#299 – Demis Hassabis: DeepMind

Demis Hassabis is the CEO and co-founder of DeepMind. Please support this podcast by checking out our sponsors: – Mailgun: https://lexfridman.com/mailgun – InsideTracker: https://insidetracker.com/lex to get 20% off – Onnit: https://lexfridman.com/onnit to get up to 10% off – Indeed: https://indeed.com/lex to get $75 credit – Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off EPISODE LINKS: Demis’s Twitter: https://twitter.com/demishassabis DeepMind’s Twitter: https://twitter.com/DeepMind DeepMind’s Instagram: https://instagram.com/deepmind DeepMind’s Website: https://deepmind.com Plasma control paper: https://nature.com/articles/s41586-021-04301-9 Quantum simulation paper: https://science.org/doi/10.1126/science.abj6511 The Emperor’s New Mind (book): https://amzn.to/3bx03lo Life Ascending (book): https://amzn.to/3AhUP7z PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/lexfridman – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (07:17) – Turing Test (14:43) – Video games (36:18) – Simulation (38:29) – Consciousness (43:29) – AlphaFold (57:09) – Solving intelligence (1:09:28) – Open sourcing AlphaFold & MuJoCo (1:19:34) – Nuclear fusion (1:23:38) – Quantum simulation (1:26:46) – Physics (1:30:13) – Origin of life (1:34:52) – Aliens (1:42:59) – Intelligent life (1:46:08) – Conscious AI (1:59:23) – Power (2:03:53) – Advice for young people (2:11:59) – Meaning of life

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Lex Fridman?

  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 AlphaFold 2 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

• 0:44Lex Fridman?

  to finally sit down with him for this conversation, and I'm sure we will talk many times again in the future. And now, a quick few-second mention of each sponsor. Check them out in the description. It's the best way to support this podcast. We've got Mailgun for email campaigns, InsideTracker for longevity, Onnit for supplements, Indeed for hiring, and Magic Spoon for breakfast. Choose wisely, my friends. And now, on to the full ad reads. As always, no ads in the middle. I try to make these interesting, but if you skip them, please still check out our sponsors. I enjoy their stuff. Maybe you will, too. This show is brought to you by Mailgun by Cinch, an email delivery service that I've used for many, many years

• 1:26Lex Fridman?

  to have an API that allows you to programmatically send emails. If you don't know what an API is, the point is, it's a way for programs, for code, to interact with this service. You have an API for both transactional and marketing emails. Those are terms used by people much smarter about the stuff than me. But I think transactional means specific-to-the-person emails, which is what I've, I guess, used. It's a way to email certain people, to notify them about the status of whatever the heck they're doing on the website. And then there's marketing emails, which is when you send an email to a lot of people, like the same email. I guess transactional is super customized

• 2:10Lex Fridman?

  to an action that a person took. And marketing is like a push email that you send to a lot of people. And both of those are two categories of how people often use email. And so Mailgun is a service that makes it super easy for you to do that kind of thing. You can go to lexfridman.com/mailgun to learn more.

• 2:31Lex Fridman?

  This show is also brought to you by InsideTracker, a service I use to track biological data, data that comes from my body. A lot of their plans that you can set up for include blood tests. Why blood tests? Because a lot of really useful data comes from your blood. And then they use machine learning algorithms to analyze that data. So that includes blood data, DNA data, and even data from your fitness tracker to provide you a clear picture of what's going on inside your body. This is the future. Anything you decide to do in your life should be based on data from your entity, from your being. That means your biological body. Maybe one day that means from your brain as well.

• 3:14Lex Fridman?

  There'd be a brain-computer interface device like Neuralink that collects data from your brain and is able to make suggestions of what kind of supplements to take, what kind of diet changes to make. You can go to inside tracker.com/lex and for a limited time, you get special savings for being a listener of this very podcast. This episode is also brought to you by Onnit, a nutrition supplement and fitness company. They have a thing called AlphaBrain, which is a nootropic that helps you with the memory, mental speed and focus. You might know it because of Mr. Joe Rogan, which is probably the first time I heard an Onnit read.

• 3:55Lex Fridman?

  It's one of the first podcasts, actually, Joe's, that I listened to religiously. I'm a huge fan of audiobooks and podcasts in general, but there's something about the authenticity that Joe projects that immediately connected with me. And over time, his ability to be curious and apathetic, a good listener, but also be able to change his mind, keep an open mind to some crazy ideas. Anyway, I mention that because the Joe Rogan Experience introduced me to a lot of these kinds of products, like Onnit, that quickly became part of my life. I guess podcast ad reads work.

• 4:35Lex Fridman?

  Anyway, you can get a special discount on AlphaBrain if you go to lexfridman.com/onnit.

• 4:42Lex Fridman?

  This show is also brought to you by Indeed, a hiring website. I've used them as part of many hiring efforts I've done for the teams I've led in the past. They have Indeed Instant Match that gives you quality candidates whose resumes on Indeed fit your job description immediately. I've said this before, I'll say it again. There's very few things in life as important as the people you surround yourself with. I'm somebody for whom the work I've done brings a lot of meaning and joy to my life. Even when I sold shoes at Sears Shoes in the women's section, that little community that you have as you try to figure out this new skill, those people are so instrumental to your happiness and to your effectiveness,

• 5:26Lex Fridman?

  to your growth as a human being. So hiring is really, really, really important. And that's why you should use the best tools for the job. Indeed is one such tool. They have a special offer for listeners of this podcast, only available for a limited time. Check it out at indeed.com/lex.

• 5:45Lex Fridman?

  This episode is also brought to you by an oldie but a goodie. Magic Spoon, a low carb keto-friendly cereal. They were there from the beginning. I really love Magic Spoon. It brings so much joy to my heart. I don't care if this is not your thing. Please get it and make it your thing because it's freaking delicious. It has all the deliciousness of a cereal without any of the negative stuff, like all the sugar. It has zero grams of sugar, 13 to 14 grams of protein, only four kilograms of carbs, 140 calories in each serving. It's, like I said, keto-friendly. It's magic. I don't understand how it works. I don't understand how it can be so delicious, but they have a lot of flavors.

• 6:30Lex Fridman?

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• 6:52Lex Fridman?

  This is the Lex Fridman Podcast. To support it, please check out our sponsors in the description. And now, dear friends, here's Demis Hassabis.

• 7:17Lex Fridman?

  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?

• 7:27Speaker 01

  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.

• 7:34Lex Fridman?

  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?

• 7:43Speaker 01

  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?

• 7:49Lex Fridman?

  This is a kind of.

• 7:50Speaker 01

  Eisenberg uncertainty principle situation. If I told you, you'd behave differently.

• 7:54Lex Fridman?

  Yeah.

• 7:55Speaker 01

  Maybe that's what's happening with us, of course.

• 7:56Lex Fridman?

  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?

• 8:08Speaker 01

  Exactly.

• 8:09Lex Fridman?

  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

• 8:19Lex Fridman?

  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.

• 8:33Speaker 01

  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 and you can see he didn't specify it very rigorously. So for example, he didn't specify the knowledge that the expert or judge would have. Not, you know, 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. And, you know, some, by some measures, people claim the Turing test passed several, you know, a decade ago, I remember someone claiming that with a, with a kind of very bog standard,

• 9:18Speaker 01

  normal. Uh, logic model because they pretended it was a, it was a kid. So the, the judges thought that the machine, you know, was, was a, was a child. So, um, that would be very different from an expert AI person, uh, interrogating a machine and knowing how it was built and so on. So I think, um, you know, we should probably move away from that as a, as a formal test and move more towards, uh, uh, general tests 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.

• 9:57Speaker 01

  So I think, um, for its time, it was an amazing thought experiment and also nineteen fifty's, obviously it was barely the dawn of the computer age. So of course he only thought about text and now, um, we have a lot more different inputs.

• 10:10Lex Fridman?

  So yeah, maybe the better thing to test is the generalizability so across multiple tasks. But I think it's also possible as, as systems like Gato 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,

• 10:52Lex Fridman?

  to, uh, operate successfully in all of these domains, which you can think of as tasks.

• 10:58Speaker 01

  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 very powerful, uh, uh, mode in which to, uh, explain, you know, the system to explain what it's doing. But I don't think it's the only, uh, uh, modality that matters. So I think there's going to be a lot of, you know, there's, there's a lot of different ways to express, uh, capabilities, uh, other than just language.

• 11:31Lex Fridman?

  Yeah, visual.

• 11:32Speaker 01

  Yeah.

• 11:32Lex Fridman?

  Robotics, body language.

• 11:35Lex Fridman?

  Yeah, action is the interactive aspect of all that. That's all part of it.

• 11:40Speaker 01

  But what's interesting with GATTO 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.

• 11:56Lex Fridman?

  And what you're predicting doesn't so much matter.

• 12:00Speaker 01

  Yeah, it seems like you can generalize that quite well. So obviously, language models predict the next word. GATTO 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 in the middle of doing that.

• 12:20Lex Fridman?

  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 fellow, 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 an AI system. For that friend to be

• 13:03Lex Fridman?

  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 of those kinds of things. But that 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. Truly shines.

• 13:34Speaker 01

  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, that what the the kind of the the philosophy behind it, which is the idea of can can a machine mimic the behaviors of a a human? And then, and I would say wider than just language and text. Um, then, you know, in terms of actions and everything else, creativity, all these things. Then, um, if it can sort of match or exceed human cognitive capabilities, then I think we have a, you know, a true intelligence. So I thought from that perspective, you're right. I think he did formulate the right uh kind of setup.

• 14:11Lex Fridman?

  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 uh 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 and think how funny it is that we humans were still confused about this whole problem.

• 14:38Speaker 01

  Absolutely.

• 14:38Lex Fridman?

  That's been long ago been solved. Well, hello to the AI agents of the future. Anyway, so good going back to your to your journey, when did you fall in love with programming first?

• 14:49Speaker 01

  Well, it's pretty uh it's pretty young age, actually. So, um, you know, I started off uh, actually, games was my first love. So starting to play chess when I was around four years old. And then, um, 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 uh it's 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 um my parents didn't really know anything about computers. So but because it was my money from a chess competition, I could I could say I I wanted to buy it.

• 15:31Speaker 01

  Uh, and then, you know, I just went to bookstores, got books on programming and um started typing in, you know, the the programming code. And and then, of course, um, once you start doing that, you start adjusting. 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 don't, 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.

• 16:13Speaker 01

  Obviously, cars make us, allow us to move faster than we can run, but this was a machine to extend the mind. And, 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 into AI quite quickly.

• 16:31Lex Fridman?

  Do you remember the, the programming language that was first started in?

• 16:36Speaker 01

  Yeah.

• 16:37Lex Fridman?

  Special to the machine?

• 16:37Speaker 01

  No, it was just a base. It was just, 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 Mega, which was a fantastic machine.

• 16:48Lex Fridman?

  Now you're just showing off.

• 16:49Speaker 01

  So, yeah, well, lots of my friends had Atari STs and I, I managed to get Amigas. It was a bit more powerful and, and that was incredible. And used to do programming in assembler and, and also Amos Basic, this, this, this specific form of Basic. It was incredible, actually. So I learned all my coding skills.

• 17:06Lex Fridman?

  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, than a simple mathematical operation?

• 17:31Speaker 01

  Yeah. So there was a few stages for me all, 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, you know, maybe 10, 11 years old, I was gonna become a professional chess player. That was my first thought.

• 17:48Lex Fridman?

  So that dream was there to

• 17:49Speaker 01

  Sure, sure.

• 17:50Lex Fridman?

  Try to get to the highest level of chess.

• 17:51Speaker 01

  Yeah, so I was. You know, I got to, when I was about twelve years old, I got to master standard. I was second highest rated player in the world to Judith Polga, who obviously ended up being an amazing chess player and world women's champion. And when I was trying to improve at chess, well, you know, 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, how can you improve that thought process? But the second thing is that you, it was just the beginning. This is like in the, in the eight, early eighties, mid eighties of chess computers. If you remember, they were physical boards, like the one we have in front of us. And you press down the, you know, the squares. And I think Kasparov had a branded version of it that I, I got.

• 18:36Speaker 01

  And you were, you know, you used to, they're not as strong as they are today, but they were, they were pretty strong and you used to practice against them to try and improve your openings and other things. And so I remember, I think I probably got my first one, I was around eleven or twelve. And I remember thinking, this is amazing, you know, how, how has someone programmed this, this chess board to play chess? And it was very formative book I bought, which was called The Chess Computer Handbook by David Levy. It came, came out in nineteen eighty-four or something. So I must have got it when I was about eleven, twelve. And it explained fully how these chess programs were made. And I remember my first AI program being, programming my Amiga. It couldn't, it wasn't powerful enough to play chess.

• 19:18Speaker 01

  I couldn't write a whole chess program, but I, I wrote a program for it to play Othello, or Versi, it's sometimes called, I think, in the US. And so a slightly simpler game than chess, but I used all of the principles that chess programs had, alpha, beta search, all of that. And that was my first AI program. I remember that very well, I was around twelve years old. So that, that, that brought me into AI. And then the second part was later on, when I was around sixteen, seventeen, 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. And it sold, you know, millions of copies around the world and people loved the way that the AI, even though it was relatively simple by today's AI standards, was, was reacting to the way you as the player played it.

• 20:03Speaker 01

  So it was called a sandbox game. So it was one of the first types of games like that, along with SimCity. And it meant that every game you played was unique.

• 20:11Lex Fridman?

  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? And how hard of a problem is that? So a million questions that would just as a brief tangent.

• 20:34Speaker 01

  Well, look, I think games have been significant in my life for three reasons. So 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. So all the games I professionally wrote had AI as a core component. And that was mostly in the in the 90s. And the reason I was doing that in games industry was at the time, the games industry, I think, was the cutting edge of technology. So whether it was graphics with people like John Carmack and Quake and those kind of things, or AI, I think actually all the action was going on in games. And we've seen, we're still reaping the benefits of that even with things like GPUs, which I find ironic, was obviously invented for graphics, computer graphics,

• 21:19Speaker 01

  but then turns out to be amazingly useful for AI. It just turns out everything's a matrix multiplication appears in the whole world. So I think games at the time had the most cutting edge AI. And a lot of the games I was involved in writing, so 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. So in that game, you know, you trained a little pet animal.

• 21:50Lex Fridman?

  It's a brilliant game.

• 21:51Speaker 01

  Yeah, and it sort of learnt from how you were treating it. So if you treated it badly, then it became mean. And then it would be mean to your villagers and your population, the sort of the little tribe that you were running. But if you were kind to it, then it would be kind. And people were fascinated by how that worked. And so was I, to be honest with the way it kind of developed.

• 22:10Lex Fridman?

  Especially the mapping to good and evil. It made you realize, made me realize that you can sort of, in the way, in the choices you make can define where you end up. And that means. All of us are capable of the good, evil. It all matters in the different choices along the trajectory to those places that you make. It's fascinating. I mean, games can do that philosophically to you, and it's rare. It seems rare.

• 22:38Speaker 01

  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, you know, films and books. So the second, so that was, you know, designing AI in games. And 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. And that was a, that was a sort of 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,

• 23:22Speaker 01

  because it's super efficient to use games. And also, you know, 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.

• 23:34Lex Fridman?

  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, like it's already a damn good benchmark.

• 23:44Speaker 01

  Yes, it's really good for so many reasons because you've got, you've got, you've got clear measures of how good humans can be at these things.

• 23:51Lex Fridman?

  That's right.

• 23:51Speaker 01

  And in some cases, like Go, we've been playing it for thousands of years. And 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. And also at the end, it's easy to, you know, to test externally, you know, how strong is your system by, of course, playing against, you know, the world's strongest players at those games. So it's so good for so many reasons, and it's also very efficient to run potentially millions of simulations in parallel on the cloud. So 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'd utilized games.

• 24:34Speaker 01

  And, you know, at the beginning of DeepMind, we also hired some amazing game engineers who I knew from my previous lives in the games industry. And that helped to bootstrap us very quickly.

• 24:46Lex Fridman?

  And plus, it's somehow super compelling, almost at a philosophical level, of man versus machine.

• 24:54Lex Fridman?

  Over chessboard 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, it's, 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.

• 25:25Speaker 01

  Yeah.

• 25:26Lex Fridman?

  And so that 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, 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

• 26:08Lex Fridman?

  that drives that message home for like millions, billions of people, especially in the case of Go.

• 26:15Speaker 01

  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, it was a really, you know, I mean, it was a fantastic, but also somewhat bittersweet moment, the AlphaGo 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, of intelligence, right? So it's sort of, I love that phrase and I think he's right because chess has been hand

• 26:59Speaker 01

  in hand with AI from the beginning of the, 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 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 then 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.

• 27:40Speaker 01

  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.

• 28:18Lex Fridman?

  Yeah.

• 28:18Speaker 01

  And, and yet, Deep Blue, brilliant as it was at chess, it'd been hand-coded for chess and actually had distilled the knowledge of chess grandmasters into, 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.

• 28:48Lex Fridman?

  Yeah.

• 28:48Speaker 01

  So, and that's obviously what we tried to do with AlphaGo.

• 28:52Lex Fridman?

  Yeah, with AlphaGo and AlphaZero, 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?

• 29:18Speaker 01

  Mm hmm.

• 29:18Lex Fridman?

  Can you explain this? First of all, it's really interesting to think about what makes a game compelling, makes it stick across centuries.

• 29:26Speaker 01

  Mm hmm. 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 games 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.

• 30:04Lex Fridman?

  So you think that dynamics is always there and then the rest of the rules are kind of trying to stabilize the game?

• 30:09Speaker 01

  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 knights for a 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.

• 30:46Lex Fridman?

  So some kind of controlled creative tension. From an AI perspective, do you think AI systems could venture to design games that are optimally compelling to humans?

• 30:57Speaker 01

  Well, that's an interesting question. You know, 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, you know, I think the kind of lowest level of creativity is like an interpolation, so an averaging of all the examples you see. So maybe very. 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.

• 31:40Speaker 01

  Um. And then it came up with brilliant new ideas like move thirty seven in game two, brilliant motif strategies in Go. That 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 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? 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

• 32:18Speaker 01

  a program right now. And the way I would do it if if I was specifying a human to do it or or a games designer, a human games designer to do it is, I would say, something like Go, I would say.

• 32:29Speaker 01

  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. Uh. And also, uh, it can be completed in three or four hours of gameplay time, which is, you know, useful for our us, you know, in in a in a human day. And so, um, you might specify these sort of high level concepts like that. And then, you know, with that and maybe a few other things, uh, one could imagine that Go satisfies uh those those constraints. Um. But the problem is, is that we we're not able to specify abstract notions like that, high level abstract notions like that yet

• 33:11Speaker 01

  to our AI systems. Um. And I think there's still something missing there in terms of um, high level concepts or abstractions that they truly understand and, you know, combinable and and compositional. Um. So for the moment, um, I think AI is capable of doing interpolation and extrapolation, but not true invention.

• 33:30Lex Fridman?

  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.

• 34:09Lex Fridman?

  So generating rules that kind of that automate even that part of the generation of rules.

• 34:16Speaker 01

  So I have thought about systems, actually, that I think would be amazing in 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 tweaks the the rules and the maybe the equations and the and the 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 a giving a base set and then allowing a Monte Carlo tree search or something like that to sort of explore it. Right? And I think that would be super, a powerful tool, actually, for for balancing, auto-balancing a game.

• 34:55Speaker 01

  Which usually takes thousands of hours from hundreds of games, human games testers normally to to balance some, you know, a game like StarCraft. Which is, you know, 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 this stuff becomes efficient enough to, you know, you might better do that like overnight.

• 35:15Lex Fridman?

  Do you think a game that is optimal designed by an AI system would look very much like Planet Earth?

• 35:25Speaker 01

  Maybe, maybe. Certainly the sort of game I would love to make is, and I've tried, you know, in my, in my, in my games career, the games design career, you know, my first big game was designing a theme park, an amusement park. Then with games like Republic, I tried to, you know, have games where we designed whole cities and, 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-

• 35:52Lex Fridman?

  Sim Earth, I haven't actually played that one. So what is it, does it incorporate of evolution or not?

• 35:56Speaker 01

  Yeah, it has evolution and it's sort of, it tries to, it, it sort of treats it as an entire biosphere, but from quite a high level. So-

• 36:03Lex Fridman?

  It'd be nice to be able to sort of zoom in, zoom out and zoom in.

• 36:07Speaker 01

  Exactly. So obviously it couldn't do, that was in the night, I think he wrote that in the 90s. So it couldn't, you know, it wasn't, it wasn't able to do that. But that, that would be obviously the ultimate sandbox game, of course.

• 36:17Lex Fridman?

  On that topic, do you think we're living in a simulation?

• 36:20Speaker 01

  Yes. Well, so, okay, so I-

• 36:22Lex Fridman?

  We're gonna jump around from the absurdly philosophical to the-

• 36:25Speaker 01

  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, in, in that sense. So in the, in the sense that are we in some sort of computer game or have our descendants somehow recreated Earth in the, you know, 21st century and, and, and some, for some kind of experimental reason. I think that, but I do think that we, that the, that we might be, that the best way to understand physics and the universe is from a computational perspective. So

• 37:05Speaker 01

  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, you know, matter or energy, you know, E equals mc squared. These are the things that are, 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, we're just out the way our bodies and the molecules in our body are arranged, as in- 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.

• 37:45Speaker 01

  But I don't, I do, I'm not, I'm not really a subscriber to the idea that, um, you know, they, these are sort of throwaway billions of simulations around. I think this is actually very critical and possibly unique, this simulation.

• 37:57Lex Fridman?

  This particular one?

• 37:58Speaker 01

  Yes.

• 37:58Lex Fridman?

  So, but, and you, you just mean treating the universe as a computer? That's processing and modifying information is, is a good way to solve the problems of physics, of chemistry, of biology.

• 38:12Speaker 01

  Yes.

• 38:13Lex Fridman?

  And perhaps of humanity and so on.

• 38:15Speaker 01

  Yes, I think understanding physics in terms of information theory. It might be the best way to, to really understand what's going on here.

• 38:25Lex Fridman?

  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, he thinks that consciousness is more than just a computation.

• 38:46Lex Fridman?

  Do you think all of it, the whole shebang is can be, can be a computation?

• 38:49Speaker 01

  Yeah, I've had many fascinating debates with Sir Roger Penrose. And obviously he's, he's famously, and I read, you know, Empress of the New Mind and, and, um, and his books, uh, his classical books. Uh, and they were pretty influential in the, you know, in the nineties. And, um, 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,

• 39:33Speaker 01

  is there anything quantum in the brain from a neuroscience or biological perspective? And, um, 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's, it can be mostly explained by classical, uh, classical theories. So. And then so there's sort of the the the search from the biology side. And then at the same time, there's the raising of the water at the bar from what classical turing machines can do. And and and, you know, including our new AI systems. And as you alluded to earlier, um, you know, I think AI, especially in the last decade

• 40:13Speaker 01

  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 and my betting would be that all of certainly what's going on in our brain can probably be mimicked or or approximated on a on a classical machine, um, not, you know, not requiring something metaphysical or or quantum.

• 40:54Lex Fridman?

  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?

• 41:21Speaker 01

  Well, look, I think it's, um, 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 is 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, uh, incredible efficient machine and and, uh, and it's, you know, 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, um, that

• 42:02Speaker 01

  Or 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 wondered about them 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, F M R machines, single cell recording, all of this stuff. But we also have the ability, computers and A I, to build intelligent systems.

• 42:47Speaker 01

  So I think that, you know, I think it is amazing what the human mind does. And I'm kind of in awe of it, really. And I think it's amazing that with our 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.

• 43:08Lex Fridman?

  Yeah, the universe built the human mind that now is building computers that help us understand both the universe and our own human mind.

• 43:17Speaker 01

  That's right. This is 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.

• 43:25Lex Fridman?

  Yeah.

• 43:28Lex Fridman?

  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 specifically Alpha Fold two did just that. It solved protein folding. I think it's one of the biggest breakthroughs.

• 44:09Lex Fridman?

  Certainly in the history of structural biology, but in general in science.

• 44:16Lex Fridman?

  Maybe from a high level, what is it and how does it work? And then we can ask some fascinating

• 44:23Speaker 01

  Sure.

• 44:23Lex Fridman?

  questions after.

• 44:24Speaker 01

  Sure. So maybe to explain it to people not familiar with protein folding is, you know, I 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 work courses of biology. And if you look into them, and I've, you know, obviously as part of AlphaFold, I've been researching proteins and structural biology for the last few years. You know, they're amazing little bio nano machines proteins. Incredible. If you actually watch the little videos of how they work, animations of how they work. And proteins are specified by their genetic sequence called their amino acid sequence. So you can think of it as their genetic makeup. And then in the body, in nature, they, when they, when they fold up into a 3D structure.

• 45:09Speaker 01

  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 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's 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.

• 45:42Lex Fridman?

  So the structure is mapped to the function.

• 45:44Speaker 01

  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?

• 46:02Lex Fridman?

  Right.

• 46:03Speaker 01

  And this has been a grand challenge in biology for over 50 years. So I think it was first articulated by Christian Anfeensohn, 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's been described to me as equivalent to Fermat's Last theorem.

• 46:26Lex Fridman?

  Yeah.

• 46:27Speaker 01

  But for biology.

• 46:28Lex Fridman?

  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 margins, just put random things.

• 46:37Speaker 01

  Yeah, exactly.

• 46:38Lex Fridman?

  That'll take like 200 years to solve.

• 46:40Speaker 01

  Set people off for 200 years.

• 46:41Lex Fridman?

  It should be possible.

• 46:42Speaker 01

  Exactly.

• 46:43Lex Fridman?

  And just don't give any details.

• 46:45Speaker 01

  Exactly. I think everyone's 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 Furman, you know, he, he set off this whole 50 year

• 46:57Speaker 01

  field really of computational biology. 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 experimental biology is that it takes one PhD student, their entire PhD, to do one protein. And with AlphaFold 2, we were able to predict the 3D structure in a matter of seconds.

• 47:42Speaker 01

  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 proteome is like the equivalent of the human genome, but on protein space. And, and sort of revolutionize really what a structural biologist can do. Because now they don't have to worry about these painstaking experimentals, 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.

• 48:09Lex Fridman?

  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.

• 48:30Speaker 01

  Yes, I mean.

• 48:31Lex Fridman?

  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 complexes with each other in order to form high level functions. I mean, it's a weird system that they figured it out.

• 48:51Speaker 01

  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 nanomachines and, and, and actually Leventhal, who is another scientist, a contemporary of Amfinson, he, he coined this Leventhal, what became known as Leventhal's paradox, which is exactly what you're saying. He calculated roughly a protein, an average protein, which is maybe 2,000 amino acids bases long is, is, is can fold in maybe 10 to the power 300 different confirmations. So there's 10 to the power 300 different ways that protein could fold up. And yet somehow in nature,

• 49:32Speaker 01

  physics solves this, solves this in a matter of milliseconds. So proteins fold up in your body in, you know, sometimes in, in fractions of a, of a second. So physics is somehow solving that search problem.

• 49:45Lex Fridman?

  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.

• 49:53Speaker 01

  Yes.

• 49:53Lex Fridman?

  So among that huge number of possibilities.

• 49:56Speaker 01

  Yes.

• 49:57Lex Fridman?

  It figures out a way how to stably.

• 50:01Lex Fridman?

  In some cases, there might be a malfunction, so on, which leads to a lot of the disorders and stuff like that. But

• 50:06Speaker 01

  Yes.

• 50:06Lex Fridman?

  Most of the time it's a unique mapping and that unique mapping is not obvious.

• 50:10Speaker 01

  No, exactly.

• 50:11Lex Fridman?

  Which is what the problem is.

• 50:13Speaker 01

  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, one conjecture is that it's because of misfolded protein, a protein that folds in the wrong way, amyloid beta protein. So, and then because it folds in the wrong way, it gets tangled up right in your, in your neurons. So it's super important to understand both healthy functioning and, and also disease is to understand, you know, what, 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, in, in biology.

• 50:53Lex Fridman?

  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.

• 51:11Speaker 01

  Sure.

• 51:12Lex Fridman?

  The size of the dataset 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?

• 51:25Speaker 01

  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, 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

• 52:08Speaker 01

  30 different component algorithms needed to be put together to crack the protein folding. I think some of the big innovations were that, kind of, building in some hardcoded 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

• 52:49Speaker 01

  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 Alpha- 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 to Alpha Fold working. So there was actually a huge number of different innovations like that that were required to to ultimately crack the problem. Alpha Fold one, what it produced was a distogram. So a kind of a matrix of the pairwise distances between all of the molecules in the in the in the protein.

• 53:33Speaker 01

  And then there had to be a separate optimization process to create the 3D structure. And what we did for Alpha Fold two is make it truly end to end. So we went straight from the amino acid sequence of of 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 we, you know, the in the end, the system is better at learning what the constraints are than than 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.

• 54:19Speaker 01

  So so it's better to let the gradients and the learning flow all the way through the system from the endpoint, the end output you want to the inputs.

• 54:26Lex Fridman?

  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.

• 54:38Speaker 01

  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 outfits. We call them Alpha X projects, right? Is and the easiest way to see that is the evolution of Alpha Go to Alpha Zero. So Alpha Go was a learning system, but it was specifically trained to only play Go. Right? So and what we wanted to do with first version of Alpha Go is just get to world champion performance, no matter how we did it. Right? And then and then, of course, Alpha Go Zero, we we we remove the need to use human games as a starting point. Right? So it it could just. Play against itself from random starting point from the beginning. So that removed the the need for human knowledge about Go.

• 55:19Speaker 01

  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.

• 55:43Lex Fridman?

  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.

• 55:56Speaker 01

  Yeah, exactly. And learning, learning the entire structure of the environment you put in from scratch, right? And and 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.

• 56:14Lex Fridman?

  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.

• 56:22Speaker 01

  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 when we did it back in 2015, 2016. And 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. Right? So it's, 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 how and pull it apart and how it works.

• 57:02Lex Fridman?

  This is tough to ask you this question because you probably will say it's everything. But let's let's try, let's try to think to 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?

• 57:38Speaker 01

  Yeah.

• 57:39Lex Fridman?

  What else is there? How much is the human infrastructure? And like just the humans interacting in certain kinds of ways, in all the space of all those ideas? And how much is maybe like philosophy? How much? What's the key? If um, if you were to sort of look back, like if we go forward 200 years and look back, what was the key thing that solved intelligence? Is it the ideas or the engineering?

• 58:03Speaker 01

  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, so even in the last 12 years, we started DeepMind in 2010, which is hard to imagine now, because 2010 is 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 a, 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.

• 58:48Speaker 01

  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.

• 58:57Lex Fridman?

  That's true.

• 58:57Speaker 01

  And of course, in industry, we couldn't get, you know, it's difficult to get two cents together, and which is hard to imagine now as well, given it's the biggest sort of buzzword in VCs and fundraising's easy and all these kind of things today. Back in 2010, it was very difficult. And what we, 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, Jeff 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 F M R I machines and other things.

• 59:41Speaker 01

  So we could get some good hints about architectures and algorithms and sort of representations, maybe, that the brain uses. So as at a systems level, not at an 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 AI Xi, AI Xi, which Shane worked on with his supervisor Marcus Hutter, which is this 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

• 1:00:26Speaker 01

  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, like three or four, if you think from 2010 till now, huge evolutions, things like AlphaGo.

• 1:01:10Speaker 01

  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.

• 1:01:38Lex Fridman?

  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, 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's speaking of MIT, that's, that's just something not, you wouldn't bring up.

• 1:02:13Speaker 01

  No.

• 1:02:14Lex Fridman?

  Not, not, maybe you did in like forty, fifty 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.

• 1:02:35Speaker 01

  Sure.

• 1:02:35Lex Fridman?

  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.

• 1:02:52Speaker 01

  Yeah, that's right. And, and, and back in 2010, you know, our mission statement and still is today, you know, is it was, used to be solving step one, solve intelligence. Step two, use it to solve everything else.

• 1:03:03Lex Fridman?

  Yes.

• 1:03:03Speaker 01

  So if you can imagine pitching that to a VC in 2010, you know, the kind of looks we, we got, we managed to, you know, find a few kooky people to back us, but it was, it was tricky. And, and I, and it got to the point where we, we wouldn't mention it to any of our professors because they would just eye roll and think we, you know, committed career suicide and, and, and, you know, so it was, there's a lot of things that we had to do, but we always believed it. And one reason, you know, by the way, one reason we, I believe, I've always believed in reinforcement learning is that. That if you look at neuroscience, that is the way that the, you know, 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 90s, uh, where they saw this

• 1:03:46Speaker 01

  in monkeys and, uh, and as, you know, probably getting prediction error. So we, you know, again, in the limit, this is, this is what I think you can use neuroscience for is, is, you know, at any, at mathematics, you, when you're, when you're doing something as ambitious as trying to solve intelligence and you're, you're, you know, it's blue sky research, no one knows how to do it. You, you, 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, so that, that was one reason we pushed so hard on that. And that's, and just going back to your other question about organization, the other big thing that I think we innovated with at DeepMind to encourage invention and, and, uh, and innovation was the multidisciplinary organization we built and we still have today.

• 1:04:30Speaker 01

  So DeepMind originally was a confluence of the, of the most cutting edge knowledge in neuroscience with machine learning, engineering and mathematics, right? And, and gaming. Uh, and then since then, we've built that out even further. So we have philosophers here and, and, uh, by, you know, ethicists, but also other types of scientists, physicists and so on. Um, and that's what brings together, I tried to build a sort of, um, new type of Bell Labs, but in its golden era, right? Uh, uh, and, and a new expression of that, um, to try and, uh, foster this incredible sort of innovation machine. So talking about the humans in the machine, DeepMind itself is a, a learning machine with lots of amazing human minds in it, um, coming together to try and build these, uh, learning systems.

• 1:05:14Lex Fridman?

  If we return to the big ambitious dream of AlphaFold, that may be the early steps on a very long journey in, um, in biology.

• 1:05:28Lex Fridman?

  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 up from there.

• 1:05:40Speaker 01

  Yeah.

• 1:05:40Lex Fridman?

  Just simulating bigger and bigger systems that eventually simulate something like the human brain or the human body, just the big mush, the mess of, the beautiful, resilient mess of biology. Do you see that as a long-term vision?

• 1:05:55Speaker 01

  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 I hope it's evidence of what could be done with computational methods. So, you know, AlphaFold solved this huge problem 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

• 1:06:35Speaker 01

  interaction, protein ligand binding, so reacting with molecules. Then you want to 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 biologist 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,

• 1:07:14Speaker 01

  it takes 10 years roughly to go from, to go from 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 work in Silico. So in order to get to a virtual cell... We have to build up a understanding of different parts of biology and the interactions. And so, you know, 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 AlphaFold was the first proof point that this might be possible. And he's very excited. And we have some collaborations with his lab. They're just across the road, actually, from us. It's a wonderful being here in King's Cross with the Crick Institute across the road.

• 1:07:58Speaker 01

  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.

• 1:08:42Lex Fridman?

  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 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.

• 1:09:10Speaker 01

  Yes.

• 1:09:11Lex Fridman?

  You have to learn them.

• 1:09:12Speaker 01

  That's exactly it. So the biology is too complicated to figure out the rules. It's too emergent, too dynamic, say, compared to a physics system like the motion of a planet. Right? And so you have to learn the rules. And that's exactly the type of systems that we're building.

• 1:09:27Lex Fridman?

  So you mentioned you've open sourced AlphaFold and even the data involved. To me personally, also.

• 1:09:35Lex Fridman?

  Really happy and a big thank you for open sourcing Majoko, the physics simulation engine that's often used for robotics research and so on. So I think that's a pretty gangster move. So what's the, what's, I mean, this very few companies or people do that kind of thing. What's the philosophy behind that?

• 1:09:57Speaker 01

  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 the scientific community, in one case, the robotics physics community with Majoko.

• 1:10:08Lex Fridman?

  We purchased it.

• 1:10:09Speaker 01

  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. He's an 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,

• 1:10:53Speaker 01

  to give all that data away 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 been 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.

• 1:11:37Speaker 01

  So they're like little 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 doughnut ring shaped 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 AlphaFold 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 more or less simultaneously using AlphaFold 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 AlphaFold

• 1:12:19Speaker 01

  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 AlphaFold has made.

• 1:12:31Lex Fridman?

  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.

• 1:12:44Speaker 01

  Yes, it is. And to get more 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 AlphaFold 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 do commercially because that will be the best way to actually get the most resources and impact behind it. In other ways, some other projects will do nonprofit style. And also we have to consider for future things as well, safety and ethics as well, like synthetic biology. There are, you know, there is dual use and we have to think about that as well with AlphaFold. We consulted with thirty different bioethicists and other people expert in this field to make sure it was safe before we released it.

• 1:13:29Speaker 01

  So there'll be other considerations in future, but for right now, you know, I think AlphaFold is a kind of a gift from us to the scientific community.

• 1:13:36Lex Fridman?

  So I'm pretty sure that something like AlphaFold 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?

• 1:14:03Speaker 01

  It depends what type of AIs 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-

• 1:14:17Lex Fridman?

  And tweets about it, announcement of the results.

• 1:14:19Speaker 01

  Yes, tweets announcing about 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 would 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. They're, I think of, 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

• 1:15:00Speaker 01

  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.

• 1:15:42Lex Fridman?

  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 really reaching up above the shoulders of giants? Maybe it's just 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 we can't be already discovered on the internet. Maybe the Nobel prizes of the next 100 years are already all there on the internet to be discovered.

• 1:16:18Speaker 01

  They could be, they could be. I mean, I think-

• 1:16:23Speaker 01

  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 DeepMind, as I said earlier, as a sort of interdisciplinary between neuroscience ideas and AI engineering ideas, originally. And so, 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

• 1:17:06Speaker 01

  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 come, you know,

• 1:17:47Speaker 01

  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.

• 1:18:04Lex Fridman?

  Although, you're making me really think, because just the thought experiment of deeply knowing 100 Wikipedia pages.

• 1:18:13Lex Fridman?

  I don't think I can, um, I've been really impressed by Wikipedia for, for technical topics.

• 1:18:18Speaker 01

  Yeah.

• 1:18:19Lex Fridman?

  So if you know a hundred pages or a thousand pages, I don't think we can visually truly comprehend what's, what kind of intelligence that is.

• 1:18:29Speaker 01

  Yeah.

• 1:18:29Lex Fridman?

  It's a pretty powerful, if you know how to use that and integrate that information correctly.

• 1:18:33Speaker 01

  Yes.

• 1:18:34Lex Fridman?

  I think you can go really far.

• 1:18:35Speaker 01

  Yeah.

• 1:18:36Lex Fridman?

  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.

• 1:18:59Speaker 01

  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 know, you could imagine, and I want us to do some of those things in material science, like room temperature superconductors is something on my list one day that I'd like to, like, you know, 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, um, could be extremely powerful.

• 1:19:33Lex Fridman?

  So speaking of which, you have a paper on nuclear fusion, magnetic control of tachymic plasmas through deep reinforcement learning. So you, uh, 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?

• 1:19:53Speaker 01

  It's been very fun last year or two and very productive because we've been ticking off a lot of my dream projects, if you like, of things that I've collected over the years of, 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 of themselves.

• 1:20:11Lex Fridman?

  This is energy.

• 1:20:12Speaker 01

  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, material science and engineering challenges as well to 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.

• 1:20:54Speaker 01

  They have a test reactor that they were willing to let us use, which, you know, I double checked with the team. We were gonna use carefully and safely. I was impressed they managed to persuade them to let us use it. And and it's it's an amazing test reactor they have there. And they try all sorts of pretty crazy experiments on it. And the what we tend to look at is if we go into a new domain like fusion, what are all the bottleneck problems? Like thinking from first principles, you know, what are all the bottleneck problems that are still stopping fusion working today? And then we look at, you know, 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. Yes. Right?

• 1:21:34Speaker 01

  And and and then 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 was perfect. So, you know, the plasma, it's a million degrees Celsius, something like that, hotter than the sun. And there's obviously no material that can contain it. So they have to be containing these magnetic, very powerful, very superconducting magnetic fields. But the problem is plasma is pretty unstable, as you imagine, you're you're kind of holding a mini sun, mini star in a reactor. So, you know, you 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, you know, to to basically contain what it's going to do next.

• 1:22:16Speaker 01

  So it seems like a perfect problem, if you think of it, for like a reinforcement learning prediction problem. So, you know, you got a controller, you're going to move the magnetic field. And and until we came along, you know, they were they were doing with 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, they have to be hardcoded. 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 we like to to to use.

• 1:22:50Lex Fridman?

  So can AI eventually solve nuclear fusion?

• 1:22:54Speaker 01

  Well, so with this problem, and we published it in a Nature paper last year, we held the fusion, we held the plasma in a specific shapes. So actually, it's almost like carving the plasma into different shapes and hold it there for a record amount of time. So that's one of the problems of fusion sort of solved.

• 1:23:13Lex Fridman?

  So have a controller that's able to, no matter the shape.

• 1:23:17Speaker 01

  Contain it.

• 1:23:17Lex Fridman?

  Contain.

• 1:23:18Speaker 01

  Yeah, contain it and hold it in structure. And there's different shapes that are better for the energy productions called droplets and so on. 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.

• 1:23:35Lex Fridman?

  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.

• 1:23:49Speaker 01

  Yes.

• 1:23:52Lex Fridman?

  Can you explain this work and can AI model and simulate arbitrary quantum mechanical systems in the future?

• 1:23:58Speaker 01

  Yeah, so this is another problem I've had my eye on for 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 we have Schrödinger's equation, and then we have approximations to that density functional theory. These things are famous. And people try and write approximations to these to these functionals 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've tried to do is learn a simulation,

• 1:24:43Speaker 01

  learn a functional that will describe more chemistry, types of chemistry. So until now, 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 Schrödinger's equation and then allow you to describe what the electrons are doing. And all material science and material properties are governed by the electrons and how they interact.

• 1:25:17Lex Fridman?

  So have a good summarization of the simulation through the functional,

• 1:25:24Lex Fridman?

  but one that is still close to what the actual simulation would come out with. So what,

• 1:25:31Lex Fridman?

  how difficult is that task? What's involved in that task? Is it running those those complicated simulations?

• 1:25:36Speaker 01

  Yeah.

• 1:25:36Lex Fridman?

  And learning the task of mapping from the initial conditions and the parameters of the simulation, learning what the functional would be?

• 1:25:43Speaker 01

  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 dynamics 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.

• 1:26:15Lex Fridman?

  I like how the free compute time is used up on quantum mechanics.

• 1:26:18Speaker 01

  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 in quantum chemistry, the idea.

• 1:26:30Lex Fridman?

  Fine.

• 1:26:30Speaker 01

  And 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.

• 1:26:46Lex Fridman?

  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?

• 1:26:55Speaker 01

  My ultimate aim has always been with AI is the reason I am personally working on AI for my whole life is 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.

• 1:27:36Speaker 01

  But I think as a true scientist, I think what you find is the more you find out, uh, you, the more you realize we don't know. And, and, and I always think that it's surprising that more people don't 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, we can't, I mean, these are all the fundamental things of nature.

• 1:27:59Lex Fridman?

  I think the way we.

• 1:28:00Speaker 01

  We don't really know what they are.

• 1:28:03Lex Fridman?

  To live life, we uh pin certain assumptions on them and kind of treat our assumptions as if they're fact.

• 1:28:10Speaker 01

  Yeah.

• 1:28:11Lex Fridman?

  That allows us to sort of.

• 1:28:12Speaker 01

  Box them off somehow.

• 1:28:13Lex Fridman?

  Yeah, box them off somehow. But the reality is, when you think of time, you should remind yourself, you should. Put it off the uh, 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? Uh, is there an arrow of time? You know, there's, there's a lot of fun about the questions that you can't just make assumptions about and maybe. Um, AI allows you to um. Not put anything on the shelf.

• 1:28:43Speaker 01

  Yeah.

• 1:28:44Lex Fridman?

  Not make any uh hard assumptions and really open it up and see what.

• 1:28:48Speaker 01

  Exactly. I think we should be truly open minded about that and uh exactly that, not be dogmatic to a particular theory.

• 1:28:56Speaker 01

  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 as 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, and how much, uh, you know, there are people who've conjectured, people like uh Scott Aronson and others about, uh, you know, how much information can a specific. Plank unit of space and time contain, right? So one might be able to think about testing those ideas if you had um. AI helping you build some new exquisite uh uh experimental tools. This is what I imagine that, you know, many decades from now we'll be able to do.

• 1:29:40Lex Fridman?

  And what kind of questions can be answered through running a simulation of them? So there's a bunch of physics simulations you can imagine that could be run in an uh, so some kind of efficient way. Much like you're doing in the quantum simulation work.

• 1:29:57Lex Fridman?

  And perhaps even the origin of life, figuring out how going even back before the work of AlphaFold begins of how this whole thing emerges from a rock.

• 1:30:09Speaker 01

  Yes.

• 1:30:10Lex Fridman?

  From a static thing. What do you think AI will allow us to, is that something you have your eye on? It's trying to understand the origin of life. First of all, yourself, what do you think?

• 1:30:22Lex Fridman?

  How the heck did life originate on Earth?

• 1:30:24Speaker 01

  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 we've done pretty well since the Enlightenment, right? As humanity. And I think AI will turbocharge 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.

• 1:31:09Speaker 01

  So I think that's all, and also running simulations and learning simulations. All of that, we're already, we're sort of doing it 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.

• 1:31:31Lex Fridman?

  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.

• 1:32:04Speaker 01

  Yeah, it could well be. So I mean, there's so many things that that that are sort of encapsulated in what you just said there. I think first of all, there's there's two different things. There's like, what do we understand today?

• 1:32:16Lex Fridman?

  Yeah.

• 1:32:16Speaker 01

  What could the human mind understand? And what is the totality of what is there to be understood?

• 1:32:21Lex Fridman?

  Yeah.

• 1:32:21Speaker 01

  Right? And so there's three concentric, you know, you can think of them as three large and larger trees or exploring more branches of that tree. And I I think with AI, we're going to explore that whole lot. Now, the question is, 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 that's there may be some hard constraints like that, you know?

• 1:32:50Lex Fridman?

  Could be smaller constraints, like we think of space time as fundamental. Us, our human brains are really used to this idea of a three dimensional world with time.

• 1:33:00Speaker 01

  Right.

• 1:33:01Lex Fridman?

  Maybe.

• 1:33:02Speaker 01

  But our tools could go beyond that.

• 1:33:03Lex Fridman?

  Yeah.

• 1:33:03Speaker 01

  They wouldn't have that limitation necessarily. They could think in eleven dimensions, twelve 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 Gary Kasparov at speed chess, or we've talked about chess and these kind of things. You know, he, if you, if you, if you're reasonably good at chess, you can, you can't come up with the move Gary comes up with in his move, but he can explain it to you.

• 1:33:29Lex Fridman?

  And you can understand.

• 1:33:30Speaker 01

  And you can understand post hoc the reasoning.

• 1:33:32Lex Fridman?

  Yeah.

• 1:33:32Speaker 01

  So so I think there's a, there's an even further level of like, well, maybe you couldn't have invented that thing. But but using like, 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 to construct it yourself, right? Invent the music yourself. So I think we see this in all forms of life. So it'll 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 all time heroes, used to say that, right? If you can't, you know, if you can explain it something simply, then you, that's a, that's the best sign, a complex topic simply, then that's one of the best signs of you understanding it.

• 1:34:16Lex Fridman?

  Yeah.

• 1:34:16Speaker 01

  So.

• 1:34:17Lex Fridman?

  I can see myself talking trash in the AI system in that way. It's it's it gets frustrated how dumb I am and and 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.

• 1:34:30Speaker 01

  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, this stuff like Neuralink and et cetera 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.

• 1:34:51Lex Fridman?

  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?

• 1:34:59Speaker 01

  So I guess this also goes back to your origin of life question too, because I think that that's key. My personal opinion, looking at all this and, you know, it's one of my hobbies, physics, I guess. So, so I, you know, it's something I think about a lot and talk to a lot of experts on and, and, and read a lot of books on. And I think my feeling currently is that, that we are alone. I think that's the most likely scenario, given what, 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, 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.

• 1:35:43Speaker 01

  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, that, that humans have dreamed of forever, right? And sci fi is talked about forever.

• 1:36:30Speaker 01

  We will be spreading across the stars, right? And von Neumann famously calculated, you know, it would only take about a million years if you sent out von Neumann probes to the nearest, you know, the nearest other solar systems. And and then they built, all they did was build two more versions of themselves and set 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 and, you know, we, 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.

• 1:37:11Speaker 01

  So and then, you know, if you think about all of our radio, television emissions that have gone out since since the, you know, thirties and forties. 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, should have joined that cacophony of voices. And what 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.

• 1:37:54Speaker 01

  But, you know, I not, I don't really buy that, that it shouldn't be as difficult as that. Like, 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 spacefaring yet. And and and we're, you know, there's some kind of globe, like universal rule not to interfere, yeah, 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, you know, these kind of matters. And even if that was true and we were in some sort of safari.

• 1:38:39Speaker 01

  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?

• 1:39:09Lex Fridman?

  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.

• 1:39:31Speaker 01

  Yeah.

• 1:39:31Lex Fridman?

  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.

• 1:39:50Speaker 01

  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 would be close to our level. You know, there, there would, there should be a whole sort of normal distribution of these things, right? Some would be aggressive, some would be, you know, curious, others would be very stoical 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, 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?

• 1:40:26Lex Fridman?

  It could be a violent dictatorship that the people, the alien civilizations that become successful, become.

• 1:40:36Lex Fridman?

  Gain the ability to be destructive, in 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.

• 1:40:52Speaker 01

  Yeah.

• 1:40:54Lex Fridman?

  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

• 1:41:05Speaker 01

  Yeah.

• 1:41:06Lex Fridman?

  That prevented them from going farther to becoming a multiplanetary species or reaching out into the stars. And those are really important questions for us, whether, 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?

• 1:41:28Speaker 01

  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, if we're alone, that's somewhat comforting from the great filter perspective, because it probably means the great filters were past us. And I'm pretty sure they are. So that by 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 cells, single cell sort of life forms elsewhere, bacteria type things. But multicellular life seems incredibly hard, that step of, you know, capturing mitochondria and then sort of using that as part of yourself, you know, when you've just eaten it.

• 1:42:10Lex Fridman?

  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?

• 1:42:24Speaker 01

  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 10, 10 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.

• 1:42:57Lex Fridman?

  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 and we started collaborating? So cooperation between our ancestors so that we can overthrow the alpha male? What is it, Richard? I talked to Richard Randham, who thinks we're all just beta males who figured out how to collaborate to defeat the one, the dictator, the authoritarian alpha male that controlled the tribe. Is there other explanation? Was there a 2001 space odyssey type of monolith?

• 1:43:40Speaker 01

  Yeah.

• 1:43:40Lex Fridman?

  That came down to earth?

• 1:43:41Speaker 01

  Well, I think, I think all of those things you suggested are good candidates. Fire and cooking, right? So that's clearly important for energy, you know, energy efficiency.

• 1:43:51Lex Fridman?

  Yeah.

• 1:43:52Speaker 01

  Cooking our meat and then, and then being able to, to, to be more efficient about eating it and getting, 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 is part of that.

• 1:44:06Lex Fridman?

  Yes.

• 1:44:07Speaker 01

  Because probably that's what allowed us to outcompete Neanderthals and perhaps less cooperative species. So, 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. You know, 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. Cause the amount of energy our brains use, I think it's like 20% of the body's energy. And it's, it's massive.

• 1:44:52Speaker 01

  And when you're thinking chess, one of the funny things that we, we used to say is it's as much as a racing driver uses for a whole, you know, Formula One race, just playing a game of, you know, serious high level chess, which you, we know, 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 with half a brain or half, you know, intelligence, say, and IQs of, you know, uh, uh, uh, of like a monkey brain. It's it's not clear you can justify that evolutionary until you get to the human level brain. And so, but how do you, 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 specialized

• 1:45:34Speaker 01

  brains that you see in animals to this sort of general purpose, churing powerful brains that humans have. Um, and which allows us to invent the modern, modern world. Um, and, uh, you know, it takes a lot to, to cross that barrier. And I think we've seen the same with AI systems, which is that, uh, 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 could potentially do many things. Cause initially, uh, that system will be way worse than, uh, less efficient than the specialized system.

• 1:46:08Lex Fridman?

  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, um, very special is ability to have a subjective experience that it feels like something, uh, to eat a cookie, the deliciousness of it or see a color 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?

• 1:46:43Speaker 01

  Yeah, we thought about this a lot, actually. And, um, I think that my, 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 and pets, if you have a pet dog or something like that, you can see some of the higher animals and dolphins, things like that are, uh, have self-awareness and, uh, a very sociable, um, seem to dream. Um, you know, those kinds of, a lot of the traits one would regard as being kind of conscious and self-aware. Um, and but yet they're not that smart, right? Uh, so they're not that intelligent by, by, say, IQ standards or something like that.

• 1:47:24Lex Fridman?

  Yeah. It's also possible that our understanding of intelligence is flawed, like putting an IQ to it.

• 1:47:30Speaker 01

  Sure.

• 1:47:30Lex Fridman?

  Maybe the thing that a dog can do. Is actually gone very far along the path of intelligence and we humans are just able to play chess and maybe write poems.

• 1:47:40Speaker 01

  Right, but if we go back to the idea of AGI and general intelligence, you know, dogs are very specialized, right? Most animals are pretty specialized. They can be amazing in what they do, but they're like kind of elite sports, sports people or something, right? So they do one thing extremely well because their entire brain is optimized.

• 1:47:56Lex Fridman?

  They have somehow convinced the entirety of the human population to feed them and service them. So in some way, they're controlling.

• 1:48:02Speaker 01

  Yes, exactly. Well, we co-evolved to some crazy degree, right? Including the way the dogs, you know, even even wag their tails and twitch their noses, right? We find we finding inexorably cute.

• 1:48:13Lex Fridman?

  Yeah.

• 1:48:14Speaker 01

  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 know, you do to to me or I do to you. And and I think actually, building AI is 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

• 1:48:55Speaker 01

  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, are just tools until we understand them better and the capabilities better.

• 1:49:09Lex Fridman?

  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 is 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 when people out there that don't have an engineer background start interacting with increasingly intelligent

• 1:49:51Lex Fridman?

  systems, we anthropomorphize them, they they start to have deep impactful.

• 1:49:59Lex Fridman?

  Interactions with us in a way that we miss them.

• 1:50:02Speaker 01

  Yeah.

• 1:50:02Lex Fridman?

  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? Was is, have you ever met a language model that's sentient?

• 1:50:20Speaker 01

  No, no.

• 1:50:22Lex Fridman?

  What do you make of the case of when you kind of feel that there's some elements of sentience to this system?

• 1:50:28Speaker 01

  Yeah, so this is, you know, 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 that's 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 inanimate things sometimes. And of course, with a language system, because language is so fundamental to intelligence,

• 1:51:13Speaker 01

  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? It pretended to be a psychologist, so it would just basically rabbit back to you the same question you asked it back to you.

• 1:51:35Speaker 01

  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 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 speculate about that, the working definition I like is it's the way information feels when it gets processed. I think maybe Max Tegmark came up with that. I like that idea.

• 1:52:17Speaker 01

  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. These things are 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 much more powerful systems than we have today, how are we going to make this judgment?

• 1:53:00Speaker 01

  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 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 carbon-based biological substrate, the squishy few pounds of flesh in our skulls,

• 1:53:45Speaker 01

  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.

• 1:54:14Lex Fridman?

  So we're gonna 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?

• 1:54:55Lex Fridman?

  Because you can, 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.

• 1:55:08Speaker 01

  Yeah.

• 1:55:09Lex Fridman?

  And I don't, are we the right, or who is to say what's the right thing to do? Should AI always be tools? Like, why? Why are we constraining AI to always be tools as opposed to friends?

• 1:55:23Speaker 01

  Yeah. I think, well, I mean, these are, you know, fantastic questions and also critical ones. And we've been thinking about this since the start of DeepMind and before that, because we plan for success and, you know, 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 is, and large models, is they're not ready, we don't understand them well enough yet. And, you know, 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, you know, there are big still ethical questions, like, should an AI system always announce that it is an AI system to begin with?

• 1:56:06Speaker 01

  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 know, 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, you know, 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.

• 1:56:50Speaker 01

  Where, you know, arts and humanities, what does it mean to be human and the spirit of being human? And to enhance that and the human condition, right? And allow us to experience things we could never experience before and improve the overall human condition and humanity overall, you know, 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, right? So there's that set of just the common or garden misuse of existing dual-use technology.

• 1:57:33Speaker 01

  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 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 dual technologies like AI, if something goes wrong, it may cause, you know, a lot of harm before you can fix it. It's not like a, you know, an imaging app or game app where, you know, if something goes wrong, it's relatively easy to fix and the harm's relatively small.

• 1:58:13Speaker 01

  So I think it comes with, you know, 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.

• 1:58:55Speaker 01

  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 it should never, 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, you know, allow us to carefully experiment, understand what these things can do.

• 1:59:16Lex Fridman?

  So the leap between tool to sentient entity being is one we should take very careful.

• 1:59:23Speaker 01

  Yes.

• 1:59:24Lex Fridman?

  Let me ask a dark, personal question. So you're one of the most brilliant people in the AI community, 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.

• 2:00:09Lex Fridman?

  Do you think about the corrupting nature of power when you talk about these kinds of systems that as all dictators and people have caused atrocities in the past, always think they're doing good.

• 2:00:24Lex Fridman?

  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?

• 2:00:32Speaker 01

  No, I think about them all the time and, you know, 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. I might, you know, my best friends are still my set of friends from my undergraduate Cambridge days. My family's, you know, and friends are very important.

• 2:00:55Speaker 01

  I've always, 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 relearn. Learning a new topic again from scratch is or new field is very humbling, right? So for me, that's been biology over the last five years. You know, huge area topic and and and it's been and I just love doing that, but it helps to keep you grounded like and keeps you open minded. Um, and and then the other important thing is to have a really good amazing set of uh people around you at your company or your organization who are also very ethical and grounded themselves and and help to keep you that way. Uh, and then ultimately, just to answer your question, I hope we're going to be a big part of

• 2:01:36Speaker 01

  of birthing AI and that being the greatest benefit to humanity of any tool or or technology ever. And and getting us into a world of radical abundance and curing diseases and and and and solving many of the big challenges we have in front of us and then ultimately, you know, 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 what is going on here in the universe. Um, this is all to come and and and that's what I've always dreamed about. But I don't think I think AI is too big an idea. It's not going to be uh, there'll be a certain set of pioneers who get there first. I hope to we're in the vanguard so we can influence how that goes. And I think it matters who builds, uh, who which which cultures they come from and what values they have.

• 2:02:22Speaker 01

  Uh, 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 the system. Um, and there's interesting questions to to discuss about that geopolitically. You know, different cultures, we're in a more fragmented world than ever. Unfortunately, I think in terms of global cooperation, uh, we see that in things like climate where we can't seem to get our act together uh globally to cooperate on these pressing matters. I hope that will change over time, perhaps, you know, if we get to an era of radical abundance, we don't have to be so competitive anymore. Maybe we can be more cooperative, uh, if resources aren't so scarce.

• 2:03:00Lex Fridman?

  It's true that in in terms of uh power corrupting and leading to destructive things. It seems that some of the atrocities of the past happen when there's a significant uh constraint on resources.

• 2:03:12Speaker 01

  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, you know, sort of 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 by just abundance. But I think it will help. Um, and I think, uh, but I think ultimately AI is not going to be run by any one person or one organization. I think it should belong to the world, belong to humanity. Um, and I think maybe many, there'll be many ways this will happen and ultimately, um, everybody should have a say in that.

• 2:03:53Lex Fridman?

  Do you have advice for, uh, young people in high school and college? Maybe, um, if they're interested in AI or interested in having a 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.

• 2:04:10Speaker 01

  I love giving talks to the next generation. What I say to them is actually two things. Uh, I think the most important things to learn about, uh, and to find out about when you're, when you're young is what are your true passions is first of all, there's 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, you have the time and you, and you can take those risks. Um, I would also encourage people to look at the, finding the connections between things, uh, in a unique way. I think that's a really great way to find a passion. Second thing I would say advise 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?

• 2:04:55Speaker 01

  Um, what are your, how do you deal with pressure? Um, sort of test yourself in various scenarios and, um, try and improve your weaknesses. Um, but also find out what your unique skills and strengths are and then hone those. So then that's what you 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, uh, excited about that intersect with what your unique, strong skills are, then you're, you know, you're on to something incredible and, and, you know, I think you can make a huge difference in the world.

• 2:05:26Lex Fridman?

  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 life of Demis's house.

• 2:05:36Speaker 01

  Yeah.

• 2:05:37Lex Fridman?

  Maybe, uh, maybe these days you're, um, there's a lot involved.

• 2:05:42Speaker 01

  Yeah.

• 2:05:42Lex Fridman?

  Maybe a slightly younger Demis, where you could focus on a single project, maybe.

• 2:05:49Lex Fridman?

  How early do you wake up? Are you 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, um, that you use? What's the setup? How many screens? What kind of keyboard? Are we talking, uh, Emacs Vim? Are we talking something more modern? So there's a bunch of those questions. So maybe, uh, day in the life.

• 2:06:12Speaker 01

  Yeah.

• 2:06:13Lex Fridman?

  What, what's the perfect day involved?

• 2:06:14Speaker 01

  Well, these days it's, it's quite different from, say, ten, twenty years ago. Back ten, twenty years ago, it would have been, you know, a whole day of, um, uh, research, individual research or programming, doing some experiment, neuroscience, computer science experiment, reading lots of research papers, uh, and then perhaps at nighttime, you know, um, reading science fiction books or, or, uh, playing, uh, some games.

• 2:06:41Lex Fridman?

  But lots of focus, so like deep focused work on whether it's, uh, programming or reading research papers.

• 2:06:47Speaker 01

  Yes. Yes. So that would be lots of deep, you know, uh, focus work. These days, for the last sort of, I guess, you know, five to ten years, I've actually got quite a structure that works very well for me now, which is that, um, I'm a night, complete night owl, always have been. So I optimize for that. So, you know, I, you know, I basically do a normal day's work, get into work about eleven o'clock and sort of do work till about seven, uh, in the office, uh, 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, uh, spend time with the family and friends, uh, have dinner, uh, relax a little bit.

• 2:07:29Speaker 01

  And then I start a second day of work. I call it my second day of work around ten PM, eleven PM. And that's the time till about the small hours. The morning, four, five in the morning, where I will do my thinking and reading of research, writing research papers. Sadly, don't have time to code anymore, but it's it's not efficient to to do that these days, given the amount of time I I have. But that's when I do, you know, maybe do the long kind of stretches of of thinking and planning. And then probably, you know, using using email, other things, I would set, I would fire off a lot of things to my team to 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.

• 2:08:10Lex Fridman?

  When you think through a problem, are you talking about a sheet of paper with a pen? Is there some

• 2:08:14Speaker 01

  It depends.

• 2:08:15Lex Fridman?

  Structured process?

• 2:08:16Speaker 01

  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 quick, better and sticks in the brain better when you're you're still using physical pen and pencil and paper.

• 2:08:35Lex Fridman?

  So you take notes with the

• 2:08:36Speaker 01

  I have lots of notes, electronic ones, and also whole stacks of notebooks that that that I use at home. Yeah.

• 2:08:43Lex Fridman?

  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 what is the right problem? What is the right approach? Because there 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 or not?

• 2:09:05Speaker 01

  Yes.

• 2:09:07Lex Fridman?

  What's the right thing to try? What's the right benchmark to you?

• 2:09:10Speaker 01

  Yeah.

• 2:09:11Lex Fridman?

  Do we need to construct a benchmark from scratch? All those kinds of things.

• 2:09:14Speaker 01

  Yes. So I think of all those kind of things in the nighttime phase, but also much more. I find 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 like 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, you know, my philosophy books and Spinoza's, my, you know, recent favorite, Kant, all these things. And I, you know, 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.

• 2:09:55Speaker 01

  And it's good, I think, I think people recommend, you know, you do your, your, your sort of creative thinking in one block. And the way I organize the day, that way I don't get interrupted because obviously no one else is up at those times. So I can, I can go, you know, it's, I can sort of get super deep and super into flow. The other nice thing about doing it nighttime wise is if I'm really onto something or I've, I've got really deep into something. I can choose to extend it and I'll go into six in the morning, whatever. And then I'll just pay for it the next day.

• 2:10:26Lex Fridman?

  Yeah.

• 2:10:26Speaker 01

  So 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. But I'm going to pay that cost the next day. So, so I think that's, that's more flexible than morning people who do that. You know, 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, you know, A A M, whatever they have their first meeting. And then it's hard, you have to reschedule a day if you're in flow.

• 2:10:54Lex Fridman?

  Yeah, that could be a true special thread of thoughts that the, you're too passionate about you. 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?

• 2:11:18Speaker 01

  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, you know, complex fields in themselves and you, you, you have to sort of keep up to, abreast of that. But I enjoy it. I've always been a, a, a sort of generalist in a way. And that's actually what happened with my games career after chess. I, I, I, I, 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, in the world to spend all your time just on one thing.

• 2:11:57Lex Fridman?

  So you mentioned Spinoza got asked the big ridiculously big question about life. What do you think is the meaning of this whole thing? Uh, why are we humans here? You've already mentioned that perhaps the universe created us.

• 2:12:11Speaker 01

  Hmm.

• 2:12:12Lex Fridman?

  Is that why you think we're here? To to understand how the universe.

• 2:12:16Speaker 01

  Yeah, I think my answer to that would be, and at least the the life I'm living is to gain and uh to gain and understand the knowledge, you know, 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, you know, the virtue of gaining knowledge. It's uh, I think it's the it's one of the few true virtues is to understand um the world around us and the context and humanity better. And um, and I think if you do that, you become more compassionate and more understanding yourself and and more tolerant and all these, I think all these other things may flow from that. And to me, you know, 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? Uh, it's so mysterious.

• 2:13:00Speaker 01

  I feel like we're in some huge puzzle. And and it's, but the world is also seems to be, the universe seems to be structured in a way. You know, why is it structured in a way that science is even possible? That, you know, methods, the scientific method works, things are repeatable. Um, it feels like it's almost structured in a way to be conducive to gaining knowledge. So I feel like, and you know, why should computers be even possible? Isn't that amazing that uh computational electronic devices can can can can be possible? And they're made of sand, our most, you know, common element that we have, you know, silicon that we on the on the earth's crust. It could be made of diamond or something, then we would have only had one computer.

• 2:13:38Lex Fridman?

  Yeah.

• 2:13:39Speaker 01

  Right? So it's a lot of things are kind of slightly suspicious to me.

• 2:13:42Lex Fridman?

  It sure as heck sounds, this puzzle sure as heck sounds like something we talked about earlier. What it takes to 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, 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

• 2:14:26Lex Fridman?

  system and have a conversation. Maybe you only get to ask one question. If you do, what question would you ask her?

• 2:14:35Speaker 01

  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.

• 2:14:48Lex Fridman?

  And then there'll be a deep sigh from the system. It's like, all right, how do I explain to this human?

• 2:14:52Speaker 01

  Exactly.

• 2:14:53Lex Fridman?

  All right, let me, I don't have the time to explain. Maybe I'll draw you a picture. That it is, I mean, how do you even begin to answer that question?

• 2:15:07Speaker 01

  Well, I think it would.

• 2:15:08Lex Fridman?

  What would you, what would you think the answer could possibly look like?

• 2:15:11Speaker 01

  I think it could, it could start looking like more fundamental explanations of physics would be the beginning, you know, more careful specification of that, taking, walking us through by the hand as to what one would do to maybe prove those things out.

• 2:15:26Lex Fridman?

  Maybe giving you glimpses of what things you totally missed in the physics of today.

• 2:15:32Speaker 01

  Exactly.

• 2:15:32Lex Fridman?

  Just here, here's glimpses of, no, like, there's a much, a much more elaborate world or a much simpler world or something.

• 2:15:42Speaker 01

  A much deeper, maybe simpler explanation of things, right, than the standard model of physics, which we know doesn't work, but we still keep adding to. So, and, and that's how I think the beginning of an explanation would look. And it would start encompassing many of the mysteries that we have wondered about for thousands of years, like, you know, consciousness, dreaming, life and gravity, all of these things.

• 2:16:04Lex Fridman?

  Yeah, giving us glimpses of explanations for those things. Yeah. Well, Davis, you're one of the special. Human beings in this giant puzzle of ours. And 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.

• 2:16:22Speaker 01

  Glad to have you. I really enjoyed it. Thanks, Lex.

• 2:16:25Lex Fridman?

  Thanks for listening to this conversation with Damas Hassabis. To support this podcast, please check out our sponsors in the description. And now, let me leave you with some words from Edsker Dykstra. "Computer science is no more about computers than astronomy is about telescopes." Thank you for listening and hope to see you next time.