Demis Hassabis: DeepMind - AI, Superintelligence & the Future of Humanity | Lex Fridman Podcast #299

The following is a conversation with Demis Hassabis, CEO and co-founder of DeepMind, a company that has published and built some of the most incredible artificial intelligence systems in the history of computing, including AlphaZero that learned all by itself to play the game of Go better than any human in the world and AlphaFold2 that solved protein folding. Both tasks considered nearly impossible for a very long time. Demis is widely considered to be one of the most brilliant and impactful humans in the history of artificial intelligence and science and engineering in general. This was truly an honor and a pleasure for me to finally sit down with him for this conversation and I'm sure we will talk many times again in the future. This is the Lux Freedman podcast. To support it, please check out our sponsors in the description. And now, dear friends, here's Demis Hassabis. Let's start with a bit of a personal question. Am I an AI program you wrote to interview people until I get good enough to interview you? Well, I'd be impressed if you were. I'd be impressed with myself if you were. I don't think we're quite up to that yet but maybe you're from the future, Lex. If you did, would you tell me? Is that a good thing to tell a language model that's tasked with interviewing that it is in fact AI? Maybe we're in a kind of meta-Turing test. Probably it would be a good idea not to tell you so it doesn't change your behavior, right? This is a kind of Heisenberg uncertainty principle situation. If I told you, you'd behave differently. Maybe that's what's happening with us, of course. This is a benchmark from the future where they replay 2022 as a year before AIs were good enough yet and now we want to see, is it going to pass? Exactly. If I was such a program, would you be able to tell, do you think? So to the Turing test question, you've talked about the benchmark for solving intelligence. What would be the impressive thing? You've talked about winning a Nobel Prize and AI system winning a Nobel Prize but I still return to the Turing test as a compelling test. The spirit of the Turing test is a compelling test. Yeah, the Turing test of course it's been unbelievably influential and Turing's one of my all-time heroes but I think if you look back at the 1950 paper, his original paper and read the original, you'll see I don't think he meant it to be a rigorous formal test. I think it was more like a thought experiment, almost a bit of philosophy he was writing if you look at the style of the paper. You can see he didn't specify it very rigorously. For example, he didn't specify the knowledge that the expert or judge would have, how much time would they have to investigate this. So these are important parameters if you were going to make it a true sort of formal test. By some measures, people claim the Turing test passed several, you know, a decade ago. I remember someone claiming that with a kind of very bog standard normal logic model because they pretended it was a kid. So the judges thought that the machine, you know, was a child. So that would be very different from an expert AI person interrogating a machine and knowing how it was built and so on. So I think, you know, we should probably move away from that as a formal test and move more towards a general test where we test the AI capabilities on a range of tasks and see if it reaches human level or above performance on maybe thousands, perhaps even millions of tasks eventually, and cover the entire sort of cognitive space. So I think for its time, it was an amazing thought experiment. And also 1950s, obviously, it was barely the dawn of the computer age. So of course, he only thought about text. And now we have a lot more different inputs. So yeah, maybe the better thing to test is the generalizability. So across multiple tasks, multiple tasks, but I think it's also possible as systems like God will show that eventually that might map right back to language. So you might be able to demonstrate your ability to generalize across tasks by then communicating your ability to generalize across tasks, which is kind of what we do through conversation anyway, when we jump around. Ultimately, what's in there in that conversation is not just you moving around knowledge. It's you moving around like these entirely different modalities of understanding that ultimately map to your ability to operate successfully in all of these domains, which you can think of as tasks. Yeah, I think certainly we as humans use language as our main generalization communication tool. So I think we end up thinking in language and expressing our solutions in language. So it's going to be a very powerful mode in which to explain the system, to explain what it's doing. But I don't think it's the only modality that matters. So I think there's going to be a lot of, you know, there's a lot of different ways to express capabilities other than just language. Yeah, visual, robotics, body language. Yeah, actions, the interactive aspect of all that, that's all part of it. But what's interesting with GATO is that it's sort of pushing prediction to the maximum in terms of like, you know, mapping arbitrary sequences to other sequences and sort of just predicting what's going to happen next. So prediction seems to be fundamental to intelligence. And what you're predicting doesn't so much matter. Yeah, it seems like you can generalize that quite well. So obviously language models predict the next word. GATO predicts potentially any action or any token. And it's just the beginning, really. It's our most general agent, one could call it so far. But, you know, that itself can be scaled up massively more than we've done so far. And obviously, we're in the middle of doing that. But the big part of solving AGI is creating benchmarks that help us get closer and closer, sort of creating benchmarks that test the generalizability. And it's just still interesting that this fella, Alan Turing, was one of the first and probably still one of the only people that was trying, maybe philosophically, but was trying to formulate a benchmark that could be followed. It is, even though it's fuzzy, it's still sufficiently rigorous to where you can run that test. And I still think something like the Turing test will, at the end of the day, be the thing that truly impresses other humans. So that you can have a close friend who's in the AI system. For that friend to be a good friend, they're going to have to be able to play Starcraft and they're going to have to do all of these tasks. Get you a beer, so the robotics tasks, play games with you, use language, humor, all those kinds of things. But that ultimately can ultimately can boil down to language. It feels like, not in terms of the AI community, but in terms of the actual impact of general intelligence on the world, it feels like language will be the place where it truly shines. I think so, because it's such an important kind of input output for us. I think you're right. I think the Turing test, the kind of the philosophy behind it, which is the idea of can a machine mimic the behaviors of a human? And I would say wider than just language and text, then in terms of actions and everything else, creativity, all these things, then if it can sort of match or exceed human cognitive capabilities, then I think we have a true intelligence. So from that perspective, you're right. I think he did formulate the right kind of setup. I just, I think there'll be a kind of humor in the AI systems of the future, looking back to this conversation and thinking about the Turing test and also thinking about by that time, they would know which year they were finally able to sort of cross the threshold of human level intelligence and think how funny it is that we humans were still confused about this whole problem that has been long ago been solved. Well, hello to the AI agents of the future. Anyway, so going back to your journey, when did you fall in love with programming first? Well, it was pretty young age actually. So, you know, I started off, actually games was my first love. So starting to play chess when I was around four years old, and then it was actually with winnings from a chess competition that I managed to buy my first chess computer when I was about eight years old. It was a ZX Spectrum, which was hugely popular in the UK at the time. And it was an amazing machine because I think it trained a whole generation of programmers in the UK because it was so accessible. You know, you literally switched it on and there was the basic prompt and you could just get going. And my parents didn't really know anything about computers. So, but because it was my money from a chess competition, I could say I wanted to buy it. And then, you know, I just went to bookstores, got books on programming and started typing in, you know, the programming code. And then, of course, once you start doing that, you start adjusting it and then making your own games. And that's when I fell in love with computers and realized that they were a very magical device. In a way, I kind of, I wouldn't have been able to explain this at the time, but I felt that they were sort of almost a magical extension of your mind. I always had this feeling and I've always loved this about computers that you can set them off doing something, some task for you, you can go to sleep, come back the next day and it's solved. You know, that feels magical to me. So, I mean, all machines do that to some extent. They all enhance our natural capabilities. Obviously, cars make us allow us to move faster than we can run, but this was a machine to extend the mind. And then, of course, AI is the ultimate expression of what a machine may be able to do or learn. So, very naturally for me, that thought extended into AI quite quickly. Do you remember the programming language that was first started? Yeah. Was it special to the machine? No, it was just a basic. I think it was just basic on the ZX Spectrum. I don't know what specific form it was. And then later on, I got a Commodore Amiga, which was a fantastic machine. Now you're just showing off. So, yeah, well, lots of my friends had Atari STs and I managed to get Amigas. It was a bit more powerful and that was incredible. And used to do programming in Assembler and also Amos Basic, this specific form of basic. It was incredible, actually. So, I learned all my coding skills. And when did you fall in love with AI? So, when did you first start to gain an understanding that you can not just write programs that do some mathematical operations for you while you sleep, but something that's akin to bringing an entity to life, sort of a thing that can figure out something more complicated than a simple mathematical operation? Yeah. So, there was a few stages for me all while I was very young. So, first of all, as I was trying to improve at playing chess, I was captaining various England Junior chess teams. And at the time when I was about maybe 10, 11 years old, I was going to become a professional chess player. That was my first thought. So, that dream was there to try to get to the highest level. Yeah. So, when I was about 12 years old, I got to master standard and I was second highest rated player in the world to Judith Polger, who obviously ended up being an amazing chess player and a world women's champion. And when I was trying to improve at chess, what you do is you, obviously, first of all, you're trying to improve your own thinking processes. So, that leads you to thinking about thinking. How is your brain coming up with these ideas? Why is it making mistakes? How can you improve that thought process? But the second thing is that you, it was just the beginning. This was like in the early 80s, mid 80s of chess computers. If you remember, they were physical balls like the one we have in front of us and you press down the squares. I think Kasparov had a branded version of it that I got. They're not as strong as they are today, but they were pretty strong and you used to practice against them to try and improve your openings and other things. So, I remember, I think I probably got my first one when I was around 11 or 12. I remember thinking, this is amazing. How has someone programmed this chess ball to play chess? It was a very formative book I bought, which was called the Chess Computer Handbook by David Levy. It came out in 1984 or something. So, I must have got it when I was about 11 or 12. It explained fully how these chess programs are made. I remember my first AI program programming my Amiga. It wasn't powerful enough to play chess. I couldn't write a whole chess program, but I wrote a program for it to play Othello or Reversi. It's sometimes called, I think, in the US. It's a slightly simpler game than chess, but I used all of the principles that chess programs had. Alpha, beta, search, all of that. That was my first AI program. I remember that very well. I was around 12 years old. So, that brought me into AI. Then the second part was later on. I was around 16 or 17 and I was writing games professionally, designing games, writing a game called Theme Park, which had AI as a core gameplay component as part of the simulation. It sold millions of copies around the world. People loved the way that the AI, even though it was relatively simple by today's AI standards, was reacting to the way you as the player played it. It was called a sandbox game. It was one of the first types of games like that along with SimCity. It meant that every game you played was unique. Is there something you could say, just on a small tangent, about really impressive AI from a game design, human enjoyment perspective? Really impressive AI that you've seen in games and maybe what does it take to create AI system? How hard of a problem is that? A million questions just as a brief tangent. Well, look, I think games have been significant in my life for three reasons. First of all, I was playing them and training myself on games when I was a kid. Then I went through a phase of designing games and writing AI for games. All the games I professionally wrote had AI as a core component. That was mostly in the 90s. The reason I was doing that in the games industry was at the time, the games industry, I think, was the cutting edge of technology. Whether it was graphics with people like John Carmack and Quake and those kinds of things or AI, I think actually all the action was going on in games. We're still reaping the benefits of that even with things like GPUs, which I find ironic. It was obviously invented for graphics, computer graphics, but then turns out to be amazingly useful for AI. It just turns out everything's a matrix multiplication, it appears in the whole world. I think games at the time had the most cutting edge AI. A lot of the games, I was involved in writing. There was a game called Black and White, which was one game I was involved with in the early stages of, which I still think is the most impressive example of reinforcement learning in a computer game. In that game, you trained a little pet animal. It's a brilliant game. Yeah. It's learned from how you were treating it. If you treated it badly, then it became mean. It would be mean to your villagers and your population, the little tribe that you were running. If you were kind to it, then it would be kind. People were fascinated by how that was. So was I, to be honest, with the way it developed. Especially the mapping to good and evil. It made me realize that in the choices you make, you can define where you end up. That means all of us are capable of the good and evil. It all matters in the different choices along the trajectory to those places that you make. It's fascinating. Games can do that philosophically to you. It's rare. It seems rare. Yeah. Well, games are, I think, a unique medium because you as the player, you're not just passively consuming the entertainment, right? You're actually actively involved as an agent. So I think that's what makes it, in some ways, can be more visceral than other mediums like films and books. So that was designing AI in games. Then the third use we've used of AI is in DeepMind from the beginning, which is using games as a testing ground for proving out AI algorithms and developing AI algorithms. That was a core component of our vision at the start of DeepMind, was that we would use games very heavily as our main testing ground, certainly to begin with, because it's super efficient to use games. Also, it's very easy to have metrics to see how well your systems are improving and what direction your ideas are going in and whether you're making incremental improvements. And because those games are often rooted in something that humans did for a long time beforehand, there's already a strong set of rules. It's already a damn good benchmark. Yes. It's really good for so many reasons because you've got clear measures of how good humans can be at these things. In some cases, like Go, we've been playing it for thousands of years. Often they have scores or at least win conditions. So it's very easy for reward learning systems to get a reward. It's very easy to specify what that reward is. Also, at the end, it's easy to test externally how strong is your system by, of course, playing against the world's strongest players at those games. It's so good for so many reasons. It's also very efficient to run potentially millions of simulations in parallel on the cloud. I think there's a huge reason why we were so successful back in, you know, starting out 2010. How come we were able to progress so quickly? Because we've utilized games. At the beginning of DeepMind, we also hired some amazing game engineers who I knew from my previous lives in the games industry. That helped to bootstrap us very quickly. Plus, it's somehow super compelling, almost at a philosophical level of man versus machine over a chess board or a Go board. And especially given that the entire history of AI is defined by people saying it's going to be impossible to make a machine that beats a human being in chess. And then once that happened, people were certain when I was coming up in AI that Go is not a game that can be solved because of the combinatorial complexity. It's just too, it's, you know, no matter how much Moore's law you have, compute is just never going to be able to crack the game of Go. And so then there's something compelling about facing, sort of, taking on the impossibility of that task from the AI researcher perspective, engineer perspective. And then as a human being, just observing this whole thing, your beliefs about what you thought was impossible being broken apart, it's humbling to realize we're not as smart as we thought. It's humbling to realize that the things we think are impossible now perhaps will be done in the future. There's something really powerful about a game, AI system beating a human being in a game that drives that message home for like millions, billions of people, especially in the case of Go. Sure. Well, look, I think it's a, I mean, it has been a fascinating journey and especially as I think about it from, I can understand it from both sides, both as the AI, you know, creators of the AI, but also as a games player originally. So, you know, it was a really interesting, you know, I mean, it was a fantastic, but also somewhat bittersweet moment, the Alpha Go match for me, seeing that and being obviously heavily, heavily involved in that. But, you know, as you say, chess has been the, I mean, Kasparov, I think rightly called it the Drosophila of intelligence, right? So it's sort of, I love that phrase. And I think he's right, because chess has been hand in hand with AI from the beginning of the whole field, right? So I think every AI practitioner, starting with Turing and Claude Shannon and all those, the sort of forefathers of the field, tried their hand at writing a chess program. I've got an original edition of Claude Shannon's first chess program, I think it was 1949, the original sort of paper. And they all did that. And Turing famously wrote a chess program that, but all the computers around them were obviously too slow to run it. So he had to run, he had to be the computer, right? So he literally, I think, spent two or three days running his own program by hand with pencil and paper and playing, playing a friend of his with his chess program. So, of course, Deep Blue was a huge moment, beating Kasparov. But actually, when that happened, I remember that very, very vividly, of course, because it was, you know, chess and computers and AI, all the things I loved, and I was at college at the time. But I remember coming away from that being more impressed by Kasparov's mind than I was by Deep Blue. Because here was Kasparov with his human mind, not only could he play chess more or less to the same level as this brute of a calculation machine. But of course, Kasparov can do everything else humans can do, ride a bike, talk many languages, do politics, all the rest of the amazing things that Kasparov does. And so with the same brain, and yet Deep Blue, brilliant as it was at chess, it had been hand coded for chess, and actually had distilled the knowledge of chess grandmasters into a cool program. But it couldn't do anything else. Like, it couldn't even play a strictly simpler game like tic-tac-toe. So something to me was missing from intelligence from that system that we would regard as intelligence. And I think it was this idea of generality and also learning. So that's what we tried to do with AlphaGo. Yeah, with AlphaGo and AlphaZero and MuZero and then Gato and all the things that we'll get into some parts of. There's just a fascinating trajectory here. But let's just stick on chess briefly. On the human side of chess, you've proposed that from a game design perspective, the thing that makes chess compelling as a game is that there's a creative tension between a bishop and the knight. Can you explain this? First of all, it's really interesting to think about what makes a game compelling, it makes it stick across centuries. Yeah, I was sort of thinking about this. And actually, a lot of even amazing chess players don't think about it necessarily from a game designer point of view. So it's with my game design hat on that I was thinking about this. Why is chess so compelling? And I think a critical reason is the dynamicness of the different kind of chess positions you can have, whether they're closed or open and other things, comes from the bishop and the knight. So if you think about how different the capabilities of the bishop and knight are in terms of the way they move, and then somehow chess has evolved to balance those two capabilities more or less equally. So they're both roughly worth three points each. So you think that dynamics is always there, and then the rest of the rules are kind of trying to stabilize the game. Well, maybe. I mean, it's sort of, I don't know, it's chicken and egg situation, probably both came together. But the fact that it's got to this beautiful equilibrium, where you can have the bishop and knight, they're so different in power, but so equal in value across the set of the universe of all positions, right? Somehow they've been balanced by humanity over hundreds of years, I think gives the game the creative tension that you can swap the bishop and knight, or bishop for a knight, and they're more or less worth the same, but now you aim for a different type of position. If you have the knight, you want a closed position. If you have the bishop, you want an open position. So I think that creates a lot of the creative tension in chess. So some kind of controlled creative tension. From an AI perspective, do you think AI systems could eventually design games that are optimally compelling to humans? Well, that's an interesting question. Sometimes I get asked about AI and creativity, and the way I answer that is relevant to that question, which is that I think there are different levels of creativity, one could say. So I think if we define creativity as coming up with something original, right, that's useful for a purpose, then I think the kind of lowest level of creativity is like an interpolation, so an averaging of all the examples you see. So maybe a very basic AI system could say you could have that. So you show it millions of pictures of cats, and then you say, give me an average looking cat, right, generate me an average looking cat. I would call that interpolation. Then there's extrapolation, which something like AlphaGo showed. So AlphaGo played, you know, millions of games of Go against itself, and then it came up with brilliant new ideas like move 37 in game two, brilliant motif strategies in Go that no humans had ever thought of, even though we've played it for thousands of years and professionally for hundreds of years. So that, I call that extrapolation. But then that's still, there's still a level above that, which is, you know, you could call out of the box thinking or true innovation, which is, could you invent Go, right? Could you invent chess and not just come up with a brilliant chess move or brilliant Go move, but can you actually invent chess or something as good as chess or Go? And I think one day, AI could, but what's missing is how would you even specify that task to a program right now? And the way I would do it, if I was telling a human to do it or a games designer, a human games designer to do it is I would say something like Go, I would say, come up with a game that only takes five minutes to learn, which Go does, because it's got simple rules, but many lifetimes to master, right? Or impossible to master in one lifetime because it's so deep and so complex. And then it's aesthetically beautiful. And also it can be completed in three or four hours of gameplay time, which is, you know, useful for our, us, you know, in a human day. And so you might specify these sort of high level concepts like that. And then, you know, with that, and maybe a few other things one could imagine that Go satisfies those constraints. But the problem is, is that we're not able to specify abstract notions like that, high level abstract notions like that yet to our AI systems. And I think there's still something missing there in terms of high level concepts or abstractions that they truly understand and, you know, combinable and compositional. So for the moment, I think AI is capable of doing interpolation and extrapolation, but not true invention. So coming up with rule sets and optimizing with complicated objectives around those rule sets, we can't currently do, but you could take a specific rule set and then run a kind of self-play experiment to see how long, just observe how an AI system from scratch learns, how long is that journey of learning? And maybe if it satisfies some of those other things you mentioned in terms of quickness to learn and so on, and you could see a long journey to master for even an AI system, then you could say that this is a promising game. But it would be nice to do almost like alpha codes or programming rules. So generating rules that automate even that part of the generation of rules. John Williams So I have thought about systems actually, I think it'd be amazing for a games designer if you could have a system that takes your game, plays it tens of millions of times, maybe overnight, and then self-balances the rules better. So it tweaks the rules and maybe the equations and the parameters so that the game is more balanced, the units in the game or some of the rules could be tweaked. So it's a bit of like giving a base set and then allowing Monte Carlo tree search or something like that to sort of explore it. And I think that would be super powerful tool actually for balancing, auto balancing a game, which usually takes thousands of hours from hundreds of games, human games testers normally to balance some game like Starcraft, which is, Blizzard are amazing at balancing their games, but it takes them years and years and years. So one could imagine at some point when this stuff becomes efficient enough to, you might better do that like overnight. John Williams Do you think a game that is optimal designed by an AI system would look very much like a planet Earth? John Williams Maybe, maybe. It's only the sort of game I would love to make is, and I've tried, in my games career, the games design career, my first big game was designing a theme park, an amusement park. Then with games like Republic, I tried to have games where we designed whole cities and allowed you to play in. So, and of course, people like Will Wright have written games like Sim Earth, trying to simulate the whole of Earth, pretty tricky, but I think- Lex Lucas Sim Earth, I haven't actually played that one. So what is it? Does it incorporate evolution? John Williams Yeah, it has evolution and it sort of treats it as an entire biosphere, but from quite high level. Lex Lucas It'd be nice to be able to sort of zoom in, zoom out, and zoom in. John Williams Exactly. So obviously it couldn't do that. That was in the 90s. I think he wrote that in the 90s. So it couldn't, it wasn't able to do that. But that would be obviously the ultimate sandbox game, of course. Lex Lucas On that topic, do you think we're living in a simulation? John Williams Yes. Well, so, okay. Lex Lucas We're going to jump around from the absurdly philosophical to the technical. John Williams Sure, sure. Very, very happy to. So I think my answer to that question is a little bit complex because there is simulation theory, which obviously Nick Bostrom, I think, famously first proposed. And I don't quite believe it in that sense. So in the sense that are we in some sort of computer game or have our descendants somehow recreated Earth in the 21st century and for some kind of experimental reason. But I do think that the best way to understand physics and the universe is from a computational perspective. So understanding it as an information universe, and actually information being the most fundamental unit of reality, rather than matter or energy. So physicists would say, matter or energy, E equals mc squared, these are the things that are the fundamentals of the universe. I'd actually say information, which of course itself can be, can specify energy or matter, right? Matter is actually just, you know, we're just out the way our bodies and the molecules in our body are arranged as information. So I think information may be the most fundamental way to describe the universe. And therefore, you could say we're in some sort of simulation because of that. But I do, I'm not really a subscriber to the idea that, you know, these are sort of throwaway billions of simulations around. I think this is actually very critical and possibly unique, this simulation. LUIS So this particular one? ALI Yes. LUIS So, but, and you just mean treating the universe as a computer that's processing and modifying information is a good way to solve the problems of physics, of chemistry, of biology, and perhaps of humanity and so on. ALI Yes. I think understanding physics in terms of information theory might be the best way to really understand what's going on here. LUIS From our understanding of a universal Turing machine, from our understanding of a computer, do you think there's something outside of the capabilities of a computer that is present in our universe? You have a disagreement with Roger Penrose about the nature of consciousness. He thinks that consciousness is more than just a computation. Do you think all of it, the whole shebang, can be a computation? ALI Yeah, I've had many fascinating debates with Sir Roger Penrose and obviously he's famously, and I read, you know, Emperors of the New Mind and his books, his classical books, and they were pretty influential in the, you know, in the 90s. And he believes that there's something more, you know, something quantum that is needed to explain consciousness in the brain. I think about what we're doing actually at DeepMind and what my career is being. We're almost like Turing's champion. So we are pushing Turing machines or classical computation to the limits. What are the limits of what classical computing can do? Now, and at the same time, I've also studied neuroscience to see, and that's why I did my PhD in, was to see, also to look at, you know, is there anything quantum in the brain from a neuroscience or biological perspective? And so far, I think most neuroscientists and most mainstream biologists and neuroscientists would say there's no evidence of any quantum systems or effects in the brain. As far as we can see, it can be mostly explained by classical theories. So, and then, so there's sort of the search from the biology side. And then at the same time, there's the raising of the water, the bar, from what classical Turing machines can do and, you know, including our new AI systems. And as you alluded to earlier, you know, I think AI, especially in the last decade plus, has been a continual story now of surprising events and surprising successes, knocking over one theory after another of what was thought to be impossible, you know, from Go to protein folding and so on. And so I think I would be very hesitant to bet against how far the universal Turing machine and classical computation paradigm can go. And my betting would be that all of certainly what's going on in our brain can probably be mimicked or approximated on a classical machine, not requiring something metaphysical or quantum. And we'll get there with some of the work with AlphaFold, which I think begins the journey of modeling this beautiful and complex world of biology. So you think all the magic of the human mind comes from this, just a few pounds of mush, of biological computational mush that's akin to some of the neural networks, not directly, but in spirit that DeepMind has been working with? Well, look, I think it's, you say it's a few, you know, of course, it's, this is the, I think, the biggest miracle of the universe is that it's just a few pounds of mush in our skulls. And yet it's also our brains are the most complex objects in the, that we know of in the universe. So there's something profoundly beautiful and amazing about our brains. And I think that it's an incredibly, incredible efficient machine and, and, and it's, you know, a phenomenon basically. And I think that building AI, one of the reasons I want to build AI, and I've always wanted to is, I think by building an intelligent artifact like AI, and then comparing it to the human mind, that will help us unlock the uniqueness and the true secrets of the mind that we've always wondered about since the dawn of history, like consciousness, dreaming, creativity, emotions, what are all these things, right? We've, we've, we've wondered about them since, since the dawn of humanity. And I think one of the reasons, and you know, I love philosophy and philosophy of mind is we found it difficult is there haven't been the tools for us to really, other than introspection to from very clever people in history, very clever philosophers to really investigate this scientifically. But now suddenly we have a plethora of tools. Firstly, we have all of the neuroscience tools, fMRI machines, single cell recording, all of this stuff. But we also have the ability computers and AI to build intelligent systems. So I think that, you know, I think it is amazing what the human mind does. And, and, and I'm kind of in awe of it really. And, and I think it's amazing that without human minds, we're able to build things like computers, and actually even, you know, think and investigate about these questions. I think that's also a testament to the human mind. Yeah, the universe built the human mind that now is building computers that help us understand both the universe and our own human mind. That's right. That's exactly it. I mean, I think that's one, you know, one could say we are, maybe we're the mechanism by which the universe is going to try and understand itself. Yeah. It's beautiful. So let's, let's go to the basic building blocks of biology that I think is another angle at which you can start to understand the human mind, the human body, which is quite fascinating, which is from the basic building blocks, start to simulate, start to model how from those building blocks, you can construct bigger and bigger, more complex systems, maybe one day, the entirety of the human biology. So here's another problem that thought to be impossible to solve, which is protein folding and alpha fold or specific specific alpha fold to did just that. It's solved protein folding. I think it's one of the biggest breakthroughs, certainly in the history of structural biology, but in general, and in science, maybe from a high level, what is it and how does it work? And then we can ask some fascinating questions after. Sure. So maybe to explain it to people not familiar with protein folding is, you know, first of all, explain proteins, which is, you know, proteins are essential to all life. Every function in your body depends on proteins. Sometimes they're called the workhorses of biology. And if you look into them and I've, you know, obviously as part of alpha fold, I've been researching proteins and, and, and structural biology for the last few years. You know, they're amazing little bio nano machines, proteins, incredible. If you actually watch little videos of how they work, animations of how they work and proteins are specified by their genetic sequence called amino acid sequence. So you can think it with their genetic makeup and then in the body, in nature, they, when they, when they fold up into a 3d structure, so you can think of it as a string of beads and then they fold up into a ball. Now, the key thing is you want to know what that 3d structure is because the structure, the 3d structure of a protein is what helps to determine what does it do, the function it does in your body. And also if you're interested in drug drugs or disease, you need to understand that 3d structure because if you want to target something with a drug compound about to block something, the protein is doing, you need to understand where it's going to bind on the surface of the protein. So obviously in order to do that, you need to understand the 3d structure. So the structure is mapped to the function, the structure is mapped to the function and the structure is obviously somehow specified by the, by the amino acid sequence. And that's the, in essence, the protein folding problem is can you just from the amino acid sequence, the one dimensional string of letters, can you immediately computationally predict the 3d structure? And this has been a grand challenge in biology for over 50 years. So I think it was first articulated by Christian Anfiensen, a Nobel Prize winner in 1972, as part of his Nobel Prize winning lecture. And he just speculated this should be possible to go from the amino acid sequence to the 3d structure, but he didn't say how. So it was, you know, it's been described to me as equivalent to Fermat's last theorem, but for biology, right? You should, as somebody that very well might win the Nobel Prize in the future, but outside of that, you should do more of that kind of thing in the margin, just put random things that will take like 200 years to solve. Set people off for 200 years. It should be possible. Exactly. And just don't give any indications. Exactly. I think everyone should, exactly, should be, I'll have to remember that for future. So yeah, so he set off, you know, with this one throwaway remark, just like Fermat, you know, he, he set off this whole 50 year field really of computational biology and, and they had, you know, they got stuck. They hadn't really got very far with doing this. And, and until now, until AlphaFold came along, this is done experimentally, right? Very painstakingly. So the rule of thumb is, and you have to like crystallize the protein, which is really difficult. Some proteins can't be crystallized like membrane proteins. And then you have to use very expensive electron microscopes or x-ray crystallography machines, really painstaking work to get the 3D structure and visualize the 3D structure. So the rule of thumb in, in, in experimental biology is that it takes one PhD student, their entire PhD to do one protein. And with AlphaFold 2, we're able to predict the 3D structure in a matter of seconds. And so we were, you know, over Christmas, we did the whole human proteome, or every protein in the human body, all 20,000 proteins. So the human proteomes like the equivalent of the human genome, but on protein space and, and sort of revolutionized really what structural biologists can do, because now they don't have to worry about these painstaking experimental, you know, should they put all of that effort in or not? They can almost just look up the structure of their proteins, like a Google search. And so there's a data set on which it's trained and how to map this amino acid sequence. First of all, it's incredible that a protein, this little chemical computer is able to do that computation itself in some kind of distributed way and do it very quickly. That's a weird thing. And they evolved that way because, you know, in the beginning, I mean, that's a great invention, just the protein itself. Yes, I mean- And then there's, I think, probably a history of, like, they evolved to have many of these proteins and those proteins figure out how to be computers themselves in such a way that you can create structures that can interact in complex ways with each other in order to form high level functions. I mean, it's a weird system that they figured it out. Well, for sure. I mean, we, you know, maybe we should talk about the origins of life too, but proteins themselves, I think, are magical and incredible. As I said, little, little bio-nano machines. And actually, Leventhal, who is another scientist, a contemporary of Amundsen, he coined this Leventhal, what became known as Leventhal's paradox, which is exactly what you're saying. He calculated roughly an average protein, which is maybe 2000 amino acids bases long, can fold in maybe 10 to the power 300 different conformations. So there's 10 to the power 300 different ways that protein could fold up. And yet somehow in nature, physics solves this, solves this in a matter of milliseconds. So proteins fold up in your body in, you know, sometimes in fractions of a second. So physics is somehow solving that search problem. And just to be clear, in many of these cases, maybe you can correct me if I'm wrong, there's often a unique way for that sequence to form itself. So among that huge number of possibilities, it figures out a way how to stably, in some cases, there might be a misfunction, so on, which leads to a lot of the disorders and stuff like that. But most of the time, it's a unique mapping. And that unique mapping is not obvious. No, exactly. It's just what the problem is. No, exactly. So there's a unique mapping, usually, in a healthy, if it's healthy. And as you say, in disease, so for example, Alzheimer's, one conjecture is that it's because of misfolded protein, a protein that folds in the wrong way, amyloid beta protein. And then because it folds in the wrong way, it gets tangled up, right, in your neurons. So it's super important to understand both healthy functioning and also disease is to understand, you know, what these things are doing and how they're structuring. Of course, the next step is sometimes proteins change shape when they interact with something. So they're not just static necessarily in biology. Maybe you can give some interesting, sort of beautiful things to you about these early days of AlphaFold, of solving this problem, because unlike games, this is real physical systems that are less amenable to self-play type of mechanisms. The size of the data set is smaller than you might otherwise like, so you have to be very clever about certain things. Is there something you could speak to? What was very hard to solve and what are some beautiful aspects about the solution? Yeah, I would say AlphaFold is the most complex and also probably most meaningful system we've built so far. So it's been an amazing time actually in the last, you know, two, three years to see that come through because as we talked about earlier, you know, games is what we started on at building things like AlphaGo and AlphaZero, but really the ultimate goal was to not just to crack games, it was just to build, use them to bootstrap general learning systems we could then apply to real world challenges. Specifically, my passion is scientific challenges like protein folding. And then AlphaFold of course is our first big proof point of that. And so, you know, in terms of the data and the amount of innovations that had to go into it, you know, it was like more than 30 different component algorithms needed to be put together to crack the protein folding. I think some of the big innovations were the kind of building in some hard coded constraints around physics and evolutionary biology to constrain sort of things like the bond angles in the protein and things like that, but not to impact the learning system. So still allowing the system to be able to learn the physics itself from the examples that we had. And the examples, as you say, there are only about 150,000 proteins, even after 40 years of experimental biology, only around 150,000 proteins have been, the structures have been found out about. So that was our training set, which is much less than normally we would like to use, but using various tricks, things like self-distillation. So actually using AlphaFold predictions, some of the best predictions that it thought was highly confident in, we put them back into the training set, right, to make the training set bigger. That was critical to AlphaFold working. So there was actually a huge number of different innovations like that, that were required to ultimately crack the problem. AlphaFold 1, what it produced was a distogram. So a kind of a matrix of the pairwise distances between all of the molecules in the protein. And then there had to be a separate optimization process to create the 3D structure. And what we did for AlphaFold 2 is make it truly end to end. So we went straight from the amino acid sequence of bases to the 3D structure directly without going through this intermediate step. And in machine learning, what we've always found is that the more end to end you can make it, the better the system. And it's probably because in the end, the system's better at learning what the constraints are than we are as the human designers of specifying it. So anytime you can let it flow end to end and actually just generate what it is you're really looking for, in this case the 3D structure, you're better off than having this intermediate step, which you then have to handcraft the next step for. So it's better to let the gradients and the learning flow all the way through the system from the end point, the end output you want to the inputs. So that's a good way to start on a new problem. Handcraft a bunch of stuff, add a bunch of manual constraints with a small end to end learning piece or a small learning piece and grow that learning piece until it consumes the whole thing. That's right. And so you can also see, you know, this is a bit of a method we've developed over doing many sort of successful alpha, we call them AlphaX projects, right? And the easiest way to see that is the evolution of AlphaGo to AlphaZero. So AlphaGo was a learning system, but it was specifically trained to only play Go, right? So, and what we wanted to do with the first version of AlphaGo is just get to world champion performance, no matter how we did it, right? And then, of course, AlphaGo Zero, we removed the need to use human games as a starting point, right? So it could just play against itself from random starting point from the beginning. So that removed the need for human knowledge about Go. And then finally, AlphaZero then generalized it so that any things we had in there, the system, including things like symmetry of the Go board, were removed. So that AlphaZero could play from scratch any two player game. And then MuZero, which is the final, our latest version of that set of things, was then extending it so that you didn't even have to give it the rules of the game. It would learn that for itself. So it could also deal with computer games as well as board games. So that line of AlphaGo, AlphaGo Zero, AlphaZero, MuZero, that's the full trajectory of what you can take from imitation learning to full self-supervised learning. Yeah, exactly. And learning the entire structure of the environment you're put in from scratch, right? And bootstrapping it through self-play yourself. But the thing is, it would have been impossible, I think, or very hard for us to build AlphaZero or MuZero first out of the box. Even psychologically, because you have to believe in yourself for a very long time. You're constantly dealing with doubt because a lot of people say that it's impossible. Exactly. So it was hard enough just to do Go, as you were saying. Everyone thought that was impossible, or at least a decade away from when we did it back in 2015, 2016. And so, yes, it would have been psychologically probably very difficult, as well as the fact that, of course, we learn a lot by building AlphaGo first. So I think this is why I call AI an engineering science. It's one of the most fascinating science disciplines, but it's also an engineering science in the sense that, unlike natural sciences, the phenomenon you're studying doesn't exist out in nature. You have to build it first. So you have to build the artifact first, and then you can study and pull it apart and how it works. This is tough to ask you this question, because you probably will say it's everything, but let's try to think through this, because you're in a very interesting position, where DeepMind is a place of some of the most brilliant ideas in the history of AI, but it's also a place of brilliant engineering. So how much of solving intelligence, this big goal for DeepMind, how much of it is science? How much is engineering? So how much is the algorithms? How much is the data? How much is the hardware compute infrastructure? How much is it the software compute infrastructure? What else is there? How much is the human infrastructure? And just the humans interacting in certain kinds of ways in the space of all those ideas, and how much is maybe philosophy? What's the key? If you were to look back, if we go forward 200 years and look back, what was the key thing that solved intelligence? Is it the ideas or the engineering? I think it's a combination. First of all, of course, it's a combination of all those things, but the ratios of them changed over time. So even in the last 12 years, so we started DeepMind in 2010, which is hard to imagine now, because 2010, it's only 12 short years ago, but nobody was talking about AI. I don't know if you remember back to your MIT days, no one was talking about it. I did a postdoc at MIT back around then, and it was sort of thought of as, well, look, we know AI doesn't work. We tried this hard in the 90s at places like MIT, mostly using logic systems and old-fashioned sort of good old-fashioned AI, we would call it now. People like Minsky and Patrick Winston, and you know all these characters, right? And I used to debate a few of them, and they used to think I was mad thinking about that some new advance could be done with learning systems. And I was actually pleased to hear that, because at least you know you're on a unique track at that point, right? Even if all of your professors are telling you you're mad. And of course in industry, we couldn't get, you know, it was difficult to get two cents together, which is hard to imagine now as well, given that it's the biggest sort of buzzword in VCs and fundraising's easy and all these kind of things today. So back in 2010, it was very difficult. And the reason we started then, and Shane and I used to discuss what were the sort of founding tenets of DeepMind, and it was various things. One was algorithmic advances, so deep learning, you know, Geoff Hinton and co had just sort of invented that in academia, but no one in industry knew about it. We love reinforcement learning, we thought that could be scaled up. But also understanding about the human brain had advanced quite a lot in the decade prior with fMRI machines and other things. So we could get some good hints about architectures and algorithms and sort of representations maybe that the brain uses, so at a systems level, not at a implementation level. And then the other big things were compute and GPUs, right? So we could see a compute was going to be really useful and it got to a place where it become commoditized mostly through the games industry, and that could be taken advantage of. And then the final thing was also mathematical and theoretical definitions of intelligence. So things like AIXI, AIXE, which Shane worked on with his supervisor, Marcus Hutter, which is sort of theoretical proof, really, of universal intelligence, which is actually a reinforcement learning system. In the limit, I mean, it assumes infinite compute and infinite memory in the way, you know, like a Turing machine proves. But I was also waiting to see something like that too, to, you know, like Turing machines and computation theory that people like Turing and Shannon came up with underpins modern computer science. You know, I was waiting for a theory like that to sort of underpin AGI research. So when I, you know, met Shane and saw he was working on something like that, you know, that to me was a sort of final piece of the jigsaw. So in the early days, I would say that ideas were the most important, you know, for us it was deep reinforcement learning, scaling up deep learning. Of course, we've seen transformers. So huge leaps, I would say, you know, three or four from, if you think from 2010 till now, huge evolutions, things like AlphaGo. And maybe there's a few more still needed. But as we get closer to AI, AGI, I think engineering becomes more and more important and data, because scale and of course the recent, you know, results of GPT-3 and all the big language models and large models, including our ones, has shown that scale is, and large models are clearly going to be a necessary, but perhaps not sufficient part of an AGI solution. And throughout that, like you said, and I'd like to give you a big thank you. You're one of the pioneers in this, is sticking by ideas like reinforcement learning, that this can actually work, given actually limited success in the past. And also, which we still don't know, but proudly having the best researchers in the world and talking about solving intelligence. So talking about whatever you call it, AGI or something like this, that speaking of MIT, that's just something you wouldn't bring up. Maybe you did in like 40, 50 years ago, but that was, AI was a place where you do tinkering, very small scale, not very ambitious projects. And maybe the biggest ambitious projects were in the space of robotics and doing like the DARPA challenge. But the task of solving intelligence and believing you can, that's really, really powerful. So in order for engineering to do its work, to have great engineers build great systems, you have to have that belief that threads throughout the whole thing that you can actually solve some of these impossible challenges. Yeah, that's right. And back in 2010, our mission statement, and still is today, it was used to be a solving step one, solve intelligence, step two, use it to solve everything else. So if you can imagine pitching that to VC in 2010, the kind of looks we got, we managed to find a few kooky people to back us, but it was tricky. And I got to the point where we wouldn't mention it to any of our professors because they would just eye roll and think we committed career suicide. And so it was, there's a lot of things that we had to do, but we always believed it. And one reason, by the way, one reason I've always believed in reinforcement learning is that if you look at neuroscience, that is the way that the primate brain learns. One of the main mechanisms is the dopamine system implements some form of TD learning. It was a very famous result in the late nineties where they saw this in monkeys and as a propagating prediction error. So again, in the limit, this is what I think you can use neuroscience for is, in any, and mathematics, when you're doing something as ambitious as trying to solve intelligence and you're, you know, it's blue sky research, no one knows how to do it. You need to use any evidence or any source of information you can to help guide you in the right direction or give you confidence you're going in the right direction. So that was one reason we pushed so hard on that. And just going back to your earlier question about organization, the other big thing that I think we innovated with at DeepMind to encourage invention and innovation was the multidisciplinary organization we built and we still have today. So DeepMind originally was a confluence of the most cutting edge knowledge in neuroscience with machine learning, engineering, and mathematics, right? And gaming. And then since then we built that out even further. So we have philosophers here and, and, you know, ethicists, but also other types of scientists, physicists, and so on. And that's what brings together. I tried to build a sort of new type of Bell Labs, but in its golden era, right? And, and a new expression of that to try and foster this incredible sort of innovation machine. So talking about the humans in the machine, DeepMind itself is a learning machine with a lots of amazing human minds in it coming together to try and build these learning systems. If we return to the big ambitious dream of AlphaFold that may be the early steps on a very long journey in, in biology, do you think the same kind of approach can use to predict the structure and function of more complex biological systems? So multi-protein interaction, and then, I mean, you can go out from there. Just simulating bigger and bigger systems that eventually simulate something like the human brain or the human body. It's just the big mush, the mess of the beautiful, resilient mess of biology. Do you, do you see that as a long-term vision? I do. And I think, you know, if you think about what are the things, top things I wanted to apply AI to once we had powerful enough systems, biology and curing diseases and understanding biology was right up there, you know, top of my list. That's one of the reasons I personally pushed that myself and with AlphaFold. But I think AlphaFold, amazing as it is, is just the beginning. And, and, and I hope it's evidence of what could be done with computational methods. So, you know, AlphaFold solved this, this huge problem of, of the structure of proteins, but biology is dynamic. So really what I imagine from here, and we're working on all these things now, is protein-protein interaction, protein-licken binding, so reacting with molecules. Then you want to get build up to pathways and then eventually a virtual cell. That's my dream, maybe in the next 10 years. And I've been talking actually to a lot of biologists, friends of mine, Paul Nurse, who runs the Crick Institute, amazing biologist, Nobel Prize winning biologist. We've been discussing for 20 years now, virtual cells. Could you build a virtual simulation of a cell? And if you could, that would be incredible for biology and disease discovery, because you could do loads of experiments on the virtual cell and then only at the last stage validate it in the wet lab. So you could, you know, in terms of the search space of discovering new drugs, you know, it takes 10 years roughly to go from, to go from, you know, identifying a target to having a drug candidate. Maybe that could be shortened to, you know, by an order of magnitude with if you could do most of that, that, that work in silico. So in order to get to a virtual cell, we have to build up understanding of different parts of biology and the interactions. And, and so we, you know, we, every, every few years we talk about this with, I talked about this with Paul. And then finally last year after AlphaFold, I said, now's the time we can finally go for it. And, and AlphaFold is the first proof point that this might be possible. And he's very excited. And we have some collaborations with his, with his lab, but they're just across the road, actually from us. So it's, you know, wonderful being here in Kings Cross with the Crick Institute across the road. And, and I think the next steps, you know, I think there's going to be some amazing advances in biology built on top of things like AlphaFold. We're already seeing that with the community doing that after we've open sourced it and released it. And, you know, I often say that I think if you think of mathematics is the perfect description language for physics, I think AI might be, end up being the perfect description language for biology, because biology is so messy. It's so emergent, so dynamic and complex. I think, I find it very hard to believe we'll ever get to something as elegant as Newton's laws of motions to describe a cell, right? It's just too complicated. So I think AI is the right tool for this. You have to, you have to start at the basic building blocks and use AI to run the simulation for all those building blocks. So have a very strong way to do prediction of what, given these building blocks, what kind of biology, how the, the function and the evolution of that biological system. It's almost like a cellular automata. You have to run it. You can't analyze it from a high level. You have to take the basic ingredients, figure out the rules and let it run. But in this case, the rules are very difficult to figure out. You have to learn them. Yes, exactly. That's exactly it. So it's the biology too complicated to figure out the rules. It's, it's too emergent, too dynamic, say compared to a physics system, like the motion of a planet. Yeah. Right. And, and so you have to learn the rules and that's exactly the type of systems that we're building. So you, you mentioned you've open sourced AlphaFold and even the data involved. To me personally, also really happy and a big thank you for open sourcing Majoko, the physics simulation engine that's, that's often used for robotics research and so on. So I think that's a pretty gangster move. So what, what's the, what's, I mean, this, this, very few companies or people do that kind of thing. What's the philosophy behind that? You know, it's a case by case basis. And in both those cases, we felt that was the maximum benefit to humanity to do that. And, and the scientific community, in one case, the robotics physics community with Majoko. So we purchased it. We purchased it for, yes, we purchased it for the express principle to open source it. So, so, you know, I hope people appreciate that. It's great to hear that you do. And then the second thing was, and mostly we did it because the person building it is, was not able to cope with supporting it anymore because it was, it got too big for him, his amazing professor who built it in the first place. So we helped him out with that. And then with AlphaFold is even bigger, I would say. And I think in that case, we decided that there were so many downstream applications of AlphaFold that we couldn't possibly even imagine what they all were. So the best way to accelerate drug discovery and also fundamental research would be to, to give all that data away and, and, and the, and the, and the system itself. You know, it's been so gratifying to see what people have done that within just one year, which is a short amount of time in science. And it's being used by over 500,000 researchers have used it. We think that's almost every biologist in the world. I think there's roughly 500,000 biologists in the world, professional biologists have used it to, to look at their proteins of interest. We've seen amazing fundamental research done. So a couple of weeks ago, front cover, there was a whole special issue of science, including the front cover, which had the nuclear pore complex on it, which is one of the biggest proteins in the body. The nuclear pore complex is a protein that governs all the nutrients going in and out of your cell nucleus. So they're like little hall gateways that open and close to let things go in and out of your cell nucleus. So they're really important, but they're huge because they're massive donut ring shape things. And they've been looking to try and figure out that structure for decades. And they have lots of, you know, experimental data, but it's too low resolution. There's bits missing. And they were able to like a giant Lego jigsaw puzzle, use alpha fold predictions, plus experimental data and combined those two independent sources of information, actually four different groups around the world were able to put it together at the same, more or less simultaneously using alpha fold predictions. So that's been amazing to see. And pretty much every pharma company, every drug company executive I've spoken to has said that their teams are using alpha fold to accelerate whatever drugs they're trying to discover. So I think the knock-on effect has been enormous in terms of the impact that alpha fold has made. And it's probably bringing in, it's creating biologists, it's bringing more people into the field, both on the excitement and both on the technical skills involved. And it's almost like a gateway drug to biology. Yes, it is. You follow up computational people involved too, hopefully. And I think for us, you know, the next stage, as I said, you know, in future, we have to have other considerations too. We're building on top of alpha fold and these other ideas I discussed with you about protein, protein interactions and genomics and other things. And not everything will be open source. Some of it will, we'll do commercially, because that will be the best way to actually get the most resources and impact behind it. And other ways, some other projects we'll do non-profit style. And also we have to consider for future things as well, safety and ethics as well, like, you know, synthetic biology, there are, you know, there is dual use. And we have to think about that as well. With alpha fold, we, you know, we consulted with 30 different bioethicists and other people expert in this field to make sure it was safe before we released it. So there'll be other considerations in future. But for right now, you know, I think alpha fold is a kind of a gift from us to the scientific community. So I'm pretty sure that something like alpha fold would be part of Nobel prizes in the future. But us humans, of course, are horrible with credit assignment. So we'll of course give it to the humans. Do you think there will be a day when AI system can't be denied that it earned that Nobel prize? Do you think we will see that in 21st century? It depends what type of AI we end up building, right? Whether they're, you know, goal seeking agents who specifies the goals, who comes up with the hypotheses, who, you know, who determines which problems to tackle, right? So I think... And tweets about it, announcement of the results. Yes, it's announcement of the results, exactly, as part of it. So I think right now, of course, it's amazing human ingenuity that's behind these systems. And then the system, in my opinion, is just a tool, you know, it'd be a bit like saying with Galileo and his telescope, you know, the ingenuity, the credit should go to the telescope. I mean, it's clearly Galileo building the tool which he then uses. So I still see that in the same way today, even though these tools learn for themselves. I think of things like AlphaFold and the things we're building as the ultimate tools for science and for acquiring new knowledge to help us as scientists acquire new knowledge. I think one day there will come a point where an AI system may solve or come up with something like general relativity of its own bat, not just by averaging everything on the internet or averaging everything on PubMed. Although that would be interesting to see what that would come up with. So that to me is a bit like our earlier debate about creativity, you know, inventing Go rather than just coming up with a good Go move. And so I think solving, I think to, you know, if we wanted to give it the credit of like a Nobel type of thing, then it would need to invent Go and sort of invent that new conjecture out of the blue, rather than being specified by the human scientists or the human creators. So I think right now that's, it's definitely just a tool. Although it is interesting how far you get by averaging everything on the internet, like you said, because, you know, a lot of people do see science as you're always standing on the shoulders of giants. And the question is, how much are you and the question is, how much are you really reaching up above the shoulders of giants? Maybe it's just a simulating different kinds of results of the past with ultimately this new perspective that gives you this breakthrough idea. But that idea may not be novel in the way that it can't be already discovered on the internet. Maybe the Nobel prizes of the next hundred years are already all there on the internet to be discovered. They could be, they could be. I mean, I think, this is one of the big mysteries, I think, is that, first of all, I believe a lot of the big new breakthroughs that are going to come in the next few decades, and even in the last decade, are going to come at the intersection between different subject areas, where there'll be some new connection that's found between what seemingly were disparate areas. And one can even think of deep mind, as I said earlier, as a sort of interdisciplinary between neuroscience ideas and AI engineering ideas originally. And so I think there's that. And then one of the things we can't imagine today is, and one of the reasons I think people, we were so surprised by how well large models worked, is that actually, it's very hard for our human minds, our limited human minds to understand what it would be like to read the whole internet, right? I think we can do a thought experiment, and I used to do this, of like, well, what if I read the whole of Wikipedia? What would I know? And I think our minds can just about comprehend maybe what that would be like, but the whole internet is beyond comprehension. So I think we just don't understand what it would be like to be able to hold all of that in mind, potentially, right? And then active at once, and then maybe what are the connections that are available there? So I think no doubt there are huge things to be discovered just like that. But I do think there is this other type of creativity of true spark of new knowledge, new idea, never thought before about, can't be averaged from things that are known, that really, of course, everything comes, you know, nobody creates in a vacuum, so there must be clues somewhere. But just a unique way of putting those things together, I think some of the greatest scientists in history have displayed that, I would say. Although it's very hard to know, going back to their time, what was exactly known when they came up with those things. Although, you're making me really think, because just the thought experiment of deeply knowing 100 Wikipedia pages, I don't think I can, I've been really impressed by Wikipedia, for technical topics. So if you know 100 pages or 1,000 pages, I don't think we can truly comprehend what kind of intelligence that is. It's a pretty powerful intelligence. If you know how to use that and integrate that information correctly, I think you can go really far. You can probably construct thought experiments based on that, like simulate different ideas. So if this is true, let me run this thought experiment, then maybe this is true. It's not really invention, it's like just taking literally the knowledge and using it to construct a very basic simulation of the world. I mean, some argue it's romantic in part, but Einstein would do the same kind of things with a thought experiment, right? Yeah, one could imagine doing that systematically across millions of Wikipedia pages, plus PubMed, all these things. I think there are many, many things to be discovered like that that are hugely useful. You could imagine, and I want us to do some of these things in material science, like room temperature superconductors is something on my list one day that I'd like to have an AI system to help build better optimized batteries, all of these sort of mechanical things. I think a systematic sort of search could be guided by a model, could be extremely powerful. So speaking of which, you have a paper on nuclear fusion, magnetic control of talking about plasmas through deep reinforcement learning. So you're seeking to solve nuclear fusion with deep RL, so it's doing control of high temperature plasmas. Can you explain this work? And can AI eventually solve nuclear fusion? It's been very fun last year or two, and very productive because we've been taking off a lot of my dream projects, if you like, of things that I've collected over the years of areas of science that I would like to, I think could be very transformative if we helped accelerate, and are really interesting problems, scientific challenges in and of themselves. So this is energy. So energy, yes, exactly. So energy and climate. So we talked about disease and biology as being one of the biggest places I think AI can help with. I think energy and climate is another one, so maybe they would be my top two. And fusion is one area I think AI can help with. Now, fusion has many challenges, mostly physics and material science and engineering challenges as well to build these massive fusion reactors and contain the plasma. And what we try to do, and whenever we go into a new field to apply our systems is we look for, we talk to domain experts, we try and find the best people in the world to collaborate with. In this case, in fusion, we collaborated with EPFL in Switzerland, the Swiss Technical Institute, who are amazing. They have a test reactor. They were willing to let us use, which I double checked with the team we were going to use carefully and safely. I was impressed they managed to persuade them to let us use it. And it's an amazing test reactor they have there. And they try all sorts of pretty crazy experiments on it. And what we tend to look at is if we go into a new domain like fusion, what are all the bottleneck problems? It's like thinking from first principles, what are all the bottleneck problems that are still stopping fusion working today? And then we look at, we get a fusion expert to tell us, and then we look at those bottlenecks and we look at the ones, which ones are amenable to our AI methods today. And would be interesting from a research perspective, from our point of view, from an AI point of view. And that would address one of their bottlenecks. And in this case, plasma control was perfect. So the plasma, it's a million degrees Celsius, something like that. It's hotter than the sun. And there's obviously no material that can contain it. So they have to be containing these magnetic, very powerful, superconducting magnetic fields. But the problem is plasma is pretty unstable, as you imagine. You're kind of holding a mini sun, mini star in a reactor. So you kind of want to predict ahead of time what the plasma is going to do. So you can move the magnetic field within a few milliseconds, to basically contain what it's going to do next. So it seems like a perfect problem if you think of it for like a reinforcement learning prediction problem. So you got a controller, you're going to move the magnetic field. And until we came along, they were doing it with traditional operational research type of controllers, which are kind of handcrafted. And the problem is, of course, they can't react in the moment to something the plasma is doing, that they have to be hard coded. And again, knowing that that's normally our go to solution is we would like to learn that instead. And they also had a simulator of these plasma. So there were lots of criteria that matched what we like to use. So can AI eventually solve nuclear fusion? Well, so we with this problem, and we published it in the Nature paper last year, we held the fusion that we held the plasma in a specific shapes. So actually, it's almost like carving the plasma into different shapes and control and hold it there for a record amount of time. So that's one of the problems of fusion sort of solved. So have a controller that's able to no matter the shape, contain it, contain it, yeah, contain it and hold it in structure. And there's different shapes that are better for for the energy productions called droplets and, and so on. So, so that was huge. And now we're looking, we're talking to lots of fusion startups to see what's the next problem we can tackle in the fusion area. So another fascinating place in a paper titled pushing the frontiers of density functionals by solving the fractional electron problem. So you're taking on modeling and simulating the quantum mechanical behavior of electrons. Yes. So, can you explain this work and can AI model and simulate arbitrary quantum mechanical systems in the future? Yeah, so this is another problem I've had my eye on for, you know, a decade or more, which is sort of simulating the properties of electrons. If you can do that, you can basically describe how elements and materials and substances work. So it's kind of like fundamental if you want to advance material science. And, you know, we have Schrodinger's equation, and then we have approximations to that density functional theory. These things are, you know, famous, and people try and write approximations to these, to these functionals and, and kind of come up with descriptions of the electron clouds, where they're going to go, how they're going to interact when you put two elements together. And what we try to do is learn a simulation, learn a functional that will describe more chemistry types of chemistry. So until now, you know, you can run expensive simulations, but then you can only simulate very small molecules, very simple molecules, we would like to simulate large materials. And so today, there's no way of doing that. And we're building up towards building functionals that approximate Schrodinger's equation, and then allow you to describe what the electrons are doing. And all material sort of science and material properties are governed by the electrons and how they interact. So have a good summarization of the simulation through the functional, but one that is still close to what the actual simulation will come out with. So what, how difficult is that task? What's involved in that task? Is it running those, those complicated simulations, and learning the task of mapping from the initial conditions and the parameters of the simulation, learning what the functional would be? Yeah. So it's pretty tricky. And we've done it with, you know, the nice thing is we, there are, we can run a lot of the simulations, the molecular dynamic simulations on our compute clusters. And so that generates a lot of data. So in this case, the data is generated. So we like those sort of systems. And that's why we use games, simulator generated data. And we can kind of create as much of it as we want, really. And just let's leave some, you know, if any computers are free in the cloud, we just run, we run some of these calculations, right? Compute cluster calculations. I like how the free compute time is used up on quantum mechanics. Yeah, quantum mechanics, exactly. Simulations and protein simulations and other things. And so, and so, you know, when you're not searching on YouTube for video, cat videos, we're using those computers usefully and quantum chemistry, the idea. I buy that. And putting them to good use. And then, yeah, and then all of that computational data that's generated, we can then try and learn the functionals from that, which of course are way more efficient once we learn the functional than running those simulations would be. Do you think one day AI may allow us to do something like basically crack open physics? So do something like travel faster than the speed of light? My ultimate aim has always been with AI is the reason I am personally working on AI for my whole life. It was to build a tool to help us understand the universe. So I wanted to, and that means physics really, and the nature of reality. So I don't think we have systems that are capable of doing that yet, but when we get towards AGI, I think that's one of the first things I think we should apply AGI to. I would like to test the limits of physics and our knowledge of physics. There's so many things we don't know. This is one thing I find fascinating about science. And, you know, as a huge proponent of the scientific method as being one of the greatest ideas humanity's ever had and allowed us to progress with our knowledge. But I think as a true scientist, I think what you find is the more you find out, the more you realize we don't know. And I always think that it's surprising that more people aren't troubled. You know, every night I think about all these things we interact with all the time that we have no idea how they work. Time, consciousness, gravity, life. I mean, these are all the fundamental things of nature. I think the way we don't really know what they are. To live life, we pin certain assumptions on them and kind of treat our assumptions as if they're fact. That allows us to sort of box them off somehow. Yeah, box them off somehow. But the reality is when you think of time, you should remind yourself, you should take it off the shelf and realize like, no, we have a bunch of assumptions. There's still a lot of, there's even not a lot of debate. There's a lot of uncertainty about exactly what is time. Is there an error of time? You know, there's a lot of fundamental questions that you can't just make assumptions about. And maybe AI allows you to not put anything on the shelf. Yeah. Not make any hard assumptions and really open it up and see what. Exactly. I think we should be truly open-minded about that. And exactly that, not be dogmatic to a particular theory. It'll also allow us to build better tools, experimental tools eventually, that can then test certain theories that may not be testable today. About things about like what we spoke about at the beginning about the computational nature of the universe, how one might, if that was true, how one might go about testing that, right. And how much, you know, there are people who've conjectured people like Scott Aaronson and others about, you know, how much information can a specific Planck unit of space and time contain, right. So one might be able to think about testing those ideas if you had AI helping you build some new exquisite experimental tools. This is what I imagine, you know, many decades from now we'll be able to do. And what kind of questions can be answered to running a simulation of them? So there's a bunch of physics simulations you can imagine that could be run in some kind of efficient way, much like you're doing in the quantum simulation work. And perhaps even the origin of life. So figuring out how going even back before the work of AlphaFold begins, of how this whole thing emerges from a rock, from a static thing. Do you think AI will allow us to, is that something you have your eye on, is trying to understand the origin of life? First of all, yourself, what do you think, how the heck did life originate on Earth? Yeah, well, maybe we'll come to that in a second, but I think the ultimate use of AI is to kind of use it to accelerate science to the maximum. So I think of it a little bit like the tree of all knowledge. If you imagine that's all the knowledge there is in the universe to attain. And we sort of barely scratched the surface of that so far, even though, you know, we've done pretty well since the enlightenment, right, as humanity. And I think AI will turbo charge all of that, like we've seen with AlphaFold. And I want to explore as much of that tree of knowledge as is possible to do. And I think that involves AI helping us with understanding or finding patterns, but also potentially designing and building new tools, experimental tools. So I think that's all, and also running simulations and learning simulations, all of that, we're already, we're sort of doing at a, you know, baby steps level here. But I can imagine that in the decades to come as, you know, what's the full flourishing of that line of thinking. It's going to be truly incredible, I would say. If I visualize this tree of knowledge, something tells me that that knowledge, tree of knowledge for humans is much smaller in the set of all possible trees of knowledge. It's actually quite small, given our cognitive limitations, limited cognitive capabilities, that even with the tools we build, we still won't be able to understand a lot of things. And that's perhaps what non-human systems might be able to reach farther, not just as tools, but in themselves understanding something that they can bring back. Yeah, it could well be. So, I mean, there's so many things that are sort of encapsulated in what you just said there. I think, first of all, there's two different things there. It's like, what do we understand today? What could the human mind understand? And what is the totality of what is there to be understood? Right? And so there's three concentric, you know, you can think of them as three larger and larger trees or exploring more branches of that tree. And I think with AI, we're going to explore that whole lot. Now, the question is, you know, if you think about what is the totality of what could be understood, there may be some fundamental physics reasons why certain things can't be understood, like what's outside a simulation or outside the universe. Maybe it's not understandable from within the universe. So there may be some hard constraints like that. You know, it could be smaller constraints, like we think of space-time as fundamental. Our human brains are really used to this idea of a three-dimensional world with time. Right. Maybe... But our tools could go beyond that. They wouldn't have that limitation necessarily. They could think in 11 dimensions, 12 dimensions, whatever is needed. But we could still maybe understand that in several different ways. The example I always give is when I, you know, play Garry Kasparov at speed chess, or we've talked about chess and these kinds of things. You know, if you're reasonably good at chess, you can't come up with the move Garry comes up with in his move, but he can explain it to you. And you can understand. And you can understand post hoc the reasoning. Yeah. So I think there's an even further level of like, well, maybe you couldn't have invented that thing, but using that, going back to using language again, perhaps you can understand and appreciate that. Same way that you can appreciate, you know, Vivaldi or Mozart or something without... You can appreciate the beauty of that without being able to construct it yourself, right? Invent the music yourself. So I think we see this in all forms of life. So it will be that times, you know, a million, but it would, you can imagine also one sign of intelligence is the ability to explain things clearly and simply, right? You know, people like Richard Feynman, another one of my old time heroes used to say that, right? If you can't, you know, if you can explain it something simply, then that's the best sign, a complex topic simply, then that's one of the best signs of you understanding it. Yeah. I can see myself talking trash in the AI system in that way. It gets frustrated how dumb I am in trying to explain something to me. I was like, well, that means you're not intelligent because if you were intelligent, you'd be able to explain it simply. Yeah. Of course, you know, there's also the other option, of course, we could enhance ourselves and with our devices, we are already sort of symbiotic with our compute devices, right? With our phones and other things. And, you know, there's stuff like Neuralink and Acceptra that could be, could advance that further. So I think there's lots of, lots of really amazing possibilities that I could foresee from here. Well, let me ask you some wild questions. So out there looking for friends, do you think there's a lot of alien civilizations out there? So I guess this also goes back to your origin of life question too, because I think that's key. My personal opinion, looking at all this and, you know, it's one of my hobbies, physics, I guess. So something I think about a lot and talk to a lot of experts on and read a lot of books on. And I think my feeling currently is that we are alone. I think that's the most likely scenario given what evidence we have. So, and the reasoning is, I think that, you know, we've tried since things like SETI program and I guess since the dawning of the space age, we've, you know, had telescopes, open radio telescopes and other things. And if you think about and try to detect signals. Now, if you think about the evolution of humans on earth, we could have easily been a million years ahead of our time now, or a million years behind, right? Easily with just some slightly different quirk thing happening hundreds of thousands of years ago. You know, things could have been slightly different if the meteor had hit the dinosaurs a million years earlier, maybe things would have evolved. We'd be a million years ahead of where we are now. So what that means is if you imagine where humanity will be in a few hundred years, let alone a million years, especially if we hopefully, you know, solve things like climate change and other things, and we continue to flourish and we build things like AI and we do space traveling and all of the stuff that humans have dreamed of forever, right? And sci-fi is talked about forever. We will be spreading across the stars, right? And Voigt Neumann famously calculated, you know, it would only take about a million years if you send out Voigt Neumann probes to the nearest, you know, the nearest other solar systems. And then all they did was build two more versions of themselves and sent those two out to the next nearest systems. You know, within a million years, I think you would have one of these probes in every system in the galaxy. So it's not actually in cosmological time, that's actually a very short amount of time. So, and you know, people like Dyson have thought about constructing Dyson spheres around stars to collect all the energy coming out of the star. You know, that there would be constructions like that would be visible across space, probably even across a galaxy. So, and then, you know, if you think about all of our radio, television emissions that have gone out since, since the, you know, 30s and 40s, imagine a million years of that. And now hundreds of civilizations doing that. When we opened our ears at the point we got technologically sophisticated enough in the space age, we should have heard a cacophony of voices. We should have joined that cacophony of voices. And what we did, we opened our ears and we heard nothing. And many people who argue that there are aliens would say, well, we haven't really done exhaustive search yet. And maybe we're looking in the wrong bands and, and we've got the wrong devices and we wouldn't notice what an alien form was like, because it'd be so different to what we're used to. But you know, I don't really buy that, that it shouldn't be as difficult as that. Like we, I think we've searched enough. There should be everywhere. If it was, it should be everywhere. We should see Dyson spheres being put up, suns blinking in and out, you know, there should be a lot of evidence for those things. And then there are other people who argue, well, the sort of safari view of like, well, we're a primitive species still because we're not space faring yet. And, and, and we, you know, there's some kind of globe, like universal rule not to interfere, you know, Star Trek rule, but like, look, look, we can't even coordinate humans to deal with climate change. And we're one species. What, what is the chance that of all of these different human civilization, you know, alien civilizations, they would have the same priorities and, and, and, and agree across the, you know, these kinds of matters. And even if that was true, and we were in some sort of safari for our own good, to me, that's not much different from the simulation hypothesis, because what does it mean the simulation hypothesis, I think in its most fundamental level, it means what we're seeing is not quite reality, right? It's something there's something more deeper underlying it, maybe computational. Now, if we were in a, if we were in a sort of safari park, and everything we were seeing was a hologram, and it was projected by the aliens or whatever, that to me is not much different than thinking we're inside of another universe, because we still can't see true reality. Right? I mean, there's, there's other explanations, it could be that the way they're communicating is just fundamentally different, that we're too dumb to understand the much better methods of communication they have. It could be, I mean, I mean, it's silly to say, but our own thoughts could be the methods by which they're communicating, like the place from which our ideas, writers talk about this, like the muse. Yeah. Like the, I mean, it sounds like very kind of wild, but it could be thoughts, it could be some interactions with our mind that we think are originating from us is actually something that is coming from other life forms elsewhere, consciousness itself might be that. It could be, but I don't see any sensible argument to the why, why would all of the alien species behave in this way? Yeah, some of them will be more primitive, they will be close to our level, you know, there should be a whole sort of normal distribution of these things, right? Some would be aggressive, some would be, you know, a curious, others would be very historical and philosophical, because, you know, maybe they're a million years older than us, but it's not, it shouldn't be like, I mean, one alien civilization might be like that, communicating thoughts and others, but I don't see why, you know, potentially the hundreds there should be, would be uniform in this way, right? It could be a violent dictatorship that the people, the alien civilizations that become successful, become, gain the ability to be destructive, an order of magnitude more destructive, but of course, the sad thought, well, either humans are very special, we took a lot of leaps that arrived at what it means to be human, there's a question there, which was the hardest, which was the most special, but also if others have reached this level, and maybe many others have reached this level, the great filter that's prevented them from going farther, to becoming a multi-planetary species, or reaching out into the stars, and those are really important questions for us, whether there's other alien civilizations out there or not, this is very useful for us to think about, if we destroy ourselves, how will we do it, and how easy is it to do? Yeah, well, you know, these are big questions, and I've thought about these a lot, but the interesting thing is that if we're alone, that's somewhat comforting from the great filter perspective, because it probably means the great filters are past us, and I'm pretty sure they are, so going back to your origin of life question, there are some incredible things that no one knows how happened, like obviously the first life form from chemical soup, that seems pretty hard, but I would guess the multicellular, I wouldn't be that surprised if we saw single cell sort of life forms elsewhere, bacteria type things, but multicellular life seems incredibly hard, that step of capturing mitochondria and then sort of using that as part of yourself, when you've just eaten it. Would you say that's the biggest, the most, like, if you had to choose one, sort of Hitchhiker's Guide to the Galaxy, one sentence summary of like, oh, those clever creatures did this, that would be the multicellular? I think that's probably the one that's the biggest. I mean, there's a great book called The Ten Great Inventions of Evolution by Nick Lane, and he speculates on ten of these, you know, what could be great filters. I think that's one. I think the advent of intelligence and conscious intelligence and in order, you know, to us to be able to do science and things like that is huge as well. I mean, it's only evolved once as far as, you know, in Earth history. So that would be a later candidate, but there's certainly for the early candidates, I think multicellular life forms is huge. By the way, what it's interesting to ask you, if you can hypothesize about what is the origin of intelligence? Is it that we started cooking meat over fire? Is it that we somehow figured out that we could be very powerful when we start collaborating? So cooperation between our ancestors so that we can overthrow the alpha male? What is it, Richard? I talked to Richard Ranham, who thinks we're all just beta males who figured out how to collaborate to defeat the dictator, the authoritarian alpha male that control the tribe. Is there other explanation? Was there a 2001 Space Odyssey type of monolith that came down to Earth? Well, I think all of those things you suggested are good candidates, fire and cooking, right? So that's clearly important for energy efficiency, cooking our meat and then being able to be more efficient about eating it and consuming the energy. I think that's huge. And then utilizing fire and tools. I think you're right about the tribal cooperation aspects and probably language as part of that. Because probably that's what allowed us to outcompete Neanderthals and perhaps less cooperative species. So that may be the case. Tool making, spears, axes. I think that let us, I mean, I think it's pretty clear now that humans were responsible for a lot of the extinctions of megafauna, especially in the Americas when humans arrived. So you can imagine once you discover tool usage, how powerful that would have been and how scary for animals. So I think all of those could have been explanations for it. The interesting thing is that it's a bit like general intelligence too, is it's very costly to begin with, to have a brain and especially a general purpose brain rather than a special purpose one. Because the amount of energy our brains use, I think it's like 20% of the body's energy. And it's massive. And when you're thinking chess, one of the funny things that we used to say is it's as much as a racing driver uses for a whole Formula One race. Just playing a game of serious high level chess, which you wouldn't think, just sitting there, because the brain's using so much energy. So in order for an animal, an organism to justify that, there has to be a huge payoff. And the problem with half a brain or half intelligence, say an IQ of like a monkey brain, it's not clear you can justify that evolutionary until you get to the human level brain. And so how do you do that jump? It's very difficult, which is why I think it's only been done once from the sort of specialised brains that you see in animals to this sort of general purpose, cheering powerful brains that humans have, which allows us to invent the modern world. And it takes a lot to cross that barrier. And I think we've seen the same with AI systems, which is that maybe until very recently, it's always been easier to craft a specific solution to a problem like chess, than it has been to build a general learning system that can potentially do many things. Because initially, that system will be way worse than less efficient than the specialised system. So one of the interesting quirks of the human mind of this evolved system is that it appears to be conscious. This thing that we don't quite understand, but it seems very, very special, its ability to have a subjective experience that it feels like something to eat a cookie, the deliciousness of it or see a colour and that kind of stuff. Do you think in order to solve intelligence, we also need to solve consciousness along the way? Do you think AGI systems need to have consciousness in order to be truly intelligent? Yeah, we thought about this a lot actually. And I think that my guess is that consciousness and intelligence are double dissociable. So you can have one without the other both ways. And I think you can see that with consciousness in that I think some animals, pets, if you have a pet dog or something like that, you can see some of the higher animals and dolphins, things like that, have self-awareness and are very sociable, seem to dream. A lot of the traits one would regard as being kind of conscious and self-aware, but yet they're not that smart. So they're not that intelligent by IQ standards or something like that. Yeah, it's also possible that our understanding of intelligence is flawed, like putting an IQ to it. Sure. Maybe the thing that a dog can do is actually go on a very far along the path of intelligence. And we humans are just able to play chess and maybe write poems. Right. But if we go back to the idea of AGI and general intelligence, dogs are very specialized, right? Most animals are pretty specialized. They can be amazing at what they do, but they're like kind of elite sports people or something, right? So they do one thing extremely well because their entire brain is optimized. They have somehow convinced the entirety of the human population to feed them and service them. So in some way they're controlling. Yes, exactly. Well, we've co-evolved to some crazy degree, right? Including the way the dogs even wag their tails and twitch their noses, right? We find it inexorably cute. But I think you can also see intelligence on the other side. So systems like artificial systems that are amazingly smart at certain things, like maybe playing Go and chess and other things, but they don't feel at all in any shape or form conscious in the way that you do to me or I do to you. And I think actually building AI, these intelligent constructs, is one of the best ways to explore the mystery of consciousness, to break it down. Because we're going to have devices that are pretty smart at certain things or capable of certain things, but potentially won't have any semblance of self-awareness or other things. And in fact, I would advocate, if there's a choice, building systems in the first place, AI systems that are not conscious to begin with, just tools, until we understand them better and the capabilities better. So on that topic, just not as the CEO of DeepMind, just as a human being, let me ask you about this one particular anecdotal evidence of the Google engineer who made a comment or believed that there's some aspect of a language model, the Lambda language model, that exhibited sentience. So you said you believe there might be a responsibility to build systems that are not sentient. And this experience of a particular engineer, I think, I'd love to get your general opinion on this kind of thing, but I think it will happen more and more and more, which not when engineers, but when people out there that don't have an engineering background start interacting with increasingly intelligent systems, we anthropomorphize them. They start to have deep, impactful interactions with us in a way that we miss them when they're gone. And we sure as heck feel like they're living entities, self-aware entities, and maybe even we project sentience onto them. So what's your thought about this particular system? Have you ever met a language model that's sentient? No. No. What do you make of the case of when you kind of feel that there's some elements of sentience to this system? Yeah, so this is an interesting question and obviously a very fundamental one. So first thing to say is I think that none of the systems we have today, I would say, even have one iota of semblance of consciousness or sentience. That's my personal feeling interacting with them every day. So I think this way premature to be discussing what that engineer talked about. I think at the moment it's more of a projection of the way our own minds work, which is to see a sort of purpose and direction in almost anything that we, you know, our brains are trained to interpret agency basically in things, even in inanimate things sometimes. And of course with a language system, because language is so fundamental to intelligence, it's going to be easy for us to anthropomorphize that. I mean, back in the day, even the first, you know, the dumbest sort of template chatbots ever, Eliza and the ilk of the original chatbots back in the 60s, fooled some people under certain circumstances, right? Pretended to be a psychologist. So just basically rabbit back to you the same question you asked it back to you. And some people believe that. So I don't think we can, this is why I think the Turing test is a little bit flawed as a formal test, because it depends on the sophistication of the judge, whether or not they are qualified to make that distinction. So I think we should talk to, you know, the top philosophers about this, people like Daniel Dennett and David Chalmers and others who've obviously thought deeply about consciousness. Of course, consciousness itself hasn't been well, there's no agreed definition. If I was to, you know, speculate about that, you know, I kind of, the working definition I like is, it's the way information feels when, you know, it gets processed. I think maybe Max Tegmark came up with that. I like that idea. I don't know if it helps us get towards any more operational thing, but I think it's a nice way of viewing it. I think we can obviously see from neuroscience certain prerequisites that are required, like self-awareness, I think is necessary, but not sufficient component. This idea of a self and other and set of coherent preferences that are coherent over time. You know, these things, maybe memory, these things are probably needed for a sentient or conscious being. But the reason, the difficult thing I think for us when we get, and I think this is a really interesting philosophical debate, is when we get closer to AGI and, you know, and much more powerful systems than we have today, how are we going to make this judgment? And one way, which is the Turing test, is sort of a behavioral judgment. Is the system exhibiting all the behaviors that a human sentient or a sentient being would exhibit? Is it answering the right questions? Is it saying the right questions? Is it saying the right things? Is it indistinguishable from a human? And so on. But I think there's a second thing that makes us as humans regard each other as sentient, right? Why do we think this? And I debated this with Daniel Dennett. And I think there's a second reason that's often overlooked, which is that we're running on the same substrate, right? So if we're exhibiting the same behavior, more or less, as humans, and we're running on the same, you know, carbon-based biological substrate, the squishy, you know, few pounds of flesh in our skulls, then the most parsimonious, I think, explanation is that you're feeling the same thing as I'm feeling, right? But we will never have that second part, the substrate equivalence, with a machine, right? So we will have to only judge based on the behavior. And I think the substrate equivalence is a critical part of why we make assumptions that we're conscious. And in fact, even with animals, high-level animals, why we think they might be, because they're exhibiting some of the behaviors we would expect from a sentient animal. And we know they're made of the same things, biological neurons. So we're going to have to come up with explanations or models of the gap between substrate differences between machines and humans to get anywhere beyond the behavioral. But to me, the practical question is very interesting and very important. When you have millions, perhaps billions of people believing that you have a sentient AI, believing what that Google engineer believed, which I just see as an obvious, very near-term future thing, certainly on the path to AGI, how does that change the world? What's the responsibility of the AI system to help those millions of people? And also, what's the ethical thing? Because you can make a lot of people happy by creating a meaningful, deep experience with a system that's faking it before it makes it. And I don't, are we the right, who is to say what's the right thing to do? Should AI always be tools? Why are we constraining AI to always be tools as opposed to friends? Yeah, I think, well, I mean, these are fantastic questions and also critical ones. And we've been thinking about this since the start of DeepMind and before that, because we planned for success and however remote that looked like back in 2010. And we've always had sort of these ethical considerations as fundamental at DeepMind. And my current thinking on the language models and large models is they're not ready. We don't understand them well enough yet. And in terms of analysis tools and guardrails, what they can and can't do and so on to deploy them at scale. Because I think, there are big still ethical questions like should an AI system always announce that it is an AI system to begin with? Probably yes. What do you do about answering those philosophical questions about the feelings people may have about AI systems perhaps incorrectly attributed? So I think there's a whole bunch of research that needs to be done first to responsibly before you can responsibly deploy these systems at scale. That will be at least be my current position. Over time, I'm very confident we'll have those tools like interpretability questions and analysis questions. And then with the ethical quandary, I think there it's important to look beyond just science. That's why I think philosophy, social sciences, even theology, other things like that come into it. Where arts and humanities, what does it mean to be human and the spirit of being human and to enhance that and the human condition. And allow us to experience things we could never experience before and improve the overall human condition and humanity overall. Get radical abundance, solve many scientific problems, solve disease. So this is the era I think, this is the amazing era I think we're heading into if we do it right. But we've got to be careful. We've already seen with things like social media how dual use technologies can be misused by firstly, by bad actors or naive actors or crazy actors. So there's that set of just the common or garden misuse of existing dual use technology. And then of course, there's an additional thing that has to be overcome with AI that eventually it may have its own agency. So it could be good or bad in of itself. So I think these questions have to be approached very carefully using the scientific method, I would say, in terms of hypothesis generation, careful control testing, not live A B testing out in the world. Because with powerful technologies like AI, if something goes wrong, it may cause a lot of harm before you can fix it. It's not like an imaging app or game app where if something goes wrong, it's relatively easy to fix and the harm is relatively small. So I think it comes with the usual cliche of like with a lot of power comes a lot of responsibility. And I think that's the case here with things like AI given the enormous opportunity in front of us. And I think we need a lot of voices and as many inputs into things like the design of the systems and the values they should have and what goals should they be put to. I think as wide a group of voices as possible beyond just the technologists is needed to input into that and to have a say in that, especially when it comes to deployment of these systems, which is when the rubber really hits the road, it really affects the general person in the street, rather than fundamental research. And that's why I say, I think as a first step, it would be better if we have the choice to build these systems as tools to give. And I'm not saying that they should never go beyond tools because of course the potential is there for it to go way beyond just tools. But I think that would be a good first step in order for us to allow us to carefully experiment and understand what these things can do. So the leap between tool to sentient entity being is one we should take very care of. Let me ask a dark personal question. So you're one of the most brilliant people in the AI community. You're also one of the most kind and if I may say, sort of loved people in the community. That said, creation of a super intelligent AI system would be one of the most powerful things in the world, tools or otherwise. And again, as the old saying goes, power corrupts and absolute power corrupts absolutely. You are likely to be one of the people, I would say probably the most likely person to be in the control of such a system. Do you think about the corrupting nature of power when you talk about these kinds of systems? As all dictators and people have caused atrocities in the past always think they're doing good, but they don't do good because the power has polluted their mind about what is good and what is evil. Do you think about this stuff or are we just focused on language model? No, I think about them all the time and I think what are the defenses against that? I think one thing is to remain very grounded and sort of humble no matter what you do or achieve. And I try to do that. My best friends are still my set of friends from my undergraduate Cambridge days. My family and friends are very important. I think trying to be a multidisciplinary person, it helps to keep you humble because no matter how good you are at one topic, someone will be better than you at that. And always relearning a new topic again from scratch or new field is very humbling. So for me that's been biology over the last five years. Huge area topic and I just love doing that, but it helps to keep you grounded and keeps you open-minded. And then the other important thing is to have a really good amazing set of people around you at your company or your organization who are also very ethical and grounded themselves and help to keep you that way. And then ultimately just to answer your question, I hope we're going to be a big part of birthing AI and that being the greatest benefit to humanity of any tool or technology ever and getting us into a world of radical abundance and curing diseases and solving many of the big challenges we have in front of us. And then ultimately help the ultimate flourishing of humanity to travel the stars and find those aliens if they are there. And if they're not there, find out why they're not there, what is going on here in the universe. This is all to come and that's what I've always dreamed about. But I think AI is too big an idea. It's not going to be, there'll be a certain set of pioneers who get there first. I hope we're in the vanguard so we can influence how that goes. And I think it matters who builds, which cultures they come from and what values they have, the builders of AI systems. Because I think even though the AI system is going to learn for itself most of its knowledge, there'll be a residue in the system of the culture and the values of the creators of that system. And there's interesting questions to discuss about that geopolitically, different cultures as we're in a more fragmented world than ever. Unfortunately, I think in terms of global cooperation, we see that in things like climate where we can't seem to get our act together globally to cooperate on these pressing matters. I hope that will change over time. Perhaps if we get to an era of radical abundance, we don't have to be so competitive anymore. Maybe we can be more cooperative if resources aren't so scarce. It's true that in terms of power corrupting and leading to destructive things, it seems that some of the atrocities of the past happen when there's a significant constraint on resources. I think that's the first thing. I don't think that's enough. I think scarcity is one thing that's led to competition, zero-sum game thinking. I would like us to all be in a positive-sum world. And I think for that, you have to remove scarcity. I don't think that's enough, unfortunately, to get world peace because there's also other corrupting things like wanting power over people and this kind of stuff, which is not necessarily satisfied by just abundance. But I think it will help. But I think ultimately, AI is not going to be run by any one person or one organisation. I think it should belong to the world, belong to humanity. And I think there'll be many ways this will happen. And ultimately, everybody should have a say in that. Do you have advice for young people in high school and college, maybe if they're interested in AI or interested in having a big impact on the world, what they should do to have a career they can be proud of or to have a life they can be proud of? I love giving talks to the next generation. What I say to them is actually two things. I think the most important things to learn about and to find out about when you're young is what are your true passions, is first of all, as two things. One is find your true passions. And I think you can do that by, the way to do that is to explore as many things as possible when you're young and you have the time and you can take those risks. I would also encourage people to look at finding the connections between things in a unique way. I think that's a really great way to find a passion. Second thing I would say, advice is know yourself. So spend a lot of time understanding how you work best, like what are the optimal times to work? What are the optimal ways that you study? How do you deal with pressure? Test yourself in various scenarios and try and improve your weaknesses, but also find out what your unique skills and strengths are and then hone those. So then that's what will be your super value in the world later on. And if you can then combine those two things and find passions that you're genuinely excited about that intersect with what your unique strong skills are, then you're onto something incredible and I think you can make a huge difference in the world. So let me ask about know yourself. This is fun. This is fun. Quick questions about day in the life, the perfect day, the perfect productive day in the life of Demesis Hobbs. Maybe these days there's a lot involved. So maybe a slightly younger Demesis Hobbs where you could focus on a single project maybe. How early do you wake up? Are you a night owl? Do you wake up early in the morning? What are some interesting habits? How many dozens of cups of coffees do you drink a day? What's the computer that you use? What's the setup? How many screens? What kind of keyboard? Are we talking Emacs Vim or are we talking something more modern? So a bunch of those questions. So maybe a day in the life. What's the perfect day involved? Well, these days it's quite different from say 10, 20 years ago. Back 10, 20 years ago it would have been a whole day of research, individual research or programming, doing some experiment, neuroscience, computer science experiment, reading lots of research papers. And then perhaps at night time, reading science fiction books or playing some games. But lots of focus, so deep focused work on whether it's programming or reading research papers. Yes, yes. So that would be lots of deep focus work. These days for the last sort of, I guess, five to 10 years, I've actually got quite a structure that works very well for me now, which is that I'm a complete night owl, always have been. So I optimize for that. So I'll basically do a normal day's work, get into work about 11 o'clock and sort of do work to about seven in the office. And I will arrange back to back meetings for the entire time of that and with as many, meet as many people as possible. So that's my collaboration management part of the day. Then I go home, spend time with the family and friends, have dinner, relax a little bit. And then I start a second day of work. I call it my second day of work around 10 PM, 11 PM. And that's the time till about the small hours of the morning, four or five in the morning where I will do my thinking and reading research, writing research papers. Sadly, I don't have time to code anymore, but it's not efficient to do that these days, given the amount of time I have. But that's when I do, maybe do the long kind of stretches of thinking and planning. And then probably, using email or other things, I would fire off a lot of things to my team to deal with the next morning. But actually thinking about this overnight, we should go for this project or arrange this meeting the next day. When you think it through a problem, are you talking about a sheet of paper with a pen? Is there some structure? Yeah, I still like pencil and paper best for working out things. But these days, it's just so efficient to read research papers just on the screen. I still often print them out, actually. I still prefer to mark out things. And I find it goes into the brain quicker, better and sticks in the brain better when you're still using physical pen and pencil and paper. So you take notes? I have lots of notes, electronic ones, and also whole stacks of notebooks that I use at home. On some of these most challenging next steps, for example, stuff none of us know about that you're working on, you're thinking, there's some deep thinking required there, right? Like, what is the right problem? What is the right approach? Because you're going to have to invest a huge amount of time for the whole team. They're going to have to pursue this thing. What's the right way to do it? Is RL going to work here? What's the right thing to try? What's the right benchmark to use? Do we need to construct a benchmark from scratch? All those kinds of things. Yes. So I think all those kinds of things in the night time phase, but also much more. I've always found the quiet hours of the morning when everyone's asleep, it's super quiet outside. I love that time. It's the golden hours, like between one and three in the morning. Put some music on, some inspiring music on, and then think these deep thoughts. So that's when I would read my philosophy books and Spinoza is my recent favourite, Kant, all these things. And I read about a great scientist of history, how they did things, how they thought things. So that's when you do all your creative, that's when I do all my creative thinking. I think people recommend you do your creative thinking in one block. The way I organise the day, that way I don't get interrupted because obviously no one else is up at those times. So I can get super deep and super into flow. The other nice thing about doing it night time wise is if I'm really onto something or I've got really deep into something, I can choose to extend it. I'll go into six in the morning, whatever, and then I'll just pay for it the next day because I'll be a bit tired and I won't be my best, but that's fine. I can decide looking at my schedule the next day and given where I'm at with this particular thought or creative idea that I'm going to pay that cost the next day. So I think that's more flexible than morning people who do that. They get up at four in the morning. They can also do those golden hours then, but then their start of their scheduled day starts at breakfast, 8am, whatever, they have their first meeting. And then it's hard. You have to reschedule a day if you're in flow. Yeah, that could be a truly special thread of thoughts that you're too passionate about. This is where some of the greatest ideas could potentially come is when you just lose yourself late into the night. And for the meetings, I mean, you're loading in really hard problems in a very short amount of time. So you have to do some kind of first principles thinking here. It's like, what's the problem? What's the state of things? What's the right next step? Yes. You have to get really good at context switching, which is one of the hardest things, because especially as we do so many things, if you include all the scientific things we do, scientific fields we're working in, these are entire complex fields in themselves. And you have to keep abreast of that. But I enjoy it. I've always been a generalist in a way. And that's actually what happened in my games career after chess. One of the reasons I stopped playing chess was because I got into computers. But also I started realizing there were many other great games out there to play too. So I've always been that way, inclined, multidisciplinary. And there's too many interesting things in the world to spend all your time just on one thing. So you mentioned Spinoza. I got to ask the big, ridiculously big question about life. What do you think is the meaning of this whole thing? Why are we humans here? You've already mentioned that perhaps the universe created us. Is that why you think we're here? To understand how the universe works? Yeah, I think my answer to that would be, and at least the life I'm living, is to gain and understand knowledge, to gain knowledge and understand the universe. That's what I think. I can't see any higher purpose than that. If you think back to the classical Greeks, the virtue of gaining knowledge, I think it's one of the few true virtues is to understand the world around us and the context and humanity better. And I think if you do that, you become more compassionate and more understanding yourself and more tolerant. And I think all these other things may flow from that. And to me, understanding the nature of reality, that is the biggest question. What is going on here is sometimes the colloquial way I say. What is really going on here? It's so mysterious. I feel like we're in some huge puzzle. The universe seems to be structured in a way. Why is it structured in a way that science is even possible? The scientific method works. Things are repeatable. It feels like it's almost structured in a way to be conducive to gaining knowledge. Why should computers be even possible? Isn't that amazing that computational or electronic devices can be possible? And they're made of sand or most common element that we have, silicon on the Earth's crust. It could be made of diamond or something. Then we would have only had one computer. So a lot of things are slightly suspicious to me. This puzzle sure as heck sounds like something we talked about earlier, what it takes to design a game that's really fun to play for prolonged periods of time. And it does seem like this puzzle, like you mentioned, the more you learn about it, the more you realize how little you know. So it humbles you but excites you by the possibility of learning more. It's one heck of a puzzle we got going on here. So like I mentioned, of all the people in the world, you're very likely to be the one who creates the AGI system that achieves human level intelligence and goes beyond it. So if you got a chance in very, well, you could be the person that goes into the room with the system and have a conversation. Maybe you only get to ask one question. If you do, what question would you ask her? I would probably ask, what is the true nature of reality? I think that's the question. I don't know if I'd understand the answer because maybe it would be 42 or something like that. But that's the question I would ask. And then there'll be a deep sigh from the systems that go, and then there'll be a deep sigh from the systems like, all right, how do I explain to this human? Exactly. All right, let me, I don't have time to explain. Maybe I'll draw you a picture. I mean, how do you even begin to answer that question? Well, I think it would- What would you think the answer could possibly look like? I think it could start looking like more fundamental explanations of physics would be the beginning. More careful specification of that, taking you, walking us through by the hand as to what one would do to maybe prove those things out. Maybe giving you glimpses of what things you totally missed in the physics of today. Exactly. Just here's glimpses of, no, like there's a much more elaborate world or a much simpler world or something. A much deeper, maybe simpler explanation of things, right? Then the standard model of physics, which we know doesn't work, but we still keep adding to. That's how I think the beginning of an explanation would look. It would start encompassing many of the mysteries that we have wondered about for thousands of years, like consciousness, dreaming, life, and gravity, all of these things. Yeah, giving us glimpses of explanations for those things. Yeah. Yeah. Well, Demis, you're one of the special human beings in this giant puzzle of ours. It's a huge honor that you would take a pause from the bigger puzzle to solve this small puzzle of a conversation with me today. It's truly an honor and a pleasure. Thank you so much. Exactly. I really enjoyed it. Thanks, Lex. Thanks for listening to this conversation with Demis Hassabis. To support this podcast, please check out our sponsors in the description. Now, let me leave you with some words from Edgar Dijkstra. Computer science is no more about computers than astronomy is about telescopes. Thank you for listening, and hope to see you next time.