it’s non-profit arm, the Google Foundation. So that’s actually significantly like the Windfall Clause in the sense that it gave a commitment that would grow in value as the firm’s prospects engaged. And therefore had low ex-ante costs but potentially higher ex-post-cost. Obviously, in personal philanthropy, a lot of people will be familiar with pledges like Founders Pledge or the Giving What We Can Pledge, where people pledge a percentage of their personal income to charity. The Founders Pledge kind of most resembles the Windfall Clause in this respect. People pledge a percentage of equity from their company upon exit or upon liquidity events and in that sense, it looks a lot like a Windfall Clause.

Lucas Perry: All right. So let’s get in to objections, alternatives and limitations here. First objection to the Windfall Clause, would be that the Windfall Clause will never be triggered.

Cullen O’Keefe: That certainly might be true. There’s a lot of reasons why that might be true. So, one is that we could all just be very wrong about the promise of AI. Also AI development could unfold in some other ways. So it could be a non-profit or an academic institution or a government that develops windfall generating AI and no one else does. Or it could just be that the windfall from AI is spread out sufficiently over a large number of firms, such that no one firm earns windfall, but collectively the tech industry does or something. So, that’s all certainly true. I think that those are all scenarios worth investing in addressing. You could potentially modify the Windfall Clause to address some of those scenarios.

hat said, I think there’s a significant non-trivial possibility that such a windfall occurs in a way that would trigger a Windfall Clause, and if it does, it seems worth investing in solutions that could mitigate any potential downside to that or share the benefits equally. Part of the benefit of the Windfall Clause is that if nothing happens, it doesn’t have any obligations. So, it’s quite low cost in that sense. From a philanthropic perspective, there’s a cost in setting this up and promoting the idea, etcetera, and those are definitely non-trivial costs. But the actual costs, signing the clause, only manifests upon actually triggering it.

Lucas Perry: This next one is that firms will find a way to circumvent their commitments under the clause. So it could never trigger because they could just keep moving money around in skillful ways such that the clause never ends up getting triggered. Some sub-points here are that firms will evade the clause by nominally assigning profits to subsidiary, parent or sibling corporations. That firms will evade the clause by paying out profits in dividends. That firms will sell all windfall generating AI assets to a firm that is not bound by the clause. Any thoughts on these here.

Cullen O’Keefe: First of all, a lot of these were raised by early commentators on the idea, and so I’m very thankful to those people for helping raise this. I think we probably haven’t exhausted the list of potential ways in which firms could evade their commitments, so in general I would want to come up with solutions that are not just patch work solutions, but also more like general incentive alignment solutions. That said, I think most of these problems are mitigable by careful contractual drafting. And then potentially also searching to other forms of the Windfall Clause like something based on firm share price. But still, I think there are probably a lot of ways to circumvent the clause in its kind of early form that we’ve proposed. And we would want to make sure that we’re pretty careful about drafting it and simulating potential ways that signatory could try to wriggle out of its commitment.

Cullen O’Keefe: I think it’s also worth noting that a lot of those potential actions would be pretty clear violations of general legal obligations that signatories to a contract have. Or could be mitigated with pretty easy contractual clauses.

Lucas Perry: Right. The solution to these would be foreseeing them and beefing up the actual windfall contract to not allow for these methods of circumvention.

Cullen O’Keefe: Yeah.

Lucas Perry: So now this next one I think is quite interesting. No firm with a realistic chance of developing windfall generating AI would sign the clause. How would you respond to that?

Cullen O’Keefe: I mean, I think that’s certainly a possibility, and if that’s the case, then that’s the case. It seems like our ability to change that might be pretty limited. I would hope that most firms in the potential position to be generating windfall, would take that opportunity as also carrying with it responsibility to follow the common good principle. And I think that a lot of people in those companies, both in leadership and the rank and file employee positions, do take that seriously. We do also think that the Windfall Clause could bring non-trivial benefits as we spent a lot of time talking about.

Lucas Perry: All right. The next one here is that quote, “If the public benefits of the Windfall Clause are supposed to be large, that is inconsistent with stating that the cost to firms will be small enough, that they would be willing to sign the clause.” This has a lot to do with this distinction with the ex-ante and the ex-post differences in cost. And also how there is probabilities and time involved here. So, your response to this objection.

Cullen O’Keefe: I think there’s some a-symmetries between the costs and benefit. Some of the costs are things that would happen in the future. So from a firms perspective, they should probably discount the costs of the Windfall Clause because if they earn windfall, it would be in future. From a public policy perspective, a lot of those benefits might not be as time sensitive. So you might no super-care when exactly those costs happen and therefore not really discount them from a present value standpoint.

Lucas Perry: You also probably wouldn’t want to live in the world in which there was no distribution mechanism or windfall function for allocating the windfall profits from one of your competitors.

Cullen O’Keefe: That’s an interesting question though, because a lot of corporate law principals suggest that firms should want to behave in a risk neutral sense, and then allow investors to kind of spread their bets according to their own risk tolerances. So, I’m not sure that this risks spreading between firms argument works that well.

Lucas Perry: I see. Okay. The next is that the Windfall Clause reduces incentives to innovate.

Cullen O’Keefe: So, I think it’s definitely true that it will probably have some effect on the incentive to innovate. That almost seems like kind of necessary or something. That said, I think people in our community are kind of the opinion that there are significant externalities to innovation and not all innovation towards AGI is strictly beneficial in that sense. So, making sure that those externalities are balanced seems important. And the Windfall Clause is one way to do that. In general, I think that the disincentive is probably just outweighed by the benefits of the Windfall Clause, but I would be open to reanalysis of that exact calculus.

Lucas Perry: Next objection is, the Windfall Clause will shift investment to competitive non-signatory firms.

Cullen O’Keefe: This was another particularly interesting comment and it has a potential perverse effect actually. Suppose you have two types of firms, you have nice firms and less nice firms. And all the nice firms sign the Windfall Clause. And therefore their future profit streams are taxed more heavily than the bad firms. And this is bad, because now investors will probably want to go to bad firms because they offer potentially more attractive return on investment. Like the previous objection, this is probably true to some extent. It kind of depends on the empirical case about how many firms you think are good and bad, and also what the exact calculus is regarding how much this disincentives investors from giving to good firms and causes the good firms to act better.

We do talk a little bit about different ways in which you could potentially mitigate this with careful mechanism design. So you could have the Windfall Clause consist in subordinated obligations but the firm could raise senior equity or senior debt to the Windfall Clause such that new investors would not be disadvantaged by investing in a firm that has signed the Windfall Clause. Those are kind of complicated mechanisms, and again, this is another point where thinking through this from a very careful micro-economic point in modeling this type of development dynamic would be very valuable.

Lucas Perry: All right. So we’re starting to get to the end here of objections or at least objections in the paper. The next is, the Windfall Clause draws attention to signatories in an undesirable way.

Cullen O’Keefe: I think the motivation for this objection is something like, imagine that tomorrow Boeing came out and said, “If we built a Death Star, we’ll only use it for good.” What are you talking about, building a Death Star? Why do you even have to talk about this? I think that’s kind of the motivation, is talking about earning windfall is itself drawing attention to the firm in potentially undesirable ways. So, that could potentially be the case. I guess the fact that we’re having this conversation suggests that this is not a super-taboo subject. I think a lot of people are generally aware of the promise of artificial intelligence. So the idea that the gains could be huge and concentrated in one firm, doesn’t seem that worrying to me. Also, if a firm was super close to AGI or something, it would actually be much harder for them to sign on to the Windfall Clause, because the costs would be so great to them in expectation, that they probably couldn’t justify it from a fiduciary duty standpoint.

So in that sense, signing on to the Windfall Clause at least from a purely rational standpoint, is kind of negative evidence that a firm is close to AGI. That said, there is certainly psychological elements that complicate that. It’s very cheap for me to just make a commitment that says, oh sure if I get a trillion dollars, I’ll give 75% of it some charity. Sure, why not? I’ll make that commitment right now in fact.

Lucas Perry: It’s kind of more efficacious if we get firms to adopt this sooner rather than later, because as time goes on, their credences in who will hit AI windfall will increase.

Cullen O’Keefe: Yeah. That’s exactly right. Assuming timelines are constant, the clock is ticking on stuff like this. Every year that goes by, committing to this gets more expensive to firms, and therefore rationally, less likely.

Lucas Perry: All right. I’m not sure that I understand this next one, but it is, the Windfall Clause will lead to moral licensing. What does that mean?

Cullen O’Keefe: So moral licensing is a psychological concept, that if you do certain actions that either are good or appear to be good, that you’re more like to do bad things later. So you have a license to act immorally because of the times that you acted morally. I think a lot of times this is a common objection to corporate philanthropy. People call this ethics washing or green washing, in the context of environmental stuff specifically. I think you should again, do pretty careful cost benefit analysis here to see whether the Windfall Clause is actually worth the potential licensing effect that it has. But of course, one could raise this objection to pretty much any pro-social act. Given that we think the Windfall Clause could actually have legally enforceable teeth, it seems kind of less likely unless you think that the licensing effects would just be so great that they’ll overcome the benefits of actually having an enforceable Windfall Clause. It seems kind of intuitively implausible to me.

Lucas Perry: Here’s another interesting one. The rule of law might not hold if windfall profits are achieved. Human greed and power really kicks in and the power structures which are meant to enforce the rule of law no longer are able to, in relation to someone with AGI or superintelligence. How do you feel about this objection?

Cullen O’Keefe: I think it’s a very serious one. I think it’s something that perhaps the AI safety maybe should be investing more in. I’m also having an interesting discussion, asynchronously on this with Rohin Shah on the EA Forum. I do think there’s a significant chance that if you have an actor that is potentially as powerful as a corporation with AGI and all the benefits that come with that at its disposal, could be such that it would be very hard to enforce the Windfall Clause against it. That said, I think we do kind of see Davids beating Goliaths in the law. People do win lawsuits against the United States government or very large corporations. So it’s certainly not the case that size is everything, though it would be naïve to suppose that it’s not correlated with the probability of winning.

Other things to worry about, are the fact that this corporation will have very powerful AI that could potentially influence the outcome of cases in some way or perhaps hide ways in which it was evading the Windfall Clause. So, I think that’s worth taking seriously. I guess just in general, I think this issue is worth a lot of investment from the AI safety and AI policy communities, for reasons well beyond the Windfall Clause. And it seems like a problem that we’ll have to figure out how to address.

Lucas Perry: Yeah. That makes sense. You brought up the rule of law not holding up because of its power to win over court cases. But the kind of power that AGI would give, would also potentially far extend beyond just winning court cases right? In your ability to not be bound by the law.

Cullen O’Keefe: Yeah. You could just act as a thug and be beyond the law, for sure.

Lucas Perry: It definitely seems like a neglected point, in terms of trying to have a good future with beneficial AI.

Cullen O’Keefe: I’m kind of the opinion that this is pretty important. It just seems like that this is just also a thing in general, that you’re going to want of a post-AGI world. You want the actor with AGI to be accountable to something other than its own will.

Lucas Perry: Yeah.

Cullen O’Keefe: You want agreements you make before AGI to still have meaning post-AGI and not just depend on the beneficence of the person with AGI.

Lucas Perry: All right. So the last objection here is, the Windfall Clause undesirably leaves control of advanced AI in private hands.

Cullen O’Keefe: I’m somewhat sympathetic to the argument that AGI is just such an important technology that it ought to be governed in a pro-social way. Basically, this project doesn’t have a good solution to that, other than to the extent that you could use Windfall Clause funds to perhaps purchase share stock from the company or have a commitment in shares of stock rather than in money. On the other hand, private companies are doing a lot of very important work right now, in developing AI technologies and are kind of the current leading developers of advanced AI. It seems to me like their behaving pretty responsibility overall. I’m just not sure what the ultimate ideal arrangement of ownership of AI will look like and want to leave that open for other discussion.

Lucas Perry: All right. So we’ve hit on all of these objections, surely there are more objections, but this gives a lot for listeners and others to consider and think about. So in terms of alternatives for the Windfall Clause, you list four things here. They are windfall profits should just be taxed. We should rely on anti-trust enforcement instead. We should establish a sovereign wealth fund for AI. We should implement a universal basic income instead. So could you just go through each of these sequentially and give us some thoughts and analysis on your end?

Cullen O’Keefe: Yeah. We talked about taxes already, so is it okay if I just skip that?

Lucas Perry: Yeah. I’m happy to skip taxes. The point there being that they will end up only serving the country in which they are being taxed, unless that country has some other mechanism for distributing certain kinds of taxes to the world.

Cullen O’Keefe: Yeah. And it also just seems much more tractable right now to work on, private commitments like the Windfall Clause rather than lobbying for pretty robust tax code.

Lucas Perry: Sure. Okay, so number two.

Cullen O’Keefe: So number two is about anti-trust enforcement. This was largely spurred by a conversation with Haydn Belfield. The idea here is that in this world, the AI developer will probably be a monopoly or at least extremely powerful in its market, and therefore we should consider anti-trust enforcement against it. I guess my points are two-fold. Number one is that just under American law, it is pretty clear that merely possessing monopoly power is not itself a reason to take anti-trust action. You have to have acquired that monopoly power in some illegal way. And if some of the stronger hypothesis about AI are right, AI could be a natural monopoly and so it seems pretty plausible that an AI monopoly could develop without any illegal actions taken to gain that monopoly.

I guess second, the Windfall Clause addresses some of the harms from monopoly, though not all of them, by transferring some wealth from shareholders to everyone and therefore transferring some wealth from shareholders to consumers.

Lucas Perry: Okay. Could focusing on anti-trust enforcement alongside the Windfall Clause be beneficial?

Cullen O’Keefe: Yeah. It certainty could be. I don’t want to suggest that we ought not to consider anti-trust, especially if there’s a natural reason to break up firms or if there’s a natural violation of anti-trust law going on. I guess I’m pretty sympathetic to the anti-trust orthodoxy that monopoly is not in itself a reason in itself to break up a firm. But I certainly think that we should continue to think about anti-trust as a potential response to these situations.

Lucas Perry: All right. And number three is we should establish a sovereign wealth fund for AI.

Cullen O’Keefe: So this is an idea that actually came out of FLI. Anthony Aguirre has been thinking about this. The idea is to set up something that looks like the sovereign wealth funds that I alluded to earlier, that places like Norway and other resource rich countries have. Some better and some worse governed, I should say. And I think Anthony’s suggestion was to set this up as a fund that held shares of stock of the corporation, and redistributed wealth in that way. I am sympathetic to this idea overall as I mentioned, I think stock based Windfall Clause could be potentially be an improvement over the cash based one that we suggest. That said, I think there are significant legal problems here if that’s kind of make this harder to imagine working. For one thing, it’s hard to imagine the government buying up all these shares of stock companies, just to acquire a significant portion of them so that you have a good probability of capturing a decent percentage of future windfall, you would have to just spend a ton of money.

Secondly, they couldn’t expropriate the shares of stock, but it would require just compensation under the US Constitution. Third, there are ways that corporations can prevent from accumulating a huge share of its stock if they don’t want it to, the poison pills, the classic example. So if the firms didn’t want a sovereign automation fund to buy up significant shares of their fund, which they might not want to since it might not govern in the best interest of other shareholders, they could just prevent it from acquiring a controlling stake. So all those seem like pretty powerful reasons why contractual mechanisms might be preferable to that kind of sovereign automation fund.

Lucas Perry: All right. And the last one here is, we should implement a universal basic income instead.

Cullen O’Keefe: Saving kind of one of the most popular suggestions for last. This isn’t even really an alternative to the Windfall Clause, it’s just one way that the Windfall Clause could look. And ultimately I think UBI is a really promising idea that’s been pretty well studied. Seems to be pretty effective. It’s obviously quite simple, has widespread appeal. And I would be probably pretty sympathetic to a Windfall Clause that ultimately implements a UBI. That said, I think there are some reasons that you might you prefer other forms of windfall distribution. So one is just that UBI doesn’t seem to target people particularly harmed by AI for example, if we’re worried about a future with a lot of automation of jobs. UBI might not be the best way to compensate those people that are harmed.

Others address that it might not be the best opportunity for providing public goods, if you thought that that’s something that the Windfall Clause should do, but I think it could be a very promising part of the Windfall Clause distribution mechanism.

Lucas Perry: All right. That makes sense. And so wrapping up here, are there any last thoughts you’d like to share with anyone particularly interested in the Windfall Clause or people in policy in government who may be listening or anyone who might find themselves at a leading technology company or AI lab?

Cullen O’Keefe: Yeah. I would encourage them to get in touch with me if they’d like. My email address is listed in the report. I think just in general, this is going to be a major challenge for society in the next century. At least it could be. As I said, I think there’s substantial uncertainty about a lot of this, so I think there’s a lot of potential opportunities to do research, not just in economics and law, but also in political science and thinking about how we can govern the windfall that artificial intelligence brings, in a way that’s universally beneficial. So I hope that other people will be interested in exploring that question. I’ll be working with the Partnership on AI to help think through this as well and if you’re interested in those efforts and have expertise to contribute, I would very much appreciate people getting touch, so they can get involved in that.

Lucas Perry: All right. Wonderful. Thank you and everyone else who helped to help work on this paper. It’s very encouraging and hopefully we’ll see widespread adoption and maybe even implementation of the Windfall Clause in our lifetime.

Cullen O’Keefe: I hope so too, thank you so much Lucas.

AI Alignment Podcast: On the Long-term Importance of Current AI Policy with Nicolas Moës and Jared Brown

 Topics discussed in this episode include:

  • The importance of current AI policy work for long-term AI risk
  • Where we currently stand in the process of forming AI policy
  • Why persons worried about existential risk should care about present day AI policy
  • AI and the global community
  • The rationality and irrationality around AI race narratives

Timestamps: 

0:00 Intro

4:58 Why it’s important to work on AI policy 

12:08 Our historical position in the process of AI policy

21:54 For long-termists and those concerned about AGI risk, how is AI policy today important and relevant? 

33:46 AI policy and shorter-term global catastrophic and existential risks

38:18 The Brussels and Sacramento effects

41:23 Why is racing on AI technology bad? 

48:45 The rationality of racing to AGI 

58:22 Where is AI policy currently?

 

We hope that you will continue to join in the conversations by following us or subscribing to our podcasts on Youtube, Spotify, SoundCloud, iTunes, Google Play, StitcheriHeartRadio, or your preferred podcast site/application. You can find all the AI Alignment Podcasts here.

You can listen to the podcast above or read the transcript below. 

Lucas Perry: Welcome to the AI Alignment Podcast. I’m Lucas Perry. Today’s episode is with Jared Brown and Nicolas Moës, two AI Policy researchers and AI influencers who are both concerned with the long-term and existential risks associated with artificial general intelligence and superintelligence. For us at the the Future of Life Institute, we’re particularly interested in mitigating threats from powerful AI that could lead to the extinction of life. One avenue of trying to address such threats could be through action in the space of AI policy. But just what can we do today to help ensure beneficial outcomes from AGI and superintelligence in the policy sphere? This podcast focuses on this question.

As for some key points to reflect on throughout the podcast, Nicolas Moes points out that engaging in AI policy today is important because: 1) Experience gained on short-term AI policy issues is important to be considered a relevant advisor on long-term AI policy issues coming up in the future. 2) There are very few people that care about AGI safety currently in government, politics or in policy communities. 3) There are opportunities to influence current AI policy decisions in order to provide a fertile ground for future policy decisions or, better but rarer, to be directly shaping AGI safety policy today though evergreen texts. Future policy that is implemented is path dependent on current policy that we implement today. What we do now is precedent setting. 4) There are opportunities today to develop a skillset useful for other policy issues and causes. 5) Little resource is being spent on this avenue for impact, so the current return on investment is quite good.

Finally I’d like to reflect on the need to bridge the long-term and short-term partitioning of AI risk discourse. You might have heard this divide before, where there are long-term risks from AI, like a long-term risk being powerful AGI or superintelligence misaligned with human values causing the extinction of life, and then short-term risk like algorithmic bias and automation induced disemployment. Bridging this divide means understanding the real and deep interdependencies and path dependencies between the technology and governance which choose to develop today, and the world where AGI or superintelligence emerges. 

For those not familiar with Jared Brown or Nicolas Moës, Nicolas is an economist by training focused on the impact of Artificial Intelligence on geopolitics, the economy and society. He is the Brussels-based representative of The Future Society. Passionate about global technological progress, Nicolas monitors global developments in the legislative framework surrounding AI. He completed his Masters degree in Economics at the University of Oxford with a thesis on institutional engineering for resolving the tragedy of the commons in global contexts. 

Jared is the Senior Advisor for Government Affairs at FLI, working to reduce global catastrophic and existential risk (GCR/x-risk) by influencing the U.S. policymaking process, especially as it relates to emerging technologies. He is also a Special Advisor for Government Affairs at the Global Catastrophic Risk Institute. He has spent his career working at the intersection of public policy, emergency management, and risk management, having previously served as an Analyst in Emergency Management and Homeland Security Policy at the U.S. Congressional Research Service and in homeland security at the U.S. Department of Transportation.

The Future of Life Institute is a non-profit and this podcast is funded and supported by listeners like you. So if you find what we do on this podcast to be important and beneficial, please consider supporting the podcast by donating at futureoflife.org/donate. These contributions make it possible for us to bring you conversations like these and to develop the podcast further. You can also follow us on your preferred listening platform by searching for us directly or following the links on the page for this podcast found in the description.

And with that, here is Jared Brown and Nicolas Moës on AI policy. 

I guess we can start off here, with developing the motivations around why it’s important for people to be considering AI policy. So, why is it important to be working on AI policy right now?

Nicolas Moës: It’s important right now because there has been an uptick in markets, right? So AI technologies are now embedded in many more products than ever before. Part of it is hype, but part of it is also having a real impact on profits and bottom line. So there is an impact on society that we have never seen before. For example, the way Facebook algorithms have affected recent history is something that has made the population and policy makers panic a bit.

And so quite naturally the policy window has opened. I think it’s also important to be working on it for people who would like to make the world better for two reasons. As I mentioned, since the policy window is open that means that there is a demand for advice to fill in the gaps that exist in the legislation, right? There have been many concrete situations where, as an AI policy researcher, you get asked to provide input either by joining expert group, or workshops or simply directly some people who say, “Oh, you know about AI, so could you just send me a position paper on this?”

Nicolas Moës: So these policies are getting written right now, which at first is quite soft and then becomes harder and harder policies, and now to the point that at least in the EU, you have regulations for AI on the agenda, which is one of the hardest form of legislation out there. Once these are written it is very difficult to change them. It’s quite sticky. There is a lot of path dependency in legislation. So this first legislation that passes, will probably shape the box in which future legislation can evolve. Its constraints, the trajectory of future policies, and therefore it’s really difficult to take future policies in another direction. So for people who are concerned about AGI, it’s important to be already present right now.

The second point, is that these people who are currently interacting with policymakers on a daily basis are concerned about very specific things and they are gaining a lot of experience with policymakers, so that in the future when you have more general algorithms that come into play, the people with experience to advise on these policies will actually be concerned about what many people call short term issues. People who are concerned more about the safety, the robustness of these more general algorithm would actually end up having a hard time getting into the room, right? You cannot just walk in and claim authority when you have people with 10, 15 or even 20 years of experience regulating this particular field of engineering.

Jared Brown: I think that sums it up great, and I would just add that there are some very specific examples of where we’re seeing what has largely been, up to this point, a set of principles being developed by different governments, or industry groups. We’re now seeing attempts to actually enact hard law or policy.

Just in the US, the Office of Management and Budget and the Office of Science and Technology Policy issued a memorandum calling for further AI regulation and non-regulatory actions and they issued a set of principles, that’s out for comment right now, and people are looking at those principles, trying to see if there’s ways of commenting on it to increase its longterm focus and its ability to adapt to increasingly powerful AI.

The OECD has already issued, and had sign ons to its AI principles, which are quite good.

Lucas Perry: What is the OECD?

Nicolas Moës: The Organization for Economic Cooperation and Development.

Jared Brown: Yes. Those principles are now going from principles to an observatory, and that will be launched by the end of February. And we’re seeing the effect of these principles now being adopted, and attempts now are being made to implement those into real regulatory approaches. So, the window from transitioning from principles to hard law is occurring right now, and as Nicholas said, decisions that are made now will have longterm effects because typically governments don’t turn their attention to issues more than once every five, maybe even 10 years. And so, if you come in three years from now with some brilliant idea about AI policy, chances are, the moment to enact that policy has already passed because the year prior, or two years prior, your government has enacted its formative legislation on AI.

Nicolas Moës: Yeah, yeah. So long as this policy benefits most people, they are very unlikely to even reopen, let’s say, the discussion, at all.

Lucas Perry: Right. So a few points here. The first is this one about path dependency, which means that the kinds of policies which we adopt now are going to be really important, because they’re going to inform and shape the kinds of policies that we’re able or willing to adopt later, and AI is going to be around for a long, long time. So we’re setting a lot of the foundation. The second thing was that if you care about AGI risk, or the risks of superintelligence, or very powerful forms of AI that you need to have been part of the conversation since the beginning, or else you’re not going to really be able to get a seat at the table when these things come around.

And Jared, is there a point here that I’m missing that you were trying to make?

Jared Brown: No, I think that sums it up nicely. The effect of these policies, and the ability of these policies to remain what you might call evergreen. So, long lasting and adaptive to the changing nature of AI technology is going to be critical. We see this all the time in tech policy. There are tech policies out there that were informed by the challenges of the time in which they were made and they quickly become detrimental, or outdated at best. And then there are tech policies that tend to be more adaptive, and those stand the test of time. And we need to be willing to engage with the short term policy making considerations, such that we’re making sure that the policies are evergreen for AI, as it becomes increasingly powerful.

Nicolas Moës: Besides the evergreen aspects of the policies that you want to set up now, there’s this notion of providing a fertile ground. So some policies that are very appropriate for short term issues, for example, fairness and deception, and fundamental rights abuse and that kind of thing, are actually almost copy pasted to future legislation. So, if you manage to already put concerns for safety, like robustness, corrigibility, and value alignment of the algorithm today, even if you don’t have any influence in 10 or 15 years when they review the legislation, you have some chances to see the policymakers just copy pasting this part on safety and to put it in whatever new legislation comes up in 10 years.

Jared Brown: There’s precedent setting, and legislators are woe to have to make fundamental reforms to legislation, and so if we see proper consideration of safety and security on AI in the evergreen pieces of legislation that are being developed now, that’s unlikely to be removed in future legislation.

Lucas Perry: Jared, you said that a lot of the principles and norms which have been articulated over say, the past five years are becoming codified into hard law slowly. It also would just be good if you guys could historically contextualize our position in terms of AI policy, whether or not we stand at an important inflection point, where we are in terms of this emerging technology.

Jared Brown: Sure, sure. So, I think if you went back just to 2017, 2016, at least in the US, there was very little attention to artificial intelligence. There were a smattering of congressional hearings being held, a few pertinent policy documents being released by executive agencies, but by and large, the term artificial intelligence remained in the science fiction realm of thinking.

Since that time, there’s been a massive amount of attention paid to artificial intelligence, such that in almost every Western democracy that I’m familiar with, it’s now part of the common discourse about technology policy. The phrase emerging tech is something that you see all over the place, regardless of the context, and there’s a real sensitivity by Western style democracy policymakers towards this idea that technology is shifting under our feet. There’s this thing called artificial intelligence, there’s this thing called synthetic biology, there’s other technologies linked into that — 5G and hypersonics are two other areas — where there’s a real understanding that something is changing, and we need to get our arms around it. Now, that has largely started with, in the past year, or year and a half, a slew of principles. There are at least 80 some odd sets of principles. FLI was one of the first to create a set of principles, along with many partners, and those are the Asilomar AI Principles.

Those principles you can see replicated and informing many sets of principles since then. We mentioned earlier, the OECD AI principles are probably the most substantive and important at this point, because they have the signature and backing of so many sovereign nation states, including the United States and most of the EU. Now that we have these core soft law principles, there’s an appetite for converting that into real hard law regulation or approaches to how AI will be managed in different governance systems.

What we’re seeing in the US, there’s been a few regulatory approaches already taken. For instance, rule making on the inclusion of AI algorithms into the housing market. This vision, if you will, from the Department of Transportation, about how to deal with autonomous vehicles. The FDA has approved products coming into the market that involve AI and diagnostics in the healthcare industry, and so forth. We’re seeing initial policies being established, but what we haven’t yet seen in any real context, is sort of a cross-sectoral AI broadly-focused piece of legislation or regulation.

And that’s what’s currently being developed both in the EU and in the US. That type of legislation, which seems like a natural evolution from where we’re at with principles, into a comprehensive holistic approach to AI regulation and legislation, is now occurring. And that’s why this time is so critical for AI policy.

Lucas Perry: So you’re saying that a broader and more holistic view about AI regulation and what it means to have and regulate beneficial AI is developed before more specific policies are implemented, with regards to the military, or autonomous weapons, or healthcare, or nuclear command and control.

Jared Brown: So, typically, governments try, whether or not they succeed remains to be seen, to be more strategic in their approach. If there is a common element that’s affecting many different sectors of society, they try and at least strategically approach that issue, to think: what is common across all policy arenas, where AI is having an effect, and what can we do to legislate holistically about AI? And then as necessary, build sector specific policies on particular issues.

So clearly, you’re not going to see some massive piece of legislation that covers all the potential issues that has to do with autonomous vehicles, labor displacement, workforce training, et cetera. But you do want to have an overarching strategic plan for how you’re regulating, how you’re thinking about governing AI holistically. And that’s what’s occurring right now, is we have the principles, now we need to develop that cross-sectoral approach, so that we can then subsequently have consistent and informed policy on particular issue areas as they come up, and as they’re needed.

Lucas Perry: And that cross-sectoral approach would be something like: AI should be interpretable and robust and secure.

Jared Brown: That’s written in principles to a large degree. But now we’re seeing, what does that really mean? So in the EU they’re calling it the European Approach to AI, and they’re going to be coming out with a white paper, maybe by the time this podcast is released, and that will sort of be their initial official set of options and opinions about how AI can be dealt with holistically by the EU. In the US, they’re setting regulatory principles for individual regulatory agencies. These are principles that will apply to the FDA, or the Department of Transportation, or the Department of Commerce, or the Department of Defense, as they think about how they deal with the specific issues of AI in their arenas of governance. Making sure that baseline foundation is informed and is an evergreen document, so that it incorporates future considerations, or is at least adaptable to future technological development in AI is critically important.

Nicolas Moës: With regards to the EU in particular, the historical context is maybe a bit different. As you mentioned, right now they are discussing this white paper with many transversal policy instruments that would be put forward, with this legislation. This is going to be negotiated over the next year. There is intentions to have the legislation at the EU level by the end of the current commission’s term. So that’s mean within five years. This is something that is quite interesting to explore, is that in 2016 there was this parliamentary dossier on initiative, so it’s something that does not have any binding power, just to show the opinion of the European parliament, that was dealing with robotics and civil laws. So, considering how civil law in Europe should be adjusted to robotics.

That was in 2016, right? And now there’s been this uptick in activities. This is something that we have to be aware of. It’s moved quite fast, but then again, there still is a couple of years before regulations get approved. This is one point that I wanted to clarify about, when we say it is fast or it is slow, we are talking still about a couple of years. Which is, when you know how long it takes for you to develop your network, to develop your understanding of the issues, and to try to influence the issues, a couple of years is really way too short. The second point I wanted to make is also, what will the policy landscape look like in two years? Will we have the EU again leveraging its huge market power to impose its regulations within the European Commission. There are some intentions to diffuse whatever regulations come out of the European Commission right now, throughout the world, right? To form a sort of influence sphere, where all the AI produced, even abroad, would actually be fitting EU standards.

Over the past two, three years there have been a mushrooming of AI policy players, right? The ITU has set up this AI For Good, and has reoriented its position towards AI. There has been the Global Forum on AI for Humanity, political AI summits, which kind of pace the discussions about the global governance of artificial intelligence.

But would there be space for new players in the future? That’s something that I’m a bit unsure. One of the reasons why it might be an inflection point, as you asked, is because now I think the pawns are set on the board, right? And it is unlikely that somebody could come in and just disturb everything. I don’t know in Washington how it plays, but in Brussels it seems very much like everybody knows each other already and it’s only about bargaining with each other, not especially listening to outside views.

Jared Brown: So, I think the policy environment is being set. I wouldn’t quite go so far as to say all of the pawns are on the chess board, but I think many of them are. The queen is certainly industry, and industry has stood up and taken notice that governments want to regulate and want to be proactive about their approach to artificial intelligence. And you’ve seen this, because you can open up your daily newspaper pretty much anywhere in the world and see some headline about some CEO of some powerful tech company mentioning AI in the same breath as government, and government action or government regulations.

Industry is certainly aware of the attention that AI is getting, and they are positioning themselves to influence that as much as possible. And so civil society groups such as the ones Nico and I represent have to step up, which is not to say the industry has all bad ideas, some of what they’re proposing is quite good. But it’s not exclusively a domain controlled by industry opinions about the regulatory nature of future technologies.

Lucas Perry: All right. I’d like to pivot here, more into some of the views and motivations the Future of Life Institute and the Future Society take, when looking at AI policy. The question in particular that I’d like to explore is how is current AI policy important for those concerned with AGI risk and longterm considerations about artificial intelligence growing into powerful generality, and then one day surpassing human beings in intelligence? For those interested in the issue of AGI risk or super intelligence risk, is AI policy today important? Why might it be important? What can we do to help shape or inform the outcomes related to this?

Nicolas Moës: I mean, obviously, I’m working full time on this and if I could, I would work double full time on this. So I do think it’s important. But it’s still too early to be talking about this in the policy rooms, at least in Brussels. Even though we have identified a couple of policymakers that would be keen to talk about that. But it’s politically not feasible to put forward these kind of discussions. However, AI policy currently is important because there is a demand for advice, for policy research, for concrete recommendations about how to govern this technological transition that we are experiencing.

So there is this demand where people who are concerned about fundamental rights, and safety, and robustness, civil society groups, but also academics and industry themselves sometime come in with their clear recommendations about how you should concretely regulate, or govern, or otherwise influence the development and deployment of AI technologies, and in that set of people, if you have people who are concerned about safety, you would be able then, to provide advice for providing evergreen policies, as we’ve mentioned earlier and set up, let’s say, a fertile ground for better policies in the future, as well.

The second part of why it’s important right now is also the longterm workforce management. If people who are concerned about the AGI safety are not in the room right now, and if they are in the room but focused only on AGI safety, they might be perceived as irrelevant by current policymakers, and therefore they might have restricted access to opportunities for gaining experience in that field. And therefore over the long term this dynamic reduces the growth rate, let’s say, of the workforce that is concerned about AGI safety, and that could be identified as a relevant advisor in the future. As a general purpose technology, even short term issues regarding AI policy have a long term impact on the whole of society.

Jared Brown: Both Nicholas and I have used this term “path dependency,” which you’ll hear a lot in our community and I think it really helps maybe to build out that metaphor. Various different members of the audience of this podcast are going to have different timelines in their heads when they think about when AGI might occur, and who’s going to develop it, what the characteristics of that system will be, and how likely it is that it will be unaligned, and so on and so forth. I’m not here to engage in that debate, but I would encourage everyone to literally think about whatever timeline you have in your head, or whatever descriptions you have for the characteristics that are most likely to occur when AGI occurs.

You have a vision of that future environment, and clearly you can imagine different environments by which humanity is more likely to be able to manage that challenge than other environments. An obvious example, if the world were engaged in World War Three, 30 years from now, and some company develops AGI, that’s not good. It’s not a good world for AGI to be developed in, if it’s currently engaged in World War Three at the same time. I’m not suggesting we’re doing anything to mitigate World War Three, but there are different environments for when AGI can occur that will make it more or less likely that we will have a beneficial outcome from the development of this technology.

We’re literally on a path towards that future. More government funding for AI safety research is a good thing. That’s a decision that has to get made, that’s made every single day, in governments all across the world. Governments have R&D budgets. How much is that being spent on AI safety versus AI capability development? If you would like to see more, then that decision is being made every single fiscal year of every single government that has an R&D budget. And what you can do to influence it is really up to you and how many resources you’re going to put into it.

Lucas Perry: Many of the ways it seems that AI policy currently is important for AGI existential risk are indirect. Perhaps it’s direct insofar as there’s these foundational evergreen documents, and maybe changing our trajectory directly is kind of a direct intervention.

Jared Brown: How much has nuclear policy changed? When our governance of nuclear weapons changed because the US initially decided to use the weapon. That decision irrevocably changed the future of Nuclear Weapons Policy, and there is no way you can counterfactually unspool all of the various different ways the initial use of the weapon, not once, but twice by the US sent a signal to the world A, the US was willing to use this weapon and the power of that weapon was on full display.

There are going to be junctures in the trajectory of AI policy that are going to be potentially as fundamental as whether or not the US should use a nuclear weapon at Hiroshima. Those decisions are going to be hard to see necessarily right now, if you’re not in the room and you’re not thinking about the way that policy is going to project into the future. That’s where this matters. You can’t unspool and rerun history. We can’t decide for instance, on lethal autonomous weapons policy. There is a world that exists, a future scenario 30 years from now, where international governance has never been established on lethal autonomous weapons. And lethal autonomous weapons is completely the norm for militaries to use indiscriminately or without proper safety at all. And then there’s a world where they’ve been completely banned. Those two conditions will have serious effect on the likelihood that governments are up to the challenge of addressing potential global catastrophic and existential risk arising from unaligned AGI. And so it’s more than just setting a path. It’s central to the capacity building of our future to deal with these challenges.

Nicolas Moës: Regarding other existential risks, I mean Jared is more of an expert on that than I am. In the EU, because this topic is so hot, it’s much more promising, let’s say as an avenue for impact, than other policy dossiers because we don’t have the omnibus type of legislation that you have in the US. The EU remains quite topic for topic. In the end, there is very little power embeded in the EU, mostly it depends on the nation states as well, right?

So AI is as moves at the EU level, which makes you want to walk at the EU level AI policy for sure. But for the other issues, it sometimes remains still at the national level. That’d being said, the EU also has this particularity, let’s say off being able to reshape debates at the national level. So, if there were people to consider what are the best approaches to reduce existential risk in general via EU policy, I’m sure there would be a couple of dossiers right now with policy window opens that could be a conduit for impact.

Jared Brown: If the community of folks that are concerned about the development of AGI are correct and that it may have potentially global catastrophic and existential threat to society, then you’re necessarily obviously admitting that AGI is also going to affect the society extremely broadly. It’s going to be akin to an industrial revolution, as is often said. And that’s going to permeate every which way in society.

And there’s been some great work to scope this out. For instance, in the nuclear sphere, I would recommend to all the audience that they take a look at a recent edited compendium of papers by the Stockholm International Peace Research Institute. They have a fantastic compendium of papers about AI’s effect on strategic stability in nuclear risk. That type of sector specific analysis can be done with synthetic biology and various other things that people are concerned about as evolving into existential or global catastrophic risk.

And then there are current concerns with non anthropomorphic risk. AI is going to be tremendously helpful if used correctly to track and monitor near earth objects. You have to be concerned about asteroid impacts. AI is a great tool to be used to help reduce that risk by monitoring and tracking near Earth objects.

We may yet make tremendous discoveries in geology to deal with supervolcanoes. Just recently there’s been some great coverage of a AI company called Blue Dot for monitoring the potential pandemics arising with the Coronavirus. We see these applications of AI very beneficially reducing other global catastrophic and existential risks, but there are aggravating factors as well, especially for other anthropomorphic concerns related to nuclear risk and synthetic biology.

Nicolas Moës: Some people who are concerned about is AGI sometimes might see AI as overall negative in expectation, but a lot of policy makers see AI as an opportunity more than as a risk, right? So, starting with a negative narrative or a pessimistic narrative is difficult in the current landscape.

In Europe it might be a bit easier because for odd historical reasons it tends to be a bit more cautious about technology and tends to be more proactive about regulations than maybe anywhere else in the world. I’m not saying whether it’s a good thing or a bad thing. I think there’s advantages and disadvantages. It’s important to know though that even in Europe you still have people who are anti-regulation. The European commission set this independent high level expert group on AI with 52 or 54 experts on AI to decide about the ethical principles that will inform the legislation on AI. So this was for the past year and a half, or the past two years even. Among them, the divisions are really important. Some of them wanted to just let it go for self-regulation because even issues of fairness or safety will be detected eventually by society and addressed when they arise. And it’s important to mention that actually in the commission, even though the current white paper seems to be more on the side of preventive regulations or proactive regulations, the commissioner for digital, Thierry Breton is definitely cautious about the approach he takes. But you can see that he is quite positive about the potential of technology.

The important thing here as well is that these players have an influential role to play on policy, right? So, going back to this negative narrative about AGI, it’s also something where we have to talk about how you communicate and how you influence in the end the policy debate, given the current preferences and the opinions of people in society as a whole, not only the opinions of experts. If it was only about experts, it would be maybe different, but this is politics, right? The opinion of everybody matters and it’s important that whatever influence you want to have on AI policy is compatible with the rest of society’s opinion.

Lucas Perry: So, I’m curious to know more about the extent to which the AI policy sphere is mindful of and exploring the shorter term global catastrophic or maybe even existential risks that arise from the interplay of more near term artificial intelligence with other kinds of technologies. Jared mentioned a few in terms of synthetic biology, and global pandemics, and autonomous weapons, and AI being implemented in the military and early warning detection systems. So, I’m curious to know more about the extent to which there are considerations and discussions around the interplay of shorter term AI risks with actual global catastrophic and existential risks.

Jared Brown: So, there’s this general understanding, which I think most people accept, that AI is not magical. It is open to manipulation, it has certain inherent flaws in its current capability and constructs. We need to make sure that that is fully embraced as we consider different applications of AI into systems like nuclear command and control. At a certain point in time, the argument could be sound that AI is a better decision maker than your average set of humans in a command and control structure. There’s no shortage of instances of near misses with nuclear war based on existing sensor arrays, and so on and so forth, and the humans behind those sensor arrays, with nuclear command and control. But we have to be making those evaluations fully informed about the true limitations of AI and that’s where the community is really important. We have to cut through the hype and cut through overselling what AI is capable of, and be brutally honest about the current limitations of AI as it evolves, and whether or not it makes sense from a risk perspective to integrate AI in certain ways.

Nicolas Moës: There has been human mistakes that have led to close calls, but I believe these close calls have been corrected because of another human in the loop. In early warning systems though, you might actually end up with no human in the loop. I mean, again, we cannot really say whether these humans in the loop were statistically important because we don’t have the alternatives obviously to compare it to.

Another thing regarding whether some people think that AI is magic, I, I think, would be a bit more cynical. I still find myself in some workshops or policy conferences where you have some people who apparently haven’t seen ever a line of code in their entire life and still believe that if you tell the developer “make sure your AI is explainable,” that magically the AI would become explainable. This is still quite common in Brussels, I’m afraid. But there is a lot of heterogeneity. I think now we have, even among the 705 MEPs, there is one of them who is a former programmer from France. And that’s the kind of person who, given his expertise, if he was placed on the AI dossier, I guess he would have a lot more influence because of his expertise.

Jared Brown: Yeah. I think in the US there’s this phrase that kicks around that the US is experiencing a techlash, meaning there’s a growing reluctance, cynicism, criticism of major tech industry players. So, this started with the Cambridge Analytica problems that arose in the 2016 election. Some of it’s related to concerns about potential monopolies. I will say that it’s not directly related to AI, but that general level of criticism, more skepticism, is being imbued into the overall policy environment. And so people are more willing to question the latest, next greatest thing that’s coming from the tech industry because we’re currently having this retrospective analysis of what we used to think of a fantastic and development may not be as fantastic as we thought it was. That kind of skepticism is somewhat helpful for our community because it can be leveraged for people to be more willing to take a critical eye in the way that we apply technology going forward, knowing that there may have been some mistakes made in the past.

Lucas Perry: Before we move on to more empirical questions and questions about how AI policy is actually being implemented today, are there any other things here that you guys would like to touch on or say about the importance of engaging with AI policy and its interplay and role in mitigating both AGI risk and existential risk?

Nicolas Moës: Yeah, the so called Brussels effect, which actually describes that whatever decisions in European policy that is made is actually influencing the rest of the world. I mentioned it briefly earlier. I’d be curious to hear what you, Jared, thinks about that. In Washington, do people consider it, the GDPR for example, as a pre made text that they can just copy paste? Because apparently, I know that California has released something quite similar based on GDPR. By the way, GDPR is the General Data Protection Regulations governing protection of privacy in the EU. It’s a regulation, so it has a binding effect on EU member States. That, by the Brussels effect, what I mean is that for example, this big piece of legislation as being, let’s say, integrated by big companies abroad, including US companies to ensure that they can keep access to the European market.

And so the commission is actually quite proud of announcing that for example, some Brazilian legislator or some Japanese legislator or some Indian legislators are coming to the commission to translate the text of GDPR, and to take it back to their discussion in their own jurisdiction. I’m curious to hear what you think of whether the European third way about AI has a greater potential to lead to beneficial AI and beneficial AGI than legislation coming out of the US and China given the economic incentives that they’ve got.

Jared Brown: I think in addition to the Brussels effect, we might have to amend it to say the Brussels and the Sacramento effect. Sacramento being the State Capitol of California because it’s one thing for the EU who have adopted the GDPR, and then California essentially replicated a lot of the GDPR, but not entirely, into what they call the CCPA, the California Consumer Privacy Act. If you combine the market size of the EU with California, you clearly have enough influence over the global economy. California for those who aren’t familiar, would be the seventh or sixth largest economy in the world if it were a standalone nation. So, the combined effect of Brussels and Sacramento developing tech policy or leading tech policy is not to be understated.

What remains to be seen though is how long lasting that precedent will be. And their ability to essentially be the first movers in the regulatory space will remain. With some of the criticism being developed around GDPR and the CCPA, it could be that leads to other governments trying to be more proactive to be the first out the door, the first movers in terms of major regulatory effects, which would minimize the Brussels effect or the Brussels and Sacramento effect.

Lucas Perry: So in terms of race conditions and sticking here on questions of global catastrophic risk and existential risks and why AI policy and governance and strategy considerations are important for risks associated with racing between say the United States and China on AI technology. Could you guys speak a little bit to the importance of appropriate AI policy and strategic positioning on mitigating race conditions and a why race would be bad for AGI risk and existential and global catastrophic risks in general?

Jared Brown: To simplify it, the basic logic here is that if two competing nations states or companies are engaged in a competitive environment to be the first to develop X, Y, Z, and they see tremendous incentive and advantage to being the first to develop such technology, then they’re more likely to cut corners when it comes to safety. And cut corners thinking about how to carefully apply these new developments to various different environments. There has been a lot of discussion about who will come to dominate the world and control AI technology. I’m not sure that either Nicolas or I really think that narrative is entirely accurate. Technology need not be a zero sum environment where the benefits are only accrued by one state or another. Or that the benefits accruing to one state necessarily reduce the benefits to another state. And there has been a growing recognition of this.

Nicolas earlier mentioned the high level expert group in the EU, an equivalent type body in the US, it’s called the National Security Commission on AI. And in their interim report they recognize that there is a strong need and one of their early recommendations is for what they call Track 1.5 or Track 2 diplomacy, which is essentially jargon for engagement with China and Russia on AI safety issues. Because if we deploy these technologies in reckless ways, that doesn’t benefit anyone. And we can still move cooperatively on AI safety and on the responsible use of AI without mitigating or entering into a zero sum environment where the benefits are only going to be accrued by one state or another.

Nicolas Moës: I definitely see the safety technologies as that would benefit everybody. If you’re thinking in two different types of inventions, the one that promotes safety indeed would be useful, but I believe that enhancing raw capabilities, you would actually race for that. Right? So, I totally agree with your decision narrative. I know people on both sides seeing this as a silly thing, you know, with media hype and of course industry benefiting a lot from this narrative.

There is a lot of this though that remains the rational thing to do, right? Whenever you start negotiating standards, you can say, “Well look at our systems. They are more advanced, so they should become the global standards for AI,” right? That actually is worrisome because the trajectory right now, since there is this narrative in place, is that over the medium term, you would expect the technologies maybe to diverge, and so both blocks, or if you want to charitably include the EU into this race, the three blocks would start diverging and therefore we’ll need each other less and less. The economic cost of an open conflict would actually decrease, but this is over the very long term.

That’s kind of the dangers of race dynamics as I see them. Again, it’s very heterogeneous, right? When we say the US against China, when you look at the more granular level of even units of governments are sometimes operating with a very different mindset. So, as for what in AI policy can actually be relevant to this for example, I do think they can, because at least on the Chinese side as far as I know, there is this awareness of the safety issue. Right? And there has been a pretty explicit article. It was like, “the US and China should work together to future proof AI.” So, it gives you the impression that some government officials or former government officials in China are interested in this dialogue about the safety of AI, which is what we would want. We don’t especially have to put the raw capabilities question on the table so long as there is common agreements about safety.

At the global level, there’s a lot of things happening to tackle this coordination problem. For example, the OECD AI Policy Observatory is an interesting setup because that’s an institution with which the US is still interacting. There have been fewer and fewer multilateral fora with which the US administration has been willing to interact constructively, let’s say. But for the OECD one yes, there’s been quite a lot of interactions. China is an observer to the OECD. So, I do believe that there is potential there to have a dialogue between the US and China, in particular about AI governance. And plenty of other fora exist at the global level to enable this Track 1.5 / Track 2 diplomacy that you mentioned Jared. For example, the Global Governance of AI Forum that the Future Society has organized, and Beneficial AGI that Future of Life Institute has organized.

Jared Brown: Yeah, and that’s sort of part and parcel with one of the most prominent examples of, some people call it scientific diplomacy, and that’s kind of a weird term, but the Pugwash conferences that occurred all throughout the Cold War where technical experts were meeting on the side to essentially establish a rapport between Russian and US scientists on issues of nuclear security and biological security as well.

So, there are plenty of examples where even if this race dynamic gets out of control, and even if we find ourselves 20 years from now in an extremely competitive, contentious relationship with near peer adversaries competing over the development of AI technology and other technologies, we shouldn’t, as civil society groups, give up hope and surrender to the inevitability that safety problems are likely to occur. We need to be looking to the past examples of what can be leveraged in order to appeal to essentially the common humanity of these nation states in their common interest in not wanting to see threats arise that would challenge either of their power dynamics.

Nicolas Moës: The context matters a lot, but sometimes it can be easier than one can think, right? So, I think when we organized the US China AI Tech Summit, because it was about business, about the cutting edge and because it was also about just getting together to discuss. And a bit before this US / China race dynamics was full on, there was not so many issues with getting our guests. Knowledge might be a bit more difficult with some officials not able to join events where officials from other countries are because of diplomatic reasons. And that was in June 2018 right? But back then there was the willingness and the possibility, since the US China tension was quite limited.

Jared Brown: Yeah, and I’ll just throw out a quick plug for other FLI podcasts. I recommend listeners check out the work that we did with Matthew Meselson. Max Tegmark had a great podcast on the development of the Biological Weapons Convention, which is a great example of how two competing nation states came to a common understanding about what was essentially a global catastrophic, or is, a global catastrophic and existential risk and develop the biological weapons convention.

Lucas Perry: So, tabling collaboration on safety, which can certainly be mutually beneficial in just focusing on capabilities research and how at least it seems basically just rational to race for that in a game theoretic sense.

That seems basically just rational to race for that in a game theoretic sense. I’m interested in exploring if you guys have any views or points to add here about mitigating the risks there, and how it may simply actually not be rational to race for that?

Nicolas Moës: So, there is the narrative currently that it’s rational to race on some aspect of raw capabilities, right? However, when you go beyond the typical game theoretical model, when you enable people to build bridges, you could actually find certain circumstances under which you have a so-called institutional entrepreneur building up in institutions that is legitimate so that everybody agrees upon that enforces the cooporation agreement.

In economics, the windfall clause is regarding the distribution of it. Here what I’m talking about in the game theoretical space, is how to avoid the negative impact, right? So, the windfall clause would operate in this very limited set of scenarios whereby the AGI leads to an abundance of wealth, and then a windfall clause deals with the distributional aspect and therefore reduce the incentive to a certain extent to produce AGI. However, to abide to the windfall clause, you still have to preserve the incentive to develop the AGI. Right? But you might actually tamp that down.

What I was talking about here, regarding the institutional entrepreneur, who can break this race by simply having a credible commitment from both sides and enforcing that commitment. So like the typical model of the tragedy of the commons, which here could be seen as you over-explored the time to superintelligence level, you can solve the tragedy of the commons, actually. So it’s not that rational anymore. Once you know that there is a solution, it’s not rational to go for the worst case scenario, right? You actually can design a mechanism that forces you to move towards the better outcome. It’s costly though, but it can be done if people are willing to put in the effort, and it’s not costly enough to justify not doing it.

Jared Brown: I would just add that the underlying assumptions about the rationality of racing towards raw capability development, largely depend on the level of risk you assign to unaligned AI or deploying narrow AI in ways that exacerbate global catastrophic and existential risk. Those game theories essentially can be changed and those dynamics can be changed if our community eventually starts to better sensitize players on both sides about the lose/lose situation, which we could find ourselves in through this type of racing. And so it’s not set in stone and the environment can be changed as information asymmetry is decreased between the two competing partners and there’s a greater appreciation for the lose/lose situations that can be developed.

Lucas Perry: Yeah. So I guess I just want to highlight the point then the superficial first analysis, it would seem that the rational game theoretic thing to do is to increase capability as much as possible, so that you have power and security over other actors. But that might not be true under further investigation.

Jared Brown: Right, and I mean, for those people who haven’t had to suffer through game theory classes, there’s a great popular culture example here that a lot of people have seen Stranger Things on Netflix. If you haven’t, maybe skip ahead 20 seconds until I’m done saying this. But there is an example of the US and Russia competing to understand the upside down world, and then releasing untold havoc onto their societies, because of this upside down discovery. For those of you who have watched, it’s actually a fairly realistic example of where this kind of competing technological development leads somewhere that’s a lose/lose for both parties, and if they had better cooperation and better information sharing about the potential risks, because they were each discovering it themselves without communicating those risks, neither would have opened up the portals to the upside down world.

Nicolas Moës: The same dynamics, the same “oh it’s rational to race” dynamic applied to nuclear policy and nuclear arms race has led to, actually, some treaties, far from perfection. Right? But some treaties. So this is the thing where, because the model, the tragedy of the commons, it’s easy to communicate. It’s a nice thing was doom and fatality that is embedded with it. This resonates really well with people, especially in the media, it’s a very simple thing to say. But this simply might not be true. Right? As I mentioned. So there is this institutional entrepreneurship aspect which requires resources, right? So that is very costly to do. But civil society is doing that, and I think the Future of Life Institute has agency to do that. The Future Society is definitely doing that. We are actually agents of breaking away from these game theoretical situations that would be otherwise unlikely.

We fixate a lot on the model, but in reality, we have seen the nuclear policy, the worst case scenario being averted sometimes by mistake. Right? The human in the loop not following the policy or something like that. Right. So it’s interesting as well. It shows how unpredictable all this is. It really shows that for AI, it’s the same. You could have the militaries on both sides, literally from one day to the next, start a discussion about AI safety, and how to ensure that they keep control. There’s a lot of goodwill on both sides and so maybe we could say like, “Oh, the economist” — and I’m an economist by just training so I can be a bit harsh on myself — they’re like, the economist would say, “But this is not rational.” Well, in the end, it is more rational, right? So long as you win, you know, remain in a healthy life and feel like you have done the right thing, this is the rational thing to do. Maybe if Netflix is not your thing, “Inadequate Equilibria” by Eliezer Yudkowsky explores these kinds of conundrums as well. Why do you have sub-optimal situations in life in general? It’s a very, general model, but I found it very interesting to think about these issues, and in the end it boils down to these kinds of situations.

Lucas Perry: Yeah, right. Like for example, the United States and Russia having like 7,000 nuclear warheads each, and being on hair trigger alert with one another, is a kind of in-optimal equilibrium that we’ve nudged ourself into. I mean it maybe just completely unrealistic, but a more optimum place to be would be no nuclear weapons, but have used all of that technology and information for nuclear power. Well, we would all just be better off.

Nicolas Moës: Yeah. What you describe seems to be a better situation. However, the rational thing to do at some point would have been before the Soviet Union developed, incapacitate Soviet Union to develop. Now, the mutually assured destruction policy is holding up a lot of that. But I do believe that the diplomacy, the discussions, the communication, even merely the fact of communicating like, “Look, if you do that and we will do that,” is a form of progress towards: basically you should not use it.

Jared Brown: Game theory is nice to boil things down into a nice little boxes, clearly. But the dynamics of the nuclear situation with the USSR and the US add countless number of boxes that you get end up in and yes, each of us having way too large nuclear arsenals is a sub-optimal outcome, but it’s not the worst possible outcome, that would have been total nuclear annihilation. So it’s important not just to look at it criticisms of the current situation, but also see the benefits of this current situation and why this box is better than some other boxes that we ended up in. And that way, we can leverage the past that we have taken to get to where we’re at, find the paths that were actually positive, and reapply those lessons learned to the trajectory of emerging technology once again. We can’t throw out everything that has happened on nuclear policy and assume that there’s nothing to be gained from it, just because the situation that we’ve ended up in is suboptimal.

Nicolas Moës: Something that I have experienced while interacting with policymakers and diplomats. You actually have an agency over what is going on. This is important also to note, is that it’s not like a small thing, and the world is passing by. No. Even in policy, which seems to be maybe a bit more arcane, in policy, you can pull the right levers to make somebody feel less like they have to obey this race narrative.

Jared Brown: Just recently in the last National Defense Authorization Act, there was a provision talking about the importance of military to military dialogues being established, potentially even with adversarial states like North Korea and Iran, for that exact reason. That better communication between militaries can lead to a reduction of miscalculation, and therefore adverse escalation of conflicts. We saw this just recently between the US and Iran. There was not direct communication perhaps between the US and Iran, but there was indirect communication, some of that over Twitter, about the intentions and the actions that different states might take. Iran and the US, in reaction to other events, and that may have helped deescalate the situation to where we find now. It’s far from perfect, but this is the type of thing that civil society can help encourage as we are dealing with new types of technology that can be as dangerous as nuclear weapons.

Lucas Perry: I just want to touch on what is actually going on now and actually being considered before we wrap things up. You talked about this a little bit before, Jared, you mentioned that currently in terms of AI policy, we are moving from principles and recommendations to the implementation of these into hard law. So building off of this, I’m just trying to get a better sense of where AI policy is, currently. What are the kinds of things that have been implemented, and what hasn’t, and what needs to be done?

Jared Brown: So there are some key decisions that have to be made in the near term on AI policy that I see replicating in many different government environments. One of them is about liability. I think it’s very important for people to understand the influence that establishing liability has for safety considerations. By liability, I mean who is legally responsible if something goes wrong? The basic idea is if an autonomous vehicle crashes into a school bus, who’s going to be held responsible and under what conditions? Or if an algorithm is biased and systematically violates the civil rights of one minority group, who is legally responsible for that? Is it the creator of the algorithm, the developer of the algorithm? Is it the deployer of that algorithm? Is there no liability for anyone at all in that system? And governments writ large are struggling with trying to assign liability, and that’s a key area of governance and AI policy that’s occurring now.

For the most part, it would be wise for governments to not provide blanket liability to AI, simply as a matter of trying to encourage and foster the adoption of those technologies; such that we encourage people to essentially use those technologies in unquestioning ways and sincerely surrender the decision making from the human to that AI algorithm. There are other key issue areas. There is the question of educating the populace. The example here I give is, you hear the term financial literacy all the time about how educated is your populace about how to deal with money matters.

There’s a lot about technical literacy, technology literacy being developed. The Finnish government has a whole course on AI that they’re making available to the entire EU. How we educate our population and prepare our population from a workforce training perspective matters a lot. If that training incorporates considerations for common AI safety problems, if we’re training people about how adversarial examples can affect machine learning and so on and so forth, we’re doing a better job of sensitizing the population to potential longterm risks. That’s another example of where AI policy is being developed. And I’ll throw out one more, which is a common example that people will understand. You have a driver’s license from your state. The state has traditionally been responsible for deciding the human qualities that are necessary, in order for you to operate a vehicle. And the same goes for state licensing boards have been responsible for certifying and allowing people to practice the law or practice medicine.

Doctors and lawyers, there are national organizations, but licensing is typically done at the state. Now if we talk about AI starting to essentially replace human functions, governments have to look again at this division about who regulates what and when. There’s sort of an opportunity in all democracies to reevaluate the distribution of responsibility between units of government, about who has the responsibility to regulate and monitor and govern AI, when it is doing something that a human being used to do. And there are different pros and cons for different models. But suffice it to say that that’s a common theme in AI policy right now, is how to deal with who has the responsibility to govern AI, if it’s essentially replacing what used to be formally, exclusively a human function.

Nicolas Moës: Yeah, so in terms of where we stand, currently, actually let’s bring some context maybe to this question as well, right? The way it has evolved over the past few years is that you had really ethical principles in 2017 and 2018. Let’s look at the global level first. Like at the global level, you had for example, the Montréal Declaration, which was intended to be global, but for mostly fundamental rights-oriented countries, so that that excludes some of the key players. We have already talked about dozens and dozens of principles for AI in values context or in general, right. That was 2018, and then once we have seen is more the first multi-lateral guidelines so we have the OECD principles, GPAI which is this global panel on AI, was also a big thing between Canada and France, which was initially intended to become kind of the international body for AI governance, but that deflated a bit over time, and so you had also the establishment of all this fora for discussion, that I have already mentioned. Political AI summits and the Global Forum on AI for Humanity, which is, again, a Franco-Canadian initiative like the AI for Good. The Global Governance of AI Forum in the Middle East. There was this ethically aligned design initiative at the IEEE, which is a global standards center, which has garnered a lot of attention among policymakers and other stakeholders. But the move towards harder law is coming, and since it’s towards harder law, at the global level there is not much that can happen. Nation states remain sovereign in the eye of international law.

So unless you write up an international treaty, it would be at the government level that you have to move towards hard law. So at the global level, the next step that we can see is these audits and certification principles. It’s not hard law, but you use labels to independently certify whether an algorithm is good. Some of them are tailored for specific countries. So I think Denmark has its own certification mechanism for AI algorithms. The US is seeing the appearance of values initiatives, notably by the big consulting companies, which are all of the auditors. So this is something that is interesting to see how we shift from soft law, towards this industry-wide regulation for these algorithms. At the EU level, where you have some hard legislative power, you had also a high level group on liability. Which is very important, because they basically argued that we’re going to have to update product liability rules in certain ways for AI and for internet of things products.

This is interesting to look at as well, because when you look at product liability rules, this is hard law, right? So what they have recommended is directly translatable into this legislation. And so you move on at this stage since the end of 2019, you have this hard law coming up and this commission white paper which really kickstarts the debates about what will the regulation for AI be? And whether it will be a regulation. So it could be something else like a directive. The high level expert group has come up with a self assessment list for companies to see whether they are obeying the ethical principles decided upon in Europe. So these are kind of soft self regulation things, which might eventually affect court rulings or something like that. But they do not represent the law, and now the big players are moving in, either at the global level with these more and more powerful labeling initiatives, or certification initiatives, and at the EU level with this hard law.

And the reason why the EU level has moved on towards hard law so quickly, is because during the very short campaign of the commission president, AI was a political issue. The techlash was strong, and of course a lot of industry was complaining that there was nothing happening in AI in the EU. So they wanted strong action and that kind of stuff. The circumstances that led the EU to be in pole position for developing hard law. Elsewhere in the world, you actually have more fragmented initiatives at this stage, except the OECD AI policy observatory, which might be influential in itself, right? It’s important to note the AI principles that the OECD has published. Even though they are not binding, they would actually influence the whole debate. Right? Because at the international level, for example, when the OECD had privacy principles, this became the reference point for many legislators. So some countries who don’t want to spend years even debating how to legislate AI might just be like, “okay, here is the OECD principles, how do we implement that in our current body of law?” And that’s it.

Jared Brown: And I’ll just add one more quick dynamic that’s coming up with AI policy, which is essentially the tolerance of that government for the risk associated with emerging technology. A classic example here is, the US actually has a much higher level of flood risk tolerance than other countries. So we engineer largely, throughout the US, our dams and our flood walls and our other flood protection systems to a 1-in-100 year standard. Meaning the flood protection system is supposed to protect you from a severe storm that would have a 1% chance of occurring in a given year. Other countries have vastly different decisions there. Different countries make different policy decisions about the tolerance that they’re going to have for certain things to happen. And so as we think about emerging technology risk, it’s important to think about the way that your government is shaping policies and the underlying tolerance that they have for something going wrong.

It could be as simple as how likely it is that you will die because of an autonomous vehicle crash. And the EU, traditionally, has had what they call a precautionary principal approach, which is in the face of uncertain risks, they’re more likely to regulate and restrict development until those risks are better understood, than the US, which typically has adopted the precautionary principle less often.

Nicolas Moës: There is a lot of uncertainty. A lot of uncertainty about policy, but also a lot of uncertainty about the impact that all these technologies are having. The dam standard, you can quantify quite easily the force of nature, but here we are dealing with social forces that are a bit different. I still remember quite a lot of people being very negative about Facebook’s chances of success, because people would not be willing to put pictures of themself online. I guess 10 years later, these people have been proven wrong. The same thing could happen with AI, right? So people are currently, at least in the EU, afraid of some aspects of AI. So let’s say an autonomous vehicle. Surrendering decision-making about our life and death to an autonomous vehicle, that’s something that’s maybe as technology improves, people would be more and more willing to do that. So yeah, it’s very difficult to predict, and even more to quantify I think.

Lucas Perry: All right. So thank you both so much. Do either of you guys have any concluding thoughts about AI policy or anything else you’d just like to wrap up on?

Jared Brown: I just hope the audience really appreciates the importance of engaging in the policy discussion. Trying to map out a beneficial forward for AI policy, because if you’re concerned like we are about the long term trajectory of this emerging technology and other emerging technologies, it’s never too early to start engaging in the policy discussion on how to map a beneficial path forward.

Nicolas Moës: Yeah, and one last thought, we were talking with Jared a couple of days ago about the number of people doing that. So thank you by the way, Jared for inviting me, and Lucas, for inviting me on the podcast. But that led us to wonder how many people are doing what we are doing, with the motivation that we have regarding these longer term concerns. That makes me think, yeah, there’s very few resources like labor resources, financial resources, dedicated to this issue. And I’d be really interested if there is, in the audience, anybody interested in that issue, definitely, they should get in touch. There are too few people right now with similar motivations, and caring about the same thing in AI policy to actually miss the opportunity of meeting each other and coordinating better.

Jared Brown: Agreed.

Lucas Perry: All right. Wonderful. So yeah, thank you guys both so much for coming on.

End of recorded material

FLI Podcast: Identity, Information & the Nature of Reality with Anthony Aguirre

Our perceptions of reality are based on the physics of interactions ranging from millimeters to miles in scale. But when it comes to the very small and the very massive, our intuitions often fail us. Given the extent to which modern physics challenges our understanding of the world around us, how wrong could we be about the fundamental nature of reality? And given our failure to anticipate the counterintuitive nature of the universe, how accurate are our intuitions about metaphysical and personal identity? Just how seriously should we take our everyday experiences of the world? Anthony Aguirre, cosmologist and FLI co-founder, returns for a second episode to offer his perspective on these complex questions. This conversation explores the view that reality fundamentally consists of information and examines its implications for our understandings of existence and identity.

Topics discussed in this episode include:

  • Views on the nature of reality
  • Quantum mechanics and the implications of quantum uncertainty
  • Identity, information and description
  • Continuum of objectivity/subjectivity

Timestamps: 

3:35 – General history of views on fundamental reality

9:45 – Quantum uncertainty and observation as interaction

24:43 – The universe as constituted of information

29:26 – What is information and what does the view of reality as information have to say about objects and identity

37:14 – Identity as on a continuum of objectivity and subjectivity

46:09 – What makes something more or less objective?

58:25 – Emergence in physical reality and identity

1:15:35 – Questions about the philosophy of identity in the 21st century

1:27:13 – Differing views on identity changing human desires

1:33:28 – How the reality as information perspective informs questions of identity

1:39:25 – Concluding thoughts

 

This podcast is possible because of the support of listeners like you. If you found this conversation to be meaningful or valuable consider supporting it directly by donating at futureoflife.org/donate. Contributions like yours make these conversations possible.

All of our podcasts are also now on Spotify and iHeartRadio! Or find us on SoundCloudiTunesGoogle Play and Stitcher.

You can listen to the podcast above or read the transcript below. 

Lucas Perry: Welcome to the Future of Life Institute Podcast. I’m Lucas Perry. Recently we had a conversation between Max Tegmark and Yuval Noah Harari where in consideration of 21st century technological issues Yuval recommended “Get to know yourself better. It’s maybe the most important thing in life. We haven’t really progressed much in the last thousands of years and the reason is that yes, we keep getting this advice but we don’t really want to do it…. I mean, especially as technology will give us all, at least some of us, more and more power, the temptations of naive utopias are going to be more and more irresistible and I think the really most powerful check on these naive utopias is really getting to know yourself better.

Drawing inspiration from this, our following podcast was with Andres Gomez Emillson and David Pearce on different views of identity, like open, closed, and empty individualism, and their importance in the world. Our conversation today with Anthony Aguirre follows up on and further explores the importance of questions of self and identity in the 21st century.

This episode focuses on exploring this question from a physics perspective where we discuss the view of reality as fundamentally consisting of information. This helps us to ground what actually exists, how we come to know that, and how this challenges our commonly held intuitions about there existing a concrete reality out there populated by conventionally accepted objects and things, like cups and people, that we often take for granted without challenging or looking into much. This conversation subverted many of my assumptions about science, physics, and the nature of reality, and if that sounds interesting to you, I think you’ll find it valuable as well. 

For those of you not familiar with Anthony Athony, he is a physicist that studies the formation, nature, and evolution of the universe, focusing primarily on the model of eternal inflation—the idea that inflation goes on forever in some regions of universe—and what it may mean for the ultimate beginning of the universe and time. He is the co-founder and associate scientific director of the Foundational Questions Institute and is also a co-founder of the Future of Life Institute. He also co-founded Metaculus, an effort to optimally aggregate predictions about scientific discoveries, technological breakthroughs, and other interesting issues.

The Future of Life Institute is a non-profit and this podcast is funded and supported by listeners like you. So if you find what we do on this podcast to be important and beneficial, please consider supporting the podcast by donating at futureoflife.org/donate. These contributions make it possible for us to bring you conversations like these and to develop the podcast further. You can also follow us on your preferred listening platform by searching for us directly or following the links on the page for this podcast found in the description.

And with that, let’s get into our conversation with Anthony Aguirre.

So the last time we had you on, we had a conversation on information. Could you take us through the history of how people have viewed fundamental reality and fundamental ontology over time from a kind of idealism to then materialism to then this new shift that’s informed by quantum mechanics about seeing things as being constituted of information.

Anthony Aguirre: So, without being a historian of science, I can only give you the general impression that I have. And of course through history, many different people have viewed things very different ways. So, I would say in the history of humanity, there have obviously been many, many ways to think about the ultimate nature of reality, if you will, starting with a sense that the fundamental nature of external reality is one that’s based on different substances and tendencies and some level of regularity in those things, but without a sense that there are firm or certainly not mathematical regularities and things. And that there are causes of events, but without a sense that those causes can be described in some mathematical way.

So that changed obviously in terms of Western science with the advent of mechanics by Galileo and Newton and others showing that there are not just regularities in the sense that the same result will happen from the same causes over and over again, that was appreciated for a long time, but that those could be accessed not just experimentally but modeled mathematically and that there could be a relatively small set of mathematical laws that could then be used to explain a very wide range of different physical phenomena. I think that sense was not there before, it was clear that things caused other things and events caused other events, but I suspect the thinking was that it was more in a one off way, like, “That’s a complicated thing. It’s caused by a whole bunch of other complicated things. In principle, those things are connected.” But there wasn’t a sense that you could get in there and understand what that connection was analytically or intellectually and certainly not in a way that had some dramatic economy in the sense that we now appreciate from Galileo and Newton and subsequent physics.

Once we had that change to mathematical laws, then there was a question of, what are those mathematical laws describing? And the answer there was essentially that those mathematical laws are describing particles and forces between particles. And at some level, a couple of other auxiliary things like space and time are sort of there in the backdrop, but essentially the nature of reality is a bunch of little bits of stuff that are moving around under mathematically specified forces.

That is a sort of complete-ish description. I mean certainly Newton would have and have not said that that’s a complete description in the sense that, in Newton’s view, there were particles and those particles made up things and the forces told them exactly what to do, but at the same time there were lots of other things in Newton’s conception of reality like God and presumably other entities. So it’s not exactly clear how materialist Newton or Galileo for example were, but as time went on that became a more entrenched idea among hardcore theoretical physicists at least, or physicists, that there was ultimately this truest, most fundamental, most base description of reality that was lots of particles moving around under mathematical forces.

Now, that I think is a conception that is very much still with us in many s