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Speaker 1

00:00

The following is a conversation with Demis Hassabis,

Speaker 2

00:03

CEO and co-founder of DeepMind, a company that has published and built some of the most incredible artificial intelligence systems in the history of computing, including AlphaZero that learned all by itself to play the game of Go better than any human in the world and AlphaFold2 that solved protein folding. Both tasks considered nearly impossible for a very long time. Demos is widely considered to be 1 of the most brilliant and impactful humans in the history of artificial intelligence and science and engineering in general.

Speaker 2

00:41

This was truly an honor and a pleasure for me to finally sit down with him for this conversation, and I'm sure we will talk many times again in the future. This is the Lux Friedman Podcast. To support it, please check out our sponsors in the description. And now, dear friends, here's Demis Hassabis.

Speaker 3

01:01

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?

Speaker 4

01:11

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.

Speaker 3

01:18

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?

Speaker 4

01:27

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?

Speaker 3

01:33

This is a kind

Speaker 4

01:34

of Heisenberg uncertainty principle situation if I told you you'd behave differently yeah maybe that's what's happening with us of course.

Speaker 3

01:40

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 gonna pass? If I was such a program, would you be able to tell, do you think? So to the Turing test question, You've talked about the benchmark for solving intelligence.

Speaker 3

02:05

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.

Speaker 4

02:17

Yeah, the Turing test, of course, it's been unbelievably influential, and Turing's 1 of my all-time heroes. But I think if you look back at the 1950 Pabey's 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.

Speaker 4

02:36

You can see he didn't specify it very rigorously. For example, he didn't specify the knowledge that the expert or judge would have, how much time would they have to investigate this? So these are important parameters if you were gonna make it a true sort of formal test. And by some measures, people claim the Turing test passed several, a decade ago, I remember someone claiming that with a kind of very bog standard normal logic model because they pretended it was a kid.

Speaker 4

03:08

So the judges thought that the machine was a child. So that would be very different from an expert AI person interrogating a machine and knowing how it was built and so on. So I think we should probably move away from that as a formal test and move more towards a general test where we test the AI capabilities on a range of tasks and see if it reaches human level or above performance on maybe thousands, perhaps even millions of tasks eventually, and cover the entire sort of cognitive space. So I think for its time it was an amazing thought experiment.

Speaker 4

03:45

And also 1950s, obviously, it was barely the dawn of the computer age, so of course he only thought about text and now we have a lot more different inputs.

Speaker 3

03:54

So yeah, maybe the better thing to test is the generalizability, so across multiple tasks, but I think it's also possible as systems like God will show that eventually that might map right back to language. So you might be able to demonstrate your ability to generalize across tasks by then communicating your ability to generalize across tasks, which is kind of what we do through conversation anyway, when we jump around. Ultimately, what's in there in that conversation is not just you moving around knowledge, it's you moving around like these entirely different modalities of understanding that ultimately map to your ability

Speaker 4

04:36

to operate successfully in all of these domains, which you can think of as tasks. Yeah, I think certainly we as humans use language as our main generalization communication tool. I think we end up thinking in language and expressing our solutions in language.

Speaker 4

04:54

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

Speaker 3

05:15

Yeah, visual, robotics, body language. Yeah, action's the interactive aspect of all that. That's all part of it.

Speaker 4

05:24

But what's interesting with gato is that it's sort of pushing prediction to the maximum in terms of like, you know, mapping arbitrary sequences to other sequences and sort of just predicting what's gonna happen next. So prediction seems to be fundamental to intelligence.

Speaker 3

05:40

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

Speaker 4

05:44

Yeah, it seems like you can generalize that quite well. So obviously language models predict the next word. Gato predicts potentially any action or any token.

Speaker 4

05:53

And it's just the beginning, really. It's our most general agent, 1 could call it, so far. But 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.

Speaker 3

06:04

But the big part of solving AGI is creating benchmarks that help us get closer and closer, sort of creating benchmarks that test the journalizability. And it's just still interesting that this fella, Alan Turing, was 1 of the first and probably still 1 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.

Speaker 3

06:34

And I still think something like the Turing test will at the end of the day be the thing that truly impresses other humans. So that you can have a close friend who's in the AI system. For that friend to be a good friend, they're going to have to be able to play StarCraft and they're gonna 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.

Speaker 3

07:07

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

Speaker 4

07:17

it truly shines. I think so, because it's such an important kind of input output for us. I think you're right.

Speaker 4

07:23

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

Speaker 3

07:55

I just, I think there'll be a kind of humor in the AI systems of the future, looking back to this conversation, and thinking about the Turing test, and also thinking about, by that time, they would know which year they were finally able to sort of cross the threshold of human-level intelligence, and think how funny it is that we humans were still confused about this whole problem. That's been long ago been solved. Well, hello to the AI agents of the future.

Speaker 3

08:27

Anyway, so going back to your journey, When did you fall in love with programming first?

Speaker 4

08:33

Well, I was pretty young age actually. So, you know, I started off, actually games was my first love. So starting to play chess when I was around 4 years old and then it was actually with winnings from a chess competition that I managed to buy my first chess computer when I was about 8 years old.

Speaker 4

08:50

It was a ZX Spectrum, which was hugely popular in the UK at the time. It was an amazing machine because I think it trained a whole generation of programmers in the UK because it was so accessible. You know, you literally switched it on and there was the basic prompt and you could just get going. And, my parents didn't really know anything about computers.

Speaker 4

09:09

So, but because it was my money from a chess competition, I could, I could say I wanted to buy it. And then, you know, I just went to bookstores, got books on programming, and started typing in the programming code. And then of course, once you start doing that, you start adjusting it and then making your own games. And that's when I fell in love with computers and realized that they were a very magical device.

Speaker 4

09:34

In a way, I wouldn't have been able to explain this at the time, but I felt that they were sort of almost a magical extension of your mind. I always had this feeling, and I've always loved this about computers, that you can set them off doing something, some task for you, you can go to sleep, come back the next day, and it's solved. That feels magical to me. So I mean, all machines do that to some extent.

Speaker 4

09:55

They all enhance our natural capabilities. Obviously, cars make us allow us to move faster than we can run. But this was a machine to extend the mind. And then of course AI is the ultimate expression of what a machine may be able to do or learn.

Speaker 4

10:11

So very naturally for me, that thought extended into AI quite quickly.

Speaker 3

10:15

Do you remember the programming language that was first started? Yeah. Was it special to the machine?

Speaker 4

10:22

No, it was just a basic. I think it was just basic on the ZX Spectrum. I don't know what specific form it was.

Speaker 4

10:27

And then later on I got a Commodore Amiga, which was a fantastic machine.

Speaker 3

10:32

Now you're just showing off.

Speaker 4

10:33

So yeah, well, lots of my friends had Atari STs and I managed to get an Amiga because it was a bit more powerful and that was incredible. And used to do programming in Assembler and also AMOS basic, this specific form of basic, it was incredible actually. So I learned all my coding skills.

Speaker 3

10:50

And when did you fall in love with AI? So when did you first start to gain an understanding that you can not just write programs that do some mathematical operations for you while you sleep, but something that's akin to bringing an entity to life, sort of a thing that can figure out something more complicated than a simple mathematical operation.

Speaker 4

11:15

Yeah, so there was a few stages for me all while I was very young. So first of all, as I was trying to improve at playing chess, I was captaining various England junior chess teams and at the time when I was about, you know, maybe 10, 11 years old, I was gonna become a professional chess player. That was my first thought.

Speaker 3

11:31

So That dream was there to try to get to the highest level.

Speaker 4

11:35

Yeah so I was you know I got to when I was about 12 years old I got to master standard I was second highest rated player in the world to Judith Polger who obviously ended up being an amazing chess player and world women's champion And when I was trying to improve at chess, what you do is you obviously, first of all, you're trying to improve your own thinking processes. So that leads you to thinking about thinking. How is your brain coming up with these ideas?

Speaker 4

12:00

Why is it making mistakes? How can you improve that thought process? But the second thing is that you, it was just the beginning, this was like in the early 80s, mid 80s of chess computers. If you remember, they were physical balls like the 1 we have in front of us and you press down the squares.

Speaker 4

12:17

I think Kasparov had a branded version of it that I got. They're not as strong as they are today, but they were pretty strong and you used to practice against them to try and improve your openings and other things. I think I probably got my first 1, I was around 11 or 12. And I remember thinking, this is amazing.

Speaker 4

12:37

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 out in 1984 or something so I must have got it when I was about 11, 12 and it explained fully how these chess programs were made. I remember my first AI program being a program my Amiga. It couldn't, It wasn't powerful enough to play chess.

Speaker 4

13:02

I couldn't write a whole chess program, but I wrote a program for it to play Othello, or Reversey, 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.

Speaker 4

13:17

I was around 12 years old. So that brought me into AI. Then the second part was later on, I was around 16, 17, and I was writing games professionally, designing games, writing a game called Theme Park, which had AI as a core gameplay component as part of the simulation. And it sold 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 reacting to the way you as the player played it.

Speaker 4

13:47

So it was called a sandbox game. So it was 1 of the first types of games like that along with SimCity. And it meant that every game you played was unique.

Speaker 3

13:55

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 just as a brief tangent.

Speaker 4

14:18

Well, look, I think games have been significant in my life for 3 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.

Speaker 4

14:32

So all the games I professionally wrote had AI as a core component. And that was mostly 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.

Speaker 4

14:56

And we're still reaping the benefits of that even with things like GPUs, which I find ironic, was obviously invented for graphics, computer graphics, but then turns out to be amazingly useful for AI. It just turns out everything's a matrix multiplication, it appears in the whole world. So I think games at the time had the most cutting edge AI. A lot of the games, I was involved in writing so there was a game called Black and White which was 1 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.

Speaker 4

15:30

So in that game, you know, you trained a little pet animal. And brilliant game. Yeah. And it sort of learned from how you were treating it.

Speaker 4

15:37

So if you treated it badly, then it became mean.

Speaker 3

15:40

And

Speaker 4

15:40

then it would be mean to to your villagers and your 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.

Speaker 4

15:51

And so was I, to be honest, with the way it kind of developed. And- Especially the mapping to good and evil. Yeah.

Speaker 3

15:57

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.

Speaker 3

16:18

It's fascinating. I mean, games can do that philosophically to you, and it's rare, it seems rare.

Speaker 4

16:22

Yeah, well, games are, I think, a unique medium because you as the player, you're not just passively consuming the entertainment, right, You're actually actively involved as an agent. So I think that's what makes it in some ways can be more visceral than other mediums like films and books. So that was designing AI in games.

Speaker 4

16:42

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. That was a core component of our vision at the start of DeepMind, was that we would use games very heavily as our main testing ground, certainly to begin with, because it's super efficient to use games. 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.

Speaker 3

17:18

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.

Speaker 4

17:28

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. 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.

Speaker 4

17:43

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 test externally how strong is your system by, of course, playing against 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.

Speaker 4

18:09

I think there's a huge reason why we were so successful back in starting out 2010. How come we were able to progress so quickly because we've utilized games. 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.

Speaker 3

18:30

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

Speaker 3

19:01

It's just too, it's, you know, no matter how much Moore's law you have, compute is just never going to be able to crack the game of Go. And so then there's something compelling about facing, sort of taking on the impossibility of that task from the AI researcher perspective, engineer perspective, and then as a human being just observing this whole thing, your beliefs about what you thought was impossible being broken apart, It's humbling to realize we're not as smart as we thought. It's humbling to realize that the things we think are impossible now perhaps will be done in the future. There's something really powerful about a game, AI system beating a human being in a game that drives that message home for like millions, billions of people, especially in the case of Go.

Speaker 4

19:59

Sure, well look, I think it's a, I mean, it has been a fascinating journey and especially as I think about it from, I can understand it from both sides, both as the AI, you know, creators of the AI, but also as a games player originally. So you know, it was a really interesting, I mean, it was a fantastic, but also somewhat bittersweet moment, the AlphaGo match for me, seeing that and being obviously heavily involved in that. But, you know, As you say, chess has been the, I mean Kasparov, I think rightly called it the Drosophila of intelligence, right?

Speaker 4

20:37

So it's sort of, I love that phrase and I think he's right because chess has been hand in hand with AI from the beginning of the whole field, right? So I think every AI practitioner starting with Turing and Claude Shannon and all those, the forefathers of the field, tried their hand at writing a chess program. I've got an original edition of Claude Shannon's first chess program. I think it was 1949, the original sort of paper.

Speaker 4

21:07

And they all did that. And Turing famously wrote a chess program 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 2 or 3 days running his own program by hand with pencil and paper and playing a friend of his with his chess program.

Speaker 4

21:24

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 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.

Speaker 4

22:00

And so with the same brain. 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 a cool program. But it couldn't do anything else. Like it couldn't even play a strictly simpler game like tic-tac-toe.

Speaker 4

22:21

So something to me was missing from intelligence from that system that we would regard as intelligence. And I think it was this idea of generality and also learning. So that's what we try to do with AlphaGo.

Speaker 3

22:36

Yeah, with AlphaGo and AlphaZero, MuseZero, 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.

Speaker 3

23:02

Can you explain this?

Speaker 4

23:03

First of

Speaker 3

23:04

all, it's really interesting to think about what makes a game compelling, makes it stick across centuries.

Speaker 4

23:11

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?

Speaker 4

23:23

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 2 capabilities more or less equally. So they're both roughly worth 3 points each.

Speaker 3

23:48

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

Speaker 4

23:53

Well, maybe, I mean, it's sort of, I don't know if 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 that are 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.

Speaker 4

24:21

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.

Speaker 3

24:30

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

Speaker 4

24:41

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, 1 could say. I think if we define creativity as coming up with something original that's useful for a purpose, then I think the lowest level of creativity is like an interpolation, so an averaging of all the examples you see.

Speaker 4

25:06

So maybe a very basic AI system could say you could have that. So you show it millions of pictures of cats, and then you say, give me an average looking cat, right? Generate me an average looking cat. I would call that interpolation.

Speaker 4

25:17

Then there's extrapolation, which something like AlphaGo showed. So AlphaGo played millions of games of Go against itself, and then it came up with brilliant new ideas like move 37 in game 2, brilliant motif strategies in Go that no humans had ever thought of, even though we've played it for thousands of years and professionally for hundreds of years. So that, I call that extrapolation. But then, 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?

Speaker 4

25:47

Right, Could you invent chess? And not just come up with a brilliant chess move or a brilliant Go move, but can you actually invent chess or something as good as chess or Go? And I think 1 day, AI could, but what's missing is how would you even specify that task to a program right now? And the way I would do it if I was telling a human to do it or a games designer, a human games designer to do it is I would say something like Go.

Speaker 4

26:11

I would say come up with a game that only takes 5 minutes to learn, which Go does because it's got simple rules, but many lifetimes to master, or impossible to master in 1 lifetime because it's so deep and so complex. And then it's aesthetically beautiful, and also it can be completed in 3 or 4 hours of gameplay time, which is useful for us in a human day. And so you might specify these high level concepts like that, and then with that and maybe a few other things, 1 could imagine that Go satisfies those constraints. But The problem is that we're not able to specify abstract notions like that, high-level abstract notions like that yet, to our AI systems.

Speaker 4

26:57

I think there's still something missing there in terms of high-level concepts or abstractions that they truly understand and that are combinable and compositional. So for the moment, I think AI is capable of doing interpolation and extrapolation, but not true invention.

Speaker 3

27:14

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?

Speaker 3

27:36

And maybe if it satisfies some of those other things you mentioned in terms of quickness to learn and so on, and you could see a long journey to master for even an AI system, then you could say that this is a promising game. But it would be nice to do almost like alpha codes or programming rules. So generating rules that kind of, that automate even that part of the generation of rules.

Speaker 4

28:00

So I have thought about systems actually, I think it would be amazing for a games designer if you could have a system that takes your game, plays it tens of millions of times, maybe overnight, and then self-balances the rules better. So it tweaks the rules and maybe the equations and the parameters so that the game is more balanced, the units in the game or some of the rules could be tweaked. So it's a bit of like giving a base set and then allowing Monte Carlo tree search or something like that to sort of explore it.

Speaker 4

28:33

And I think that would be super powerful tool actually for balancing, auto balancing a game, which usually takes thousands of hours from hundreds of games, human games testers normally to balance some game like StarCraft, which is, you know, Blizzard are amazing at balancing their games, but it takes them years and years and years. So 1 could imagine at some point when this stuff becomes efficient enough to, you might be able to do that overnight.

Speaker 3

28:59

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

Speaker 4

29:09

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

Speaker 4

29:28

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.

Speaker 3

29:36

SimEarth, I haven't actually played that 1. So what is it, does it incorporate of evolution?

Speaker 4

29:40

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

Speaker 3

29:48

able to sort of zoom in, zoom out, and zoom in.

Speaker 4

29:51

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

Speaker 3

30:01

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

Speaker 4

30:04

Yes, well, so, okay, so I- We're

Speaker 3

30:06

gonna jump around from the absurdly philosophical to the technical.

Speaker 4

30:10

Sure, sure, 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 famously first proposed. I don't quite believe it in that sense.

Speaker 4

30:26

In the sense that are we in some sort of computer game or have our descendants somehow recreated Earth in the 21st century and for some kind of experimental reason. I think that, but I do think that we might be, that the best way to understand physics and the universe is from a computational perspective. So understanding it as an information universe and actually information being the most fundamental unit of reality rather than matter or energy. So a physicist would say, you know, matter or energy, you know, E equals MC squared.

Speaker 4

31:03

These are the things that are the fundamentals of the universe. I'd actually say information, which of course itself can specify energy or matter. Matter is actually just, We're just out the way our bodies and all the molecules in our body are arranged as information. So I think information may be the most fundamental way to describe the universe, and therefore, you could say we're in some sort of simulation because of that.

Speaker 4

31:29

But I'm not really a subscriber to the idea that these are sort of throwaway billions of simulations around. I think this is actually very critical and possibly unique, this simulation. That's a particular 1.

Speaker 3

31:42

Yes. And you just mean treating the universe as a computer that's processing and modifying information is a good way to solve the problems of physics, of chemistry, of biology, and perhaps of humanity and so on.

Speaker 4

31:59

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

Speaker 3

32:09

From our understanding of a universal Turing machine, from our understanding of a computer, Do you think there's something outside of the capabilities of a computer that is present in our universe? You have a disagreement with Roger Penrose about the nature of consciousness. He thinks that consciousness is more than just a computation.

Speaker 3

32:30

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

Speaker 4

32:33

Yeah, I've had many fascinating debates with Sir Roger Penrose, and obviously he's famously, and I read Emperors of the New Mind and his books, his classical books, and they were pretty influential in the 90s. He believes that there's something more, 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.

Speaker 4

32:59

We're almost like Turing's champion. So we are pushing Turing machines, or classical computation, to the limits. What are the limits of what classical computing can do? Now, and at the same time, I've also studied neuroscience to see, and that's why I did my PhD in, was to see, also to look at, you know, is there anything quantum in the brain from a neuroscience or biological perspective?

Speaker 4

33:22

And so far, I think most neuroscientists and most mainstream biologists and neuroscientists would say there's no evidence of any quantum systems or effects in the brain. As far as we can see, it can be mostly explained by classical theories. There's sort of the search from the biology side. Then at the same time, there's the raising of the water, the bar, from what classical Turing machines can do, including our new AI systems.

Speaker 4

33:52

As you alluded to earlier, I think AI, especially in the last decade plus, has been a continual story now of surprising events and surprising successes, knocking over 1 theory after another of what was thought to be impossible, from Go to protein folding and so on. I think I would be very hesitant to bet against how far the universal Turing machine and classical computation paradigm can go. And my betting would be that all of certainly what's going on in our brain can probably be mimicked or approximated on a classical machine, not requiring something metaphysical or quantum.

Speaker 3

34:38

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?

Speaker 4

35:06

Well, look, I think it's, you say it's a few, you know, of course, 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 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 efficient machine.

Speaker 4

35:32

And it's a phenomenon, basically. And I think that building AI, 1 of the reasons I want to build AI, and I've always wanted to, is I think by building an intelligent artifact like AI and then comparing it to the human mind, that will help us unlock the uniqueness and the true secrets of the mind that we've always wondered about since the dawn of history, like consciousness, dreaming, creativity, emotions. What are all these things, right? We've wondered about them since the dawn of humanity.

Speaker 4

36:04

And I think 1 of the reasons, and 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 the neuroscience tools, fMRI machines, single cell recording, all of this stuff. But we also have the ability, computers and AI, to build intelligent systems.

Speaker 4

36:31

So I think that 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 without human minds we're able to build things like computers and actually even think and investigate about these questions. I think that's also a testament to the human mind.

Speaker 3

36:52

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

Speaker 4

37:01

That's right, that's exactly it. I mean, I think that's 1, you know, 1 could say we are, maybe we're the mechanism by which the universe is going to try and understand itself.

Speaker 3

37:09

Yeah. It's beautiful. So 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 1 day the entirety of the human biology. So here's another problem that thought to be impossible to solve, which is protein folding.

Speaker 3

37:42

And AlphaFold, or specific AlphaFold2, did just that. It solved protein folding. I think it's 1 of the biggest breakthroughs, certainly in the history of structural biology, but in general and in science. Maybe from a high level, what is it and how does it work?

Speaker 2

38:04

And

Speaker 3

38:04

then we can ask some fascinating questions after.

Speaker 4

38:08

Sure, so maybe to explain it to people not familiar with protein folding is, first of all explain proteins, which is, proteins are essential to all life. Every function in your body depends on proteins. Sometimes they're called the workhorses of biology.

Speaker 4

38:23

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. They're incredible if you actually watch little videos of how they work, animations of how they work. And proteins are specified by their genetic sequence called amino acid sequence. So you can think of it as their genetic makeup.

Speaker 4

38:47

And then in the body, in nature, they fold up into a 3D structure. So you can think of it as a string of beads, and then they fold up into a ball. Now the key thing is, you want to know what that 3D structure is, because the 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.

Speaker 4

39:23

So obviously in order to do that, you need to understand the 3D structure.

Speaker 3

39:26

So the structure is mapped to the function.

Speaker 4

39:28

The structure is mapped to the function and The structure is obviously somehow specified by the amino acid sequence. And that's, in essence, the protein folding problem is, can you just from the amino acid sequence, the 1 dimensional string of letters, can you immediately computationally predict the 3D structure? And this has been a grand challenge in biology for over 50 years.