From Max Tegmark’s Life 3.0 to Stuart Russell’s Human Compatible and Nick Bostrom’s Superintelligence, much has been written and said about the long-term risks of powerful AI systems. When considering concrete actions one can take to help mitigate these risks, governance and policy related solutions become an attractive area of consideration. But just what can anyone do in the present day policy sphere to help ensure that powerful AI systems remain beneficial and aligned with human values? Do today’s AI policies matter at all for AGI risk? Jared Brown and Nicolas Moës join us on today’s podcast to explore these questions and the importance of AGI-risk sensitive persons’ involvement in present day AI policy discourse.
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
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?
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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.