How Can AI Learn to Be Safe?

As artificial intelligence improves, machines will soon be equipped with intellectual and practical capabilities that surpass the smartest humans. But not only will machines be more capable than people, they will also be able to make themselves better. That is, these machines will understand their own design and how to improve it – or they could create entirely new machines that are even more capable.

The human creators of AIs must be able to trust these machines to remain safe and beneficial even as they self-improve and adapt to the real world.

Recursive Self-Improvement

This idea of an autonomous agent making increasingly better modifications to its own code is called recursive self-improvement. Through recursive self-improvement, a machine can adapt to new circumstances and learn how to deal with new situations.

To a certain extent, the human brain does this as well. As a person develops and repeats new habits, connections in their brains can change. The connections grow stronger and more effective over time, making the new, desired action easier to perform (e.g. changing one’s diet or learning a new language). In machines though, this ability to self-improve is much more drastic.

An AI agent can process information much faster than a human, and if it does not properly understand how its actions impact people, then its self-modifications could quickly fall out of line with human values.

For Bas Steunebrink, a researcher at the Swiss AI lab IDSIA, solving this problem is a crucial step toward achieving safe and beneficial AI.

Building AI in a Complex World

Because the world is so complex, many researchers begin AI projects by developing AI in carefully controlled environments. Then they create mathematical proofs that can assure them that the AI will achieve success in this specified space.

But Steunebrink worries that this approach puts too much responsibility on the designers and too much faith in the proof, especially when dealing with machines that can learn through recursive self-improvement. He explains, “We cannot accurately describe the environment in all its complexity; we cannot foresee what environments the agent will find itself in in the future; and an agent will not have enough resources (energy, time, inputs) to do the optimal thing.”

If the machine encounters an unforeseen circumstance, then that proof the designer relied on in the controlled environment may not apply. Says Steunebrink, “We have no assurance about the safe behavior of the [AI].”

Experience-based Artificial Intelligence

Instead, Steunebrink uses an approach called EXPAI (experience-based artificial intelligence). EXPAI are “self-improving systems that make tentative, additive, reversible, very fine-grained modifications, without prior self-reasoning; instead, self-modifications are tested over time against experiential evidences and slowly phased in when vindicated, or dismissed when falsified.”

Instead of trusting only a mathematical proof, researchers can ensure that the AI develops safe and benevolent behaviors by teaching and testing the machine in complex, unforeseen environments that challenge its function and goals.

With EXPAI, AI machines will learn from interactive experience, and therefore monitoring their growth period is crucial. As Steunebrink posits, the focus shifts from asking, “What is the behavior of an agent that is very intelligent and capable of self-modification, and how do we control it?” to asking, “How do we grow an agent from baby beginnings such that it gains both robust understanding and proper values?”

Consider how children grow and learn to navigate the world independently. If provided with a stable and healthy childhood, children learn to adopt values and understand their relation to the external world through trial and error, and by examples. Childhood is a time of growth and learning, of making mistakes, of building on success – all to help prepare the child to grow into a competent adult who can navigate unforeseen circumstances.

Steunebrink believes that researchers can ensure safe AI through a similar, gradual process of experience-based learning. In an architectural blueprint developed by Steunebrink and his colleagues, the AI is constructed “starting from only a small amount of designer-specific code – a seed.” Like a child, the beginnings of the machine will be less competent and less intelligent, but it will self-improve over time, as it learns from teachers and real-world experience.

As Steunebrink’s approach focuses on the growth period of an autonomous agent, the teachers, not the programmers, are most responsible for creating a robust and benevolent AI. Meanwhile, the developmental stage gives researchers time to observe and correct an AI’s behavior in a controlled setting where the stakes are still low.

The Future of EXPAI

Steunebrink and his colleagues are currently creating what he describes as a “pedagogy to determine what kind of things to teach to agents and in what order, how to test what the agents understand from being taught, and, depending on the results of such tests, decide whether we can proceed to the next steps of teaching or whether we should reteach the agent or go back to the drawing board.”

A major issue Steunebrink faces is that his method of experience-based learning diverges from the most popular methods for improving AI. Instead of doing the intellectual work of crafting a proof-backed optimal learning algorithm on a computer, EXPAI requires extensive in-person work with the machine to teach it like a child.

Creating safe artificial intelligence might prove to be more a process of teaching and growth rather than a function of creating the perfect mathematical proof. While such a shift in responsibility may be more time-consuming, it could also help establish a far more comprehensive understanding of an AI before it is released into the real world.

Steunebrink explains, “A lot of work remains to move beyond the agent implementation level, towards developing the teaching and testing methodologies that enable us to grow an agent’s understanding of ethical values, and to ensure that the agent is compelled to protect and adhere to them.”

The process is daunting, he admits, “but it is not as daunting as the consequences of getting AI safety wrong.”

If you would like to learn more about Bas Steunebrink’s research, you can read about his project here, or visit http://people.idsia.ch/~steunebrink/. He is also the co-founder of NNAISENSE, which you can learn about at https://nnaisense.com/.

This article is part of a Future of Life series on the AI safety research grants, which were funded by generous donations from Elon Musk and the Open Philanthropy Project.

Training Artificial Intelligence to Compromise

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Imagine you’re sitting in a self-driving car that’s about to make a left turn into on-coming traffic. One small system in the car will be responsible for making the vehicle turn, one system might speed it up or hit the brakes, other systems will have sensors that detect obstacles, and yet another system may be in communication with other vehicles on the road. Each system has its own goals — starting or stopping, turning or traveling straight, recognizing potential problems, etc. — but they also have to all work together toward one common goal: turning into traffic without causing an accident.

Harvard professor and FLI researcher, David Parkes, is trying to solve just this type of problem. Parkes told FLI, “The particular question I’m asking is: If we have a system of AIs, how can we construct rewards for individual AIs, such that the combined system is well behaved?”

Essentially, an AI within a system of AIs — like that in the car example above — needs to learn how to meet its own objective, as well as how to compromise so that it’s actions will help satisfy the group objective. On top of that, the system of AIs needs to consider the preferences of society. The safety of the passenger in the car or a pedestrian in the crosswalk is a higher priority than turning left.

Training a well-behaved AI

Because environments like a busy street are so complicated, an engineer can’t just program an AI to act in some way to always achieve its objectives. AIs need to learn proper behavior based on a rewards system. “Each AI has a reward for its action and the action of the other AI,” Parkes explained. With the world constantly changing, the rewards have to evolve, and the AIs need to keep up not only with how their own goals change, but also with the evolving objectives of the system as a whole.

The idea of a rewards-based learning system is something most people can likely relate to. Who doesn’t remember the excitement of a gold star or a smiley face on a test? And any dog owner has experienced how much more likely their pet is to perform a trick when it realizes it will get a treat. A reward for an AI is similar.

A technique often used in designing artificial intelligence is reinforcement learning. With reinforcement learning, when the AI takes some action, it receives either positive or negative feedback. And it then tries to optimize its actions to receive more positive rewards. However, the reward can’t just be programmed into the AI. The AI has to interact with its environment to learn which actions will be considered good, bad or neutral. Again, the idea is similar to a dog learning that tricks can earn it treats or praise, but misbehaving could result in punishment.

More than this, Parkes wants to understand how to distribute rewards to subcomponents – the individual AIs – in order to achieve good system-wide behavior. How often should there be positive (or negative) reinforcement, and in reaction to which types of actions?

For example, if you were to play a video game without any points or lives or levels or other indicators of success or failure, you might run around the world killing or fighting aliens and monsters, and you might eventually beat the game, but you wouldn’t know which specific actions led you to win. Instead, games are designed to provide regular feedback and reinforcement so that you know when you make progress and what steps you need to take next. To train an AI, Parkes has to determine which smaller actions will merit feedback so that the AI can move toward a larger, overarching goal.

Rather than programming a reward specifically into the AI, Parkes shapes the way rewards flow from the environment to the AI in order to promote desirable behaviors as the AI interacts with the world around it.

But this is all for just one AI. How do these techniques apply to two or more AIs?

Training a system of AIs

Much of Parkes’ work involves game theory. Game theory helps researchers understand what types of rewards will elicit collaboration among otherwise self-interested players, or in this case, rational AIs. Once an AI figures out how to maximize its own reward, what will entice it to act in accordance with another AI? To answer this question, Parkes turns to an economic theory called mechanism design.

Mechanism design theory is a Nobel-prize winning theory that allows researchers to determine how a system with multiple parts can achieve an overarching goal. It is a kind of “inverse game theory.” How can rules of interaction – ways to distribute rewards, for instance – be designed so individual AIs will act in favor of system-wide and societal preferences? Among other things, mechanism design theory has been applied to problems in auctions, e-commerce, regulations, environmental policy, and now, artificial intelligence.

The difference between Parkes’ work with AIs and mechanism design theory is that the latter requires some sort of mechanism or manager overseeing the entire system. In the case of an automated car or a drone, the AIs within have to work together to achieve group goals, without a mechanism making final decisions. As the environment changes, the external rewards will change. And as the AIs within the system realize they want to make some sort of change to maximize their rewards, they’ll have to communicate with each other, shifting the goals for the entire autonomous system.

Parkes summarized his work for FLI, saying, “The work that I’m doing as part of the FLI grant program is all about aligning incentives so that when autonomous AIs decide how to act, they act in a way that’s not only good for the AI system, but also good for society more broadly.”

Parkes is also involved with the One Hundred Year Study on Artificial Intelligence, and he explained his “research with FLI has informed a broader perspective on thinking about the role that AI can play in an urban context in the near future.” As he considers the future, he asks, “What can we see, for example, from the early trajectory of research and development on autonomous vehicles and robots in the home, about where the hard problems will be in regard to the engineering of value-aligned systems?”

This article is part of a Future of Life series on the AI safety research grants, which were funded by generous donations from Elon Musk and the Open Philanthropy Project.

The Evolution of AI: Can Morality be Programmed?

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The following article was originally posted on Futurism.com.

Recent advances in artificial intelligence have made it clear that our computers need to have a moral code. Disagree? Consider this: A car is driving down the road when a child on a bicycle suddenly swerves in front of it. Does the car swerve into an oncoming lane, hitting another car that is already there? Does the car swerve off the road and hit a tree? Does it continue forward and hit the child?

Each solution comes with a problem: It could result in death.

It’s an unfortunate scenario, but humans face such scenarios every day, and if an autonomous car is the one in control, it needs to be able to make this choice. And that means that we need to figure out how to program morality into our computers.

Vincent Conitzer, a Professor of Computer Science at Duke University, recently received a grant from the Future of Life Institute in order to try and figure out just how we can make an advanced AI that is able to make moral judgments…and act on them.

MAKING MORALITY

At first glance, the goal seems simple enough—make an AI that behaves in a way that is ethically responsible; however, it’s far more complicated than it initially seems, as there are an amazing amount of factors that come into play. As Conitzer’s project outlines, “moral judgments are affected by rights (such as privacy), roles (such as in families), past actions (such as promises), motives and intentions, and other morally relevant features. These diverse factors have not yet been built into AI systems.”

That’s what we’re trying to do now.

In a recent interview with Futurism, Conitzer clarified that, while the public may be concerned about ensuring that rogue AI don’t decide to wipe-out humanity, such a thing really isn’t a viable threat at the present time (and it won’t be for a long, long time). As a result, his team isn’t concerned with preventing a global-robotic-apocalypse by making selfless AI that adore humanity. Rather, on a much more basic level, they are focused on ensuring that our artificial intelligence systems are able to make the hard, moral choices that humans make on a daily basis.

So, how do you make an AI that is able to make a difficult moral decision?

Conitzer explains that, to reach their goal, the team is following a two path process: Having people make ethical choices in order to find patterns and then figuring out how that can be translated into an artificial intelligence. He clarifies, “what we’re working on right now is actually having people make ethical decisions, or state what decision they would make in a given situation, and then we use machine learning to try to identify what the general pattern is and determine the extent that we could reproduce those kind of decisions.”

In short, the team is trying to find the patterns in our moral choices and translate this pattern into AI systems. Conitzer notes that, on a basic level, it’s all about making predictions regarding what a human would do in a given situation, “if we can become very good at predicting what kind of decisions people make in these kind of ethical circumstances, well then, we could make those decisions ourselves in the form of the computer program.”

However, one major problem with this is, of course, that morality is not objective — it’s neither timeless nor universal.

Conitzer articulates the problem by looking to previous decades, “if we did the same ethical tests a hundred years ago, the decisions that we would get from people would be much more racist, sexist, and all kinds of other things that we wouldn’t see as ‘good’ now. Similarly, right now, maybe our moral development hasn’t come to its apex, and a hundred years from now people might feel that some of the things we do right now, like how we treat animals, is completely immoral. So there’s kind of a risk of bias and with getting stuck at whatever our current level of moral development is.”

And of course, there is the aforementioned problem regarding how complex morality is. “Pure altruism, that’s very easy to address in game theory, but maybe you feel like you owe me something based on previous actions. That’s missing from the game theory literature, and so that’s something that we’re also thinking about a lot—how can you make this, what game theory calls ‘Solutions Concept’—sensible? How can you compute these things?”

To solve these problems, and to help figure out exactly how morality functions and can (hopefully) be programmed into an AI, the team is combining the methods from computer science, philosophy, and psychology “That’s, in a nutshell, what our project is about,” Conitzer asserts.

But what about those sentient AI? When will we need to start worrying about them and discussing how they should be regulated?

THE HUMAN-LIKE AI

According to Conitzer, human-like artificial intelligence won’t be around for some time yet (so yay! No Terminator-styled apocalypse…at least for the next few years).

“Recently, there have been a number of steps towards such a system, and I think there have been a lot of surprising advances….but I think having something like a ‘true AI,’ one that’s really as flexible, able to abstract, and do all these things that humans do so easily, I think we’re still quite far away from that,” Conitzer asserts.

True, we can program systems to do a lot of things that humans do well, but there are some things that are exceedingly complex and hard to translate into a pattern that computers can recognize and learn from (which is ultimately the basis of all AI).

“What came out of early AI research, the first couple decades of AI research, was the fact that certain things that we had thought of as being real benchmarks for intelligence, like being able to play chess well, were actually quite accessible to computers. It was not easy to write and create a chess-playing program, but it was doable.”

Indeed, today, we have computers that are able to beat the best players in the world in a host of games—Chess and Alpha Go, for example.

But Conitzer clarifies that, as it turns out, playing games isn’t exactly a good measure of human-like intelligence. Or at least, there is a lot more to the human mind. “Meanwhile, we learned that other problems that were very simple for people were actually quite hard for computers, or to program computers to do. For example, recognizing your grandmother in a crowd. You could do that quite easily, but it’s actually very difficult to program a computer to recognize things that well.”

Since the early days of AI research, we have made computers that are able to recognize and identify specific images. However, to sum the main point, it is remarkably difficult to program a system that is able to do all of the things that humans can do, which is why it will be some time before we have a ‘true AI.’

Yet, Conitzer asserts that now is the time to start considering what the rules we will use to govern such intelligences. “It may be quite a bit further out, but to computer scientists, that means maybe just on the order of decades, and it definitely makes sense to try to think about these things a little bit ahead.” And he notes that, even though we don’t have any human-like robots just yet, our intelligence systems are already making moral choices and could, potentially, save or end lives.

“Very often, many of these decisions that they make do impact people and we may need to make decisions that we will typically be considered to be a morally loaded decision. And a standard example is a self-driving car that has to decide to either go straight and crash into the car ahead of it or veer off and maybe hurt some pedestrian. How do you make those trade-offs? And that I think is something we can really make some progress on. This doesn’t require superintelligent AI, this can just be programs that make these kind of trade-offs in various ways.”

But of course, knowing what decision to make will first require knowing exactly how our morality operates (or at least having a fairly good idea). From there, we can begin to program it, and that’s what Conitzer and his team are hoping to do.

So welcome to the dawn of moral robots.

This interview has been edited for brevity and clarity. 

This article is part of a Future of Life series on the AI safety research grants, which were funded by generous donations from Elon Musk and the Open Philanthropy Project.

Grants Timeline

Grants F.A.Q.

Grants RFP Overview

Grants Program Press Release

New International Grants Program Jump-Starts Research to Ensure AI Remains Beneficial

Elon-Musk-backed program signals growing interest in new branch of artificial intelligence research

July 1, 2015
Amid rapid industry investment in developing smarter artificial intelligence, a new branch of research has begun to take off aimed at ensuring that society can reap the benefits of AI while avoiding potential pitfalls.

The Boston-based Future of Life Institute (FLI) announced the selection of 37 research teams around the world to which it plans to award about $7 million from Elon Musk and the Open Philanthropy Project as part of a first-of-its-kind grant program dedicated to “keeping AI robust and beneficial”. The program launches as an increasing number of high-profile figures including Bill Gates, Elon Musk and Stephen Hawking voice concerns about the possibility of powerful AI systems having unintended, or even potentially disastrous, consequences. The winning teams, chosen from nearly 300 applicants worldwide, will research a host of questions in computer science, law, policy, economics, and other fields relevant to coming advances in AI.

The 37 projects being funded include:

  • Three projects developing techniques for AI systems to learn what humans prefer from observing our behavior, including projects at UC Berkeley and Oxford University
  • A project by Benja Fallenstein at the Machine Intelligence Research Institute on how to keep the interests of superintelligent systems aligned with human values
  • A project led by Manuela Veloso from Carnegie Mellon University on making AI systems explain their decisions to humans
  • A study by Michael Webb of Stanford University on how to keep the economic impacts of AI beneficial
  • A project headed by Heather Roff studying how to keep AI-driven weapons under “meaningful human control”
  • A new Oxford-Cambridge research center for studying AI-relevant policy

As Skype founder Jaan Tallinn, one of FLI’s founders, has described this new research direction, “Building advanced AI is like launching a rocket. The first challenge is to maximize acceleration, but once it starts picking up speed, you also need to to focus on steering.”

When the Future of Life Institute issued an open letter in January calling for research on how to keep AI both robust and beneficial, it was signed by a long list of AI researchers from academia, nonprofits and industry, including AI research leaders from Facebook, IBM, and Microsoft and the founders of Google’s DeepMind Technologies. It was seeing that widespread agreement that moved Elon Musk to seed the research program that has now begun.

“Here are all these leading AI researchers saying that AI safety is important”, said Musk at the time. “I agree with them, so I’m today committing $10M to support research aimed at keeping AI beneficial for humanity.”

“I am glad to have an opportunity to carry this research focused on increasing the transparency of AI robotic systems,” said Manuela Veloso, past president of the Association for the Advancement of Artificial Intelligence (AAAI) and winner of one of the grants.

“This grant program was much needed: because of its emphasis on safe AI and multidisciplinarity, it fills a gap in the overall scenario of international funding programs,” added Prof. Francesca Rossi, president of the International Joint Conference on Artificial Intelligence (IJCAI), also a grant awardee.

Tom Dietterich, president of the AAAI, described how his grant — a project studying methods for AI learning systems to self-diagnose when failing to cope with a new situation — breaks the mold of traditional research:

“In its early days, AI research focused on the ‘known knowns’ by working on problems such as chess and blocks world planning, where everything about the world was known exactly. Starting in the 1980s, AI research began studying the ‘known unknowns’ by using probability distributions to represent and quantify the likelihood of alternative possible worlds. The FLI grant will launch work on the ‘unknown unknowns’: How can an AI system behave carefully and conservatively in a world populated by unknown unknowns — aspects that the designers of the AI system have not anticipated at all?”

As Terminator Genisys debuts this week, organizers stressed the importance of separating fact from fiction. “The danger with the Terminator scenario isn’t that it will happen, but that it distracts from the real issues posed by future AI”, said FLI president Max Tegmark. “We’re staying focused, and the 37 teams supported by today’s grants should help solve such real issues.”

The full list of research grant winners can be found here. The plan is to fund these teams for up to three years, with most of the research projects starting by September 2015, and to focus the remaining $4M of the Musk-backed program on the areas that emerge as most promising.

FLI has a mission to catalyze and support research and initiatives for safeguarding life and developing optimistic visions of the future, including positive ways for humanity to steer its own course considering new technologies and challenges.

Contacts at the Future of Life Institute:

  • Max Tegmark: max@futureoflife.org
  • Meia Chita-Tegmark: meia@futureoflife.org
  • Jaan Tallinn: jaan@futureoflife.org
  • Anthony Aguirre: anthony@futureoflife.org
  • Viktoriya Krakovna: vika@futureoflife.org
  • Jesse Galef: jesse@futureoflife.org

 

Elon Musk donates $10M to keep AI beneficial

Thursday January 15, 2015

We are delighted to report that technology inventor Elon Musk, creator of Tesla and SpaceX, has decided to donate $10M to the Future of Life Institute to run a global research program aimed at keeping AI beneficial to humanity.

There is now a broad consensus that AI research is progressing steadily, and that its impact on society is likely to increase. A long list of leading AI-researchers have signed an open letter calling for research aimed at ensuring that AI systems are robust and beneficial, doing what we want them to do. Musk’s donation aims to support precisely this type of research: “Here are all these leading AI researchers saying that AI safety is important”, says Elon Musk. “I agree with them, so I’m today committing $10M to support research aimed at keeping AI beneficial for humanity.”

Musk’s announcement was welcomed by AI leaders in both academia and industry:

“It’s wonderful, because this will provide the impetus to jump-start research on AI safety”, said AAAI president Tom Dietterich. “This addresses several fundamental questions in AI research that deserve much more funding than even this donation will provide.”

“Dramatic advances in artificial intelligence are opening up a range of exciting new applications”, said Demis Hassabis, Shane Legg and Mustafa Suleyman, co-founders of DeepMind Technologies, which was recently acquired by Google. “With these newfound powers comes increased responsibility. Elon’s generous donation will support researchers as they investigate the safe and ethical use of artificial intelligence, laying foundations that will have far reaching societal impacts as these technologies continue to progress”.


Elon Musk and AAAI President Thomas Dietterich comment on the announcement
The $10M program will be administered by the Future of Life Institute, a non-profit organization whose scientific advisory board includes AI-researchers Stuart Russell and Francesca Rossi. “I love technology, because it’s what’s made 2015 better than the stone age”, says MIT professor and FLI president Max Tegmark. “Our organization studies how we can maximize the benefits of future technologies while avoiding potential pitfalls.”

The research supported by the program will be carried out around the globe via an open grants competition, through an application portal at http://futureoflife.org that will open by Thursday January 22. The plan is to award the majority of the grant funds to AI researchers, and the remainder to AI-related research involving other fields such as economics, law, ethics and policy (a detailed list of examples can be found here). “Anybody can send in a grant proposal, and the best ideas will win regardless of whether they come from academia, industry or elsewhere”, says FLI co-founder Viktoriya Krakovna.

“This donation will make a major impact”, said UCSC professor and FLI co-founder Anthony Aguirre: “While heavy industry and government investment has finally brought AI from niche academic research to early forms of a potentially world-transforming technology, to date relatively little funding has been available to help ensure that this change is actually a net positive one for humanity.”

“That AI systems should be beneficial in their effect on human society is a given”, said Stuart Russell, co-author of the standard AI textbook “Artificial Intelligence: a Modern Approach”. “The research that will be funded under this program will make sure that happens. It’s an intrinsic and essential part of doing AI research.”

Skype-founder Jaan Tallinn, one of FLI’s founders, agrees: “Building advanced AI is like launching a rocket. The first challenge is to maximize acceleration, but once it starts picking up speed, you also need to to focus on steering.”

Along with research grants, the program will also include meetings and outreach programs aimed at bringing together academic AI researchers, industry AI developers and other key constituents to continue exploring how to maximize the societal benefits of AI; one such meeting was held in Puerto Rico last week with many of the open-letter signatories.

“Hopefully this grant program will help shift our focus from building things just because we can, toward building things because they are good for us in the long term”, says FLI co-founder Meia Chita-Tegmark.

Contacts at Future of Life Institute:

  • Max Tegmark: max@futureoflife.org
  • Meia Chita-Tegmark: meia@futureoflife.org
  • Jaan Tallinn: jaan@futureoflife.org
  • Anthony Aguirre: anthony@futureoflife.org
  • Viktoriya Krakovna: vika@futureoflife.org

Contacts among AI researchers:

  • Prof. Tom Dietterich, President of the Association for the Advancement of Artificial Intelligence (AAAI), Director of Intelligent Systems: tgd@eecs.oregonstate.edu
  • Prof. Stuart Russell, Berkeley, Director of the Center for Intelligent Systems, and co-author of the standard textbook Artificial Intelligence: a Modern Approach: russell@cs.berkeley.edu
  • Prof. Bart Selman, co-chair of the AAAI presidential panel on long-term AI futures: selman@cs.cornell.edu
  • Prof. Francesca Rossi, Professor of Computer Science, University of Padova and Harvard University, president of the International Joint Conference on Artificial Intelligence (IJCAI): frossi@math.unipd.it
  • Prof. Murray Shanahan, Imperial College: m.shanahan@imperial.ac.uk


Max Tegmark interviews Elon Musk about his life, his interest in the future of humanity and the background to his donation