Demis Hassabis on AI, Game Theory, Multimodality, and the Nature of Creativity | Possible

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Speaker 16 Hi, everyone. This is Pivot from New York Magazine and the Vox Media Podcast Network.

Speaker 16 I'm Kara Swisher, and today we're sharing an episode of Possible, hosted by one of our recent guests, Reid Hoffman.

Speaker 16 Join Reed and his co-host, Arya Finger, as they sit down with the co-founder and CEO of Google DeepMind Demis Hasevis, one of the most influential figures in AI.

Speaker 16 They'll dive into game theory, medicine, multi-modality, the nature of innovation, and how board games and video games shape our understanding of the future of AI.

Speaker 16 Enjoy the episode, and remember, you can find it and subscribe to Possible wherever you listen to podcasts.

Speaker 17 AI is going to affect the whole world. It's going to affect every industry.
It's going to affect every country. It's going to be the most transformative technology ever, in my opinion.

Speaker 17 So if that's true and it's going to be like electricity or fire, then I think it's important that the whole world participates in its design.

Speaker 17 I think it's important that it's not just 100 square miles of patch of California.

Speaker 17 I do actually think it's important that we get these other inputs, the broader inputs, not just geographically, but also different subjects, philosophy, social sciences, economists, not just the tech companies, not just the scientists involved in deciding how this gets built and what it gets used for.

Speaker 15 Hi, I'm Reid Hoffman.

Speaker 18 And I'm Aria Finger.

Speaker 15 We want to know how, together, we can use technology like AI to help us shape the best possible future.

Speaker 18 With support from Stripe, we ask technologists, ambitious builders, and deep thinkers to help us sketch out the brightest version of the future. And we learn what it'll take to get there.

Speaker 15 This is possible.

Speaker 15 In the 13th century, Sir Galahad embarked on a treacherous journey in pursuit of the elusive Holy Grail.

Speaker 15 The grail, known in Christian lore as the cup Christ used in the Last Supper, had disappeared from King Arthur's table. The knights of the round table swore to find it.

Speaker 15 After many trials, Galahad's pure heart allowed him the unique ability to look into the grail and observe divine mysteries that could not be described by the human tongue.

Speaker 18 In 2020, a team of researchers at DeepMind successfully created a model called AlphaFold that could predict how proteins will fold. This model helped answer one of the holy grail questions of biology.

Speaker 18 How does a long line of amino acids configure itself into a 3D structure that becomes the building block of life itself?

Speaker 18 In October 2024, three scientists involved with AlphaFold won a Nobel Prize for these efforts. This is just one of the striking achievements spearheaded by our guest today.

Speaker 15 Demis Hassabas is a British artificial intelligence researcher, co-founder, and CEO of the AI company DeepMind.

Speaker 15 Under his leadership, DeepMind developed AlphaGo, the first AI to defeat a human world champion In Go, and later created AlphaFold, which solved the 50-year protein-folding problem.

Speaker 15 He is considered one of the most influential figures in AI.

Speaker 18 Reed and I sat down for an interview with Demis, in which we talked about everything from game theory to medicine to multimodality and the nature of innovation and creativity.

Speaker 15 Here's our conversation with Demis Asabas.

Speaker 15 Demis, welcome to Possible. It was awesome dining with you at Queen's.
It was kind of a special moment in all kinds of ways.

Speaker 15 And, you know, I think I'm going to start with a question that kind of came from your Babbage theater lecture and also from the fireside chat that you did with Mohamed El-Aryan, which is share with us the moment where you went from thinking chess is the kind of the thing that I have spent my childhood doing to what I want to do is start thinking about thinking.

Speaker 15 I want to accelerate the process of thinking and that computers are a way to do that. And how did you arrive at that? What age were you?

Speaker 15 What was that turn into

Speaker 15 metacognition?

Speaker 17 Well, yeah, well, first of all, thanks for having me on the podcast. Chess for me was where it all started actually in gaming.

Speaker 17 And I started playing chess when I was four, very seriously, all through my childhood, playing for most of the England junior teams, captaining a lot of the teams.

Speaker 17 And for a long while, I was going to, my main, you know, aim was to become a professional chess player, you know, grandmaster, maybe one day, possibly a world champion.

Speaker 17 And that was my whole childhood, really.

Speaker 17 Every spare moment, not at school, I was playing, going around the world playing chess, you know, against adults in international tournaments.

Speaker 17 And then around 11 years old, I sort of had an epiphany, really,

Speaker 17 that although I love chess and I still love chess today, is it really

Speaker 17 something that one should spend your entire life on? Is it the best use of my mind? So that was one thing that was troubling me a little bit.

Speaker 17 But then the other thing was, as we were going to training camps with the England chess team, you know, we started to use early chess computers and to try and improve improve your chess.

Speaker 17 And I remember thinking that, you know, of course, we were supposed to be focusing on improving the chess openings and chess theory and tactics, but actually I was more fascinated by the fact that someone had programmed this inanimate lump of plastic to play very good chess against me.

Speaker 17 And I was fascinated by how that was done. And I really wanted to understand that and then eventually try and make my own chess programs.

Speaker 18 I mean, it's so funny. I was saying to Reed before this, my seven-year-old school just won the New York State chess championship.
So they have a long way to go before they get to you.

Speaker 18 But he takes it on faith, like, oh, yeah, mom, I'm just going to go play chess kid on the computer.

Speaker 18 Like, I'll go play against the computer a few games, which, of course, was sort of a revelation sort of decades ago.

Speaker 18 And I remember, you know, when I was in middle school, it was obviously the deep blue versus Gary Kasparov. And this was like a man versus machine moment.

Speaker 18 And one thing that you've gestured at about this moment is that it illustrated, like in this case, based on grandmaster data, it was like brute force versus like a self-learning system.

Speaker 18 Can you say more about that dichotomy?

Speaker 17 Yeah, well, look, first of all, I mean, it's great. Your son's playing chess.
And I think it's fantastic. I'm a big advocate for teaching chess in schools as a part of the curriculum.

Speaker 17 I think it's fantastic training for the mind, just like doing maths or programming would be.

Speaker 17 And it's certainly affected the way, you know, the way I approach problems and problem solve and visualize solutions and plan, you know, teaches you all these amazing meta skills, dealing with pressure.

Speaker 17 So you sort of learn all of that as a young kid, which is fantastic for anything else you're going to do.

Speaker 17 And as, you know, as far as deep blue goes, you're right.

Speaker 17 Most of these early chess programs and then deep blue became the pinnacle of that were these types of expert systems, which at the time was the favored way of approaching AI, where actually it's the programmers that solve the problem, in this case, playing chess, and then they encapsulate that solution in a set of heuristics and rules, which guides guides a kind of brute force search towards you know in this case making a good chess move and and i always had this although i was fascinated by these early chess programs that they could do that i was also slightly disappointed by them and actually by the time it got to deep blue i was already studying at cambridge in my undergrad i was actually more impressed with kasparov's mind because i'd already started studying neuroscience than i was with the the machine because um he was this brute of a machine all it can do is play chess and then kasparov can play chess at the same sort of roughly the same level, but also can do all the other things, amazing things that humans can do.

Speaker 17 And so I thought, isn't that, you know, doesn't that speak to the wonderfulness of the human mind?

Speaker 17 And it also, more importantly, means something was missing from very fundamental from deep blue and these expert system approaches to AI, right?

Speaker 17 Very clearly, because deep blue did not seem in, even though it was a pinnacle of AI at the time, it did not seem intelligent. And what was missing was its ability to learn, learn new things.

Speaker 17 So, for example, it was crazy that deep blue could play chess to world champion level, but it couldn't even play tic-tac-toe, right? You'd have to reprogram.

Speaker 17 Nothing in the system would allow it to play tic-tac-toe. So that's odd, right? That's very different to a human grandmaster who could obviously play a simpler game trivially.

Speaker 17 And then also it was not general, right? In the way that the human mind is. And I think those are the hallmarks.
That's what I took away from that match is those are the hallmarks of intelligence.

Speaker 17 And they were needed if we wanted to crack AI.

Speaker 15 And go a little bit into the deep learning, which obviously is part of the reason why deep mind was named where it is, because part of, I think, the what was seemed to be completely contrarian hypothesis that you guys played out with self-play and kind of learning system was that this learning approach was the right way to generate these significant systems.

Speaker 15 So say a little bit about having the hypothesis, what the trek through the desert looked like. And then what finding the Nile ended up with.

Speaker 17 Yes. Well, look, of course, we started Deep Mind in 2010 before anyone was working on this in industry and there was barely any work on it in academia.

Speaker 17 And we partially named the company Deep Mind, the deep part because of deep learning. It was also a nod to deep thought in, you know, Hitchhiker's Guys Galaxy and Deep Blue and other AI things.

Speaker 17 But it was mostly around the idea we were better on these learning techniques.

Speaker 17 Deep learning and hierarchical neural networks, they, you know, just sort of been invented, right, in seminal work by Jeff Hinton and colleagues in 2006. So it's very, very new.

Speaker 17 And reinforcement learning, which has always been a speciality of DeepMind,

Speaker 17 and the idea of learning from trial and error, learning from your experience, right? And then making plans and acting in the world. And we combine those two things, really.

Speaker 17 We sort of pioneered doing that. And we called it deep reinforcement learning, these two approaches.

Speaker 17 And deep learning to kind of build a model of the environment or what you were doing, in this case, a game.

Speaker 17 And then the reinforcement learning to do the planning and the acting and actually accomplish it, be able to build agent systems that could accomplish goals.

Speaker 17 In the case of games, is maximizing the score, winning the game. And we felt that that was actually the entirety of what's needed for intelligence.

Speaker 17 And the reason that we sort of were pretty confident about that is actually from using the brain as an example.

Speaker 17 right basically those are the two major components of of how the brain works you know your your the brain is a neural network It's a pattern matching and structure finding system,

Speaker 17 but then it also has reinforcement learning and this idea of planning and learning from trial and error and trying to maximize reward, which is actually in the human brain and the animal brains, mammal brain is the dopamine system

Speaker 17 implements that, a form of reinforcement learning called TD learning.

Speaker 17 So that gave us confidence that if we pushed hard enough in this direction, even though no one was really doing that, that eventually this should work, right? Because

Speaker 17 we have the existence proof of the human mind and of course that's why i also studied neuroscience because when you're in the desert like you say you need any source of water or any any evidence that you might get out of the desert there's you know even a mirage in the distance is a useful thing to to understand in terms of giving you some direction when you're in the midst of that of that desert and of course ai was itself in the midst of that because um you know several times this had failed the expert system approach basically had reached the ceiling i could easily hog the entire interview, so I'm trying not to.

Speaker 15 So one of the things that the learning system obviously ended up creating was solving what was previously considered an insoluble problem.

Speaker 15 There were even people who thought that computers couldn't, like classical computational techniques couldn't solve Go, and it did.

Speaker 15 But not only did it solve Go, but in the classic Move 37, it demonstrated originality, creativity that was beyond the thousands of years of Go play and books and the hundreds of years of very serious play.

Speaker 15 What was that moment of move 37 like for understanding where AI is?

Speaker 15 And what do you think the next move 37 is?

Speaker 17 Well, look, the reason Go was considered to be and ended up being so much harder than chess, so it took another 20 years,

Speaker 17 even us with AlphaGo, and all the approaches that have been taken with chess, these these expert systems

Speaker 17 approaches, had failed with Go, right?

Speaker 17 Basically couldn't even be a professional, let alone a world champion. And the reason was two main reasons.
One is the complexity of Go is so enormous.

Speaker 17 You know, it's one way to measure that is there are 10 to the power 170 possible positions, right? Far more than atoms in the universe. There's no way you can brute force a solution to Go, right?

Speaker 17 It's impossible. But even harder than that is that it's such a beautiful, esoteric, elegant game.
You know, it's sort of considered art, an art form in Asia, really, right?

Speaker 17 And it's because it's both beautiful aesthetically, but also it's all about patterns rather than sort of brute calculation, which chess is more about. And so

Speaker 17 even the best players in the world can't really describe to you very clearly what are the heuristics they're using. They just kind of intuitively feel the right moves, right?

Speaker 17 They'll sometimes just say that, this move, why did you play this move? Well, it felt right, right?

Speaker 17 And then it turns out their intuition, if they're a brilliant player, their intuition is brilliant, fantastic, and

Speaker 17 it's an amazingly beautiful and effective move. But that's very difficult then to encapsulate in a set of heuristics and rules that to direct how a machine should play go.

Speaker 17 And so that's why all of these kind of deep blue methods didn't work.

Speaker 17 Now, we got around that by having the system learn for itself what are good patterns, what are good moves, what are good motifs and approaches, and

Speaker 17 what are kind of valuable and high probability of winning positions are.

Speaker 17 So it kind of learned that for itself through experience, through seeing millions of games and playing millions of games against itself.

Speaker 17 So that's how we got AlphaGo to be, you know, better than world champion level.

Speaker 17 But the additional exciting thing about that is that it means those kinds of systems can actually go beyond what we as the programmers or the system designers know how to do right no expert system can do that because of course it's strictly limited by what we can just what we already know and can can describe to the machine but these systems can learn for themselves so and that's what we resulted in move 37 in game two of the famous you know world championship match that challenge match we had against lisa doll uh in seoul in 2016 and that was a truly creative move you know go has been played for thousands of years it's the oldest game humans have invented and it's the most complex game.

Speaker 17 And it's been played professionally for hundreds of years in places like Japan.

Speaker 17 And even still, even despite all of that exploration by brilliant human players, this Move 37 was something never seen before. And actually, worse than that, it was thought to be a terrible strategy.

Speaker 17 In fact, if you go and watch the documentary, you know, which I recommend on YouTube, it's on YouTube now of AlphaGo, you'll see the professional commentators nearly fell off their chairs when they saw Move 37 because they thought it was a mistake they thought the the the the computer operator adja had misclicked on the computer because it was so unthinkable that someone would would would play there and then of course in the end it turned out 100 moves later that move 37 the stone the piece that was put down on the board was in exactly the right place to be decisive for the whole game So it turned, you know, now it's studied as a great classic of the of the Go, you know, history of Go, that game and that move.

Speaker 17 And of course, then, and then even more exciting for that is that's exactly what we hoped these systems would do because the whole point of me and my whole motivation, my whole life of working on AI was to use AI to accelerate scientific discovery.

Speaker 17 And it's those kinds of new innovations, albeit in a game, is what we were looking for from our systems.

Speaker 15 And that I think is a... awesome rendition of kind of why it is these learning systems are you know even now

Speaker 15 doing

Speaker 15 original discovery What do you think the next

Speaker 15 move 37

Speaker 15 might be for kind of opening our minds to what is the way that AI can add a whole lot to the kind of quality of human thought, human existence, human science?

Speaker 17 Yeah. Well, look,

Speaker 17 I think there'll be a lot of move 37s in almost every area of human endeavor.

Speaker 17 Of course, the thing I've been focusing on since then is mostly being how can we apply those types of AI techniques, those learning techniques, those general learning techniques to science, big areas of science, I call them root node problems.

Speaker 17 So problems where if you think of the tree of all knowledge that's out there in the universe, you know, can you unlock some root nodes that unlock entire branches or new avenues of discovery that people can build on afterwards?

Speaker 17 Right. And for us, protein folding and alpha fold was one of those.
It was always, you know, top of my list.

Speaker 17 I have a kind of mental list of all these types of problems that I've come across throughout my life and just being generally interested in all areas of science and

Speaker 17 sort of thinking through which ones would be suitable would both be hugely impactful, but also suitable for these types of techniques.

Speaker 17 And I think we're, you know, we're going to see a kind of new golden era of these types of new strategies, new ideas. in very important areas of human endeavor.

Speaker 17 Now, I would say one thing to say, though, is that we haven't fully cracked creativity yet, right? So I don't want to claim that.

Speaker 17 I think that there are, you know, I often describe as three levels of creativity. And I think AI is capable of

Speaker 17 the first two. So first one would be interpolation.

Speaker 17 So you give it, you know, a million pictures of cats, an AI system, a million pictures of cats, and you say, create me a, the prototypical cat, and it will just like average all the million cats pictures that it's seen.

Speaker 17 And that prototypical one won't be in the training set.

Speaker 17 So it will be a unique cat but it's not very you know that's not very interesting from a creative point of view right it's just an averaging but the second thing would be what i call extrapolation so that's more like alpha go where you you've played 10 million games of go you've looked at you know a few million human games of go but then you come up with you extrapolate from what's known uh to a new strategy never seen before like move 37.

Speaker 17 okay so that's very valuable already that is you know i think that is true creativity but then there's a third level which i call it kind of invention or out of the box thinking, which is not only can you come up with a move 37, but could you have invented Go, right?

Speaker 17 Or another

Speaker 17 measure I like to use is if we went back to time of Einstein in 1900, early 1900s, could an AI system actually come up with general relativity with the same information that Einstein had at the time?

Speaker 17 And clearly today, the answer is no. to those things, right?

Speaker 17 It can't invent something as a game as great as Go, and uh it wouldn't be able to invent general relativity just from the information that einstein had at the time and so there's still something missing uh from our systems to get uh you know true out of the box thinking but i think it'll come but we just don't have it yet I think so many people outside of sort of the AI realm would sort of be surprised that sort of it all starts with gaming, but that's sort of gospel for what we're doing.

Speaker 18 It's like, that's how we created these systems.

Speaker 18 And so switching gears from board games to video games, can you give us just like the elevator pitch explanation for what exactly makes an AI that can play StarCraft II, like Alpha Star, so much more advanced and fascinating than the one that can play chess or Go?

Speaker 15 Yep.

Speaker 17 With AlphaGo, we sort of cracked the pinnacle of board games, right? So Go was always considered the Mount Everest, if you like, of games AI for board games.

Speaker 17 But there are even more complex games by some measures, if you take on board the most complex strategy games that you can play on computers.

Speaker 17 And StarCraft II is acknowledged to be the sort of classic of the genre of real-time strategy games. And it's a very complex game.
You've got to build up your base and your units and other things.

Speaker 17 So every game is different, right? And the board game is very fluid and you've got to move many units around in real time.

Speaker 17 And the way we cracked that was to add this additional level in of a league of agents competing against each other, all seeded with slightly different initial strategies.

Speaker 17 And then you kind of get a sort of survival of the fittest. You have a tournament between them all.
So it's a kind of multi-agent setup now.

Speaker 17 And the strategies that win out in that tournament go to the next, you know, the next epoch. And then you generate some other new strategies around that.
And you keep doing that for many generations.

Speaker 17 You're kind of both having this idea of self-play that we had in AlphaGo, but you're adding in this multi-agent competitive, almost evolutionary dynamic in there.

Speaker 17 And then eventually you get an agent that or series or set of agents that are kind of the Nash distribution of agents.

Speaker 17 So no other strategy dominates them, but they dominate the most number of other strategies. And then you have this kind of Nash equilibrium.

Speaker 17 And then you pick out the, you know, you pick out the top agents from that.

Speaker 17 And

Speaker 17 that succeeded very well with this type of very open-ended kind of gameplay.

Speaker 17 So it's quite different from what you get with chess or go where the rules are very prescribed and the pieces that you get are always the same

Speaker 17 and it's sort of a very ordered game something like starcraft's much more chaotic so it's it's sort of uh interesting to have to deal with that it has hidden information too you can't see the whole map at once you have to explore it so you it's not it's not a perfect information game which is another thing we wanted our systems to be able to cope with is is partial information situations which is actually more like the real world right very rarely in the real world do you actually have full information about everything uh usually you only have partial information and then you have to infer everything else in order to come up with the right strategies.

Speaker 15 And part of the game side of this is, I presume you've heard that there's this kind of theory of homologens. Yes.
That we're game players. Is that informing the kind of thinking about how

Speaker 15 games is both strategic, but also

Speaker 15 kind of framing for like science acceleration, framing for

Speaker 15 kind of the serendipity of innovation is

Speaker 15 in addition to the kind of the fitness function, the

Speaker 15 kind of evolution of self-play, the ability to play, scale, compute, are there other deeper elements to the game playing nature that allows this thinking of thinking?

Speaker 17 Well, look, I'm glad you brought up Home Eludens, and it's a wonderful book. And it basically argues that

Speaker 17 games playing

Speaker 17 is actually a fundamental part of being human, right? In many ways, that's

Speaker 17 the act of play. What could be more human than that, right? And then of course it leads into creativity, fun,

Speaker 17 all of these things kind of get built on top of that. And so I've always loved them as a way to practice and train your own mind in situations that you might only ever get

Speaker 17 a handful of times in real life. but they're usually very critical, you know, what company to start, what deal to make, things like that.

Speaker 17 So I think games is a way to uh practice those scenarios and if you take games seriously then you can actually simulate a lot of the pressures one would have in decision-making situations and i and i and and going back to earlier that's why i think chess is such a great training ground for kids to learn because it does teach them about all of these these situations and um and so of course it's the same for ai systems too there was the perfect proving ground for our early ai system ideas um partly because they were invented to be challenging and fun for humans to play.

Speaker 17 So they, and, and of course, there's different levels of gameplay.

Speaker 17 So we could start with very simple games like Atari games and then go all the way up to the most complex computer games like StarCraft, right? And continue to sort of challenge our system.

Speaker 17 So we were in the sweet spot of the S curve. So it's not too easy, it's trivial, or too hard, you can't even see if you're making any progress.
You want to be in that

Speaker 17 maximum sort of part of the S curve where you're making almost exponential progress. And we could keep picking harder and harder games as our systems got improved.

Speaker 17 And then the other nice feature about games is because they're some kind of microcosm of the real world, they've usually been boiled down to very clear objective functions, right?

Speaker 17 So winning the game or maximizing the score is usually the objective of a game.

Speaker 17 And that's very easy to specify to a reinforcement learning system or an agent-based system.

Speaker 17 So you can, it's perfect for hill climbing against, right, and measuring elo scores, ratings, and exactly where you are.

Speaker 17 And then finally, of course, you can calibrate yourself against the best human players. So you can sort of calibrate what your agents are doing in their own tournaments.

Speaker 17 In the end, even with the StarCraft agent, we had to eventually challenge a professional grandmaster at StarCraft to make sure that our systems hadn't overfitted somehow to their own tournament strategies, right?

Speaker 17 It actually needed to be, oh, we grounded it with, oh, it it can actually be a genuine human grandmaster StarCraft player.

Speaker 17 The final thing is, of course, you can generate as much synthetic data as you want with games too, which is, you know, coming into vogue right now, again, about data limitations and, you know, with large language models and how many tokens left in the world and has it read everything in the world.

Speaker 17 Obviously, for things like games, you can actually just play the system against itself and generate lots more data from the right distribution.

Speaker 18 Can you double click on that for a moment? Like you said, it is in vogue to talk about, are we running out of data? Do we need synthetic data? Like, where do you stand on that issue?

Speaker 17 Well, I've always been a huge proponent of simulations

Speaker 17 and simulations and AI.

Speaker 17 And, you know, it's also interesting to think about what the real world is, right, in terms of a computational system.

Speaker 17 And

Speaker 17 so I've always been involved with trying to build very realistic simulations of things.

Speaker 17 And now, of course, that interacts with AI because you can have an AI that learns a simulator of some real world system

Speaker 17 just by observing that system or all the data from that system. So I think the current debate is to do with these large foundation models

Speaker 17 now pretty much use the whole internet, right? And so then once you've tried to learn from those, what's left, right? That's all the language that's out there.

Speaker 17 Of course, there's other modalities like video and audio. I don't think we've exhausted all of that kind of multimodal tokens, but even that will reach some limit.

Speaker 17 So then the question comes of like, can you generate synthetic data?

Speaker 17 And I think that's why you're seeing quite a lot of progress with maths and coding, because in those domains, it's quite easy to generate synthetic data.

Speaker 17 The problem with synthetic data is, are you creating data that is from the right distribution? the actual distribution, right? Does it mimic the kind of real distribution?

Speaker 17 And also, are you generating data that's correct? Right.

Speaker 17 And of course, for things like maths, for coding, and for things like gaming, you can actually test the final data and verify if it's correct, right? Before you feed it in as

Speaker 17 input into the training data for a new system.

Speaker 17 So it's very amenable,

Speaker 17 certain areas. In fact, turns out the more abstract areas of human thinking that you can verify and prove that it's correct.

Speaker 17 And so therefore, that unlocks a sort of ability to create a lot of synthetic data.

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Speaker 15 So one of the things that, you know, is kind of also in addition to the kind of the frequent discussion around, you know, data, how do we get more, but one of the questions is, in order to do AI,

Speaker 15 is it important to actually have it embedded in the world? Yeah.

Speaker 17 Well, interestingly, if we talked about this

Speaker 17 five years ago or certainly 10 years ago, I would have said that some real world experience, you know, maybe through robotics.

Speaker 17 Usually when we talk about embodied intelligence, we're meaning robotics but it could also be a very accurate simulator right like some kind of ultra realistic game environment would be needed to fully understand the say the physics of the world around you right and and the physical context around you and there's actually a whole branch of neuroscience that is uh predicated on this it's called action in in perception so this is the idea that one can't actually fully perceive the world unless you can also act in it and the kinds of arguments go is like how can you really understand the concept of the weight of something, for example, unless you can pick things up and sort of compare them with each other?

Speaker 17 And then you get this sort of idea of weight. Like, can you actually, you know, can you really get that notion just by looking at things? It seems hard, right? Certainly for humans.

Speaker 17 Like, I think you need to act in the world. So this is idea that acting in the world is part of your learning.
You're kind of like an active learner.

Speaker 17 And in fact, reinforcement learning is like that, because the decisions you make give you new experiences, but those experiences depend on the actions you took, but also those are the experiences that you'll then subsequently learn from.

Speaker 17 So in a sense, reinforcement learning systems are involved in their own learning process, right? Because they're active learners.

Speaker 17 And I think you can make a good argument that that's also required in the physical

Speaker 17 world.

Speaker 17 Now, it turns out, I'm not sure I believe that anymore, because now, you know, with our systems, especially our video models, if you've seen VO2, you know, our latest video models, completely state of the art, which we released late last year.

Speaker 17 And it astounds kind of shocked even me that even though we're building this thing, that it can sort of basically by watching YouTube video, a lot of YouTube videos, it can figure out, you know, the physics of the world.

Speaker 17 There's a sort of funny Turing test of, you know, in some sense, Turing tests in Verg commas of video models, which is, can you chop a tomato?

Speaker 17 Can you show a video of, you know, a knife chopping a tomato with the fingers and everything in the right place?

Speaker 17 And the tomato doesn't, you know, magically spring back together or the knife goes through the tomato without cutting it, et cetera. And VO can do it.

Speaker 17 And if you think through the complexity of the physics, you know, to understand this, you know, you've got to, what you've got to keep consistent and so on, it's pretty amazing.

Speaker 17 It's like, it's hard to argue that it doesn't understand something about physics and the physics of the world.

Speaker 17 And it's done it without acting in the world and certainly not without, certainly not acting as a robot in the the world now uh so i you know it's not clear to me there is a limit now with just sort of passive perception now the interesting thing is that i think this has huge consequences for um robots as an embodied intelligence as an application because the types of models we've built gemini and also now vo and we'll be combining those things together at some point in the future is uh we've always built gemini our our foundation model to be multimodal from the beginning and the reason we did that and

Speaker 17 we still lead on all the multimodal benchmarks, is because for twofold.

Speaker 17 One is we have a vision for this idea of a universal digital assistant, an assistant that goes around with you on the digital devices, but also in the real world, maybe on your phone or a glasses device,

Speaker 17 and actually helps you.

Speaker 17 um in the real world like recommend things to you navigate you you know help you navigate around um you know help with physical things in the world like cooking stuff like like that and and um for that to work you obviously need to understand the context that you're in it's not just the language i'm typing into a chat bot you actually have to understand the 3d world i'm living in right i think to be a really good assistant you need to do that um but the second thing is of course is exactly what you need for robotics as well and and we uh released our our first big sort of gemini robotics work which has caused a bit of a stir and that's the beginning of showing what we showcasing what we can do with these multimodal models that do understand physics of the world with a little bit of robotics fine-tuning on top to do with the actions and the motor actions and the planning a robot needs to do.

Speaker 17 And it looks like it's going to work.

Speaker 17 So actually now I think these general models are actually going to transfer to the embodied robotic setting without too much extra sort of special casing or extra data or extra effort, which is probably not what most people, even the top roboticists, would have predicted five years ago.

Speaker 18 I mean, that's wild.

Speaker 18 And you know, thinking about benchmarks and what we're going to need these digital assistants to do, like when we look under the hood of these big AI models, there's, there's, well, some people would say it's attention.

Speaker 18 So, the trade-offs is thinking time versus output quality. We need them to be fast, but of course, we need them to be accurate.

Speaker 18 And so, talk about like what is that trade-off and how is that going in the world right now?

Speaker 17 Well, look, we, of course, we sort of pioneered all that area of thinking systems because that's what our original gaming systems all did, right?

Speaker 17 Go, AlphaGo, but actually most famously AlphaZero, which was our follow-up system that could play any two-player game.

Speaker 17 And there, you always have to think about your time budget, your compute budget you've got to actually do the planning part, right?

Speaker 17 So the model you can pre-train, just like we do with our foundation models today.

Speaker 17 So you can play millions of games offline, and then you have your model of chess or your model of Go or whatever it is.

Speaker 17 And then, but at test time, at runtime, you've only got one minute to, you know, to think about your move, right? One minute times however many computers you've got running.

Speaker 17 So that's still a limited compute budget. So what's very interesting today is there's this trade-off between, do you use a more expensive, larger base model, foundation model?

Speaker 17 right so in our case you know we have we have different size names like gemini flash and which and or pro or even bigger which is ultra but those models are more costly to run so they take longer longer to run.

Speaker 17 But they're more accurate and they're more capable.

Speaker 17 So you can run a bigger model with a shorter number of planning steps, or you can run a very efficient smaller model that's slightly less powerful, but you can run it for many more steps, right?

Speaker 17 And it's actually currently what we're finding is it's sort of roughly about equal. But of course, what we want to find is some, is some...
the Pareto frontier of that, right?

Speaker 17 Like actually the exact right trade-off of the size of the model and the expense of that running that model versus the amount of thinking time you want to and thinking steps that you're you're able to do per unit of compute time and uh i think that's that's actually fairly cutting-edge research right now that that i think all the leading labs are probably experimenting on and uh i i think there's not a clear answer to that yet you know all the major labs the deep mind others are all working intensely on coding assistance.

Speaker 15 And there's, you know, a number of reasons. Everything from, you know, like, it's one of the things that accelerates productivity across the whole front.
It has a kind of good fitness function.

Speaker 15 It's also, of course, one of the ways that

Speaker 15 everyone is going to be enhancing productivity is having a software, you know, kind of co-pilot agent for helping. There's just a ton of reasons.

Speaker 15 Now, one of the things that gets interesting here is as you're building these,

Speaker 15 you know, obviously there's a tendency to start with these computer languages that have been designed for humans.

Speaker 15 What would be computer languages that would be designed for AIs or an agentic world or designed for this hybrid process of a human plus an AI?

Speaker 15 Is that a good world to start looking at those kind of computer languages? How would it change our theory of computation, linguistics, et cetera?

Speaker 17 I think we are entering a new era in coding, which is going to be very interesting. And, you know, as you say, all the leading labs are pushing on this frontier for many reasons.

Speaker 17 It's easy to create synthetic data. So that's another reason that

Speaker 17 everyone's pushing on this vector. And I think we're going to move into a world where

Speaker 17 sometimes it's called vibe coding, where you're basically coding with natural language, really.

Speaker 17 And we've seen this before with computers, right? I remember when I first started programming

Speaker 17 in the 80s, we were doing assembler. And then, of course,

Speaker 17 that seems crazy now. Like, why would you do machine code? You just, you know, you start with C and then you get Python and so on.

Speaker 17 And really, one could see as the natural evolution of going higher and higher up the abstraction stack of programming languages and leaving the lower the more and more of the lower level implementation details to the compiler in a sense and now this is just you know one could just view this as the as the natural sort of final step is well we just use natural language uh and then and then the whole you know everything is is high level program you know super high level programming language um

Speaker 17 and and and i think we eventually that's maybe maybe what we'll get to and the exciting thing there is that of course it will make accessible coding to a whole new range of people people, creatives, right?

Speaker 17 Who normally would, you know, designers, game designers, app writers,

Speaker 17 that would normally would not have been able to implement their ideas without the help of, you know, teams of programmers.

Speaker 17 So that's going to be pretty exciting, I think, from a creativity point of view. But it may also be very good, certainly in the next few years for coders as well, because I think

Speaker 17 there's, and I think this in general with these AI tools is I think that the people that are going to get most benefit out of them initially will be the experts in that area who also know how to use these tools in precisely the right way, whether that's prompting or interfacing with your existing code base.

Speaker 17 There's going to be this sort of interim period where I think the current experts who embrace these new tools, whether that's filmmakers, game designers, or coders, are going to be like superhuman

Speaker 17 in terms of what they're able to do. And I see that with some film

Speaker 17 directors and film designer

Speaker 17 friends of mine who are able to create pitch decks, for example, for new film ideas in a day on their own.

Speaker 17 But it's very high quality pitch deck that they can pitch for a $10 million budget for.

Speaker 17 And normally they would have had to spend a few tens of thousands of dollars just to get to that pitch deck, which is a huge risk for them.

Speaker 17 So it becomes,

Speaker 17 you know, I think there's going to be a whole new incredible set of opportunities.

Speaker 17 And then there's the question of like, if you think about creative the creative arts whether there'll be new ways of working much more fluid instead of doing you know adobe photoshop or something you're actually co-creating this thing with this fluid responsive tool and um that could be kind of feel more like minority report or something you know i imagine with the kind of interface and there's this thing swirling around you and you're and and you're kind of but it'll it'll require people to get used to a very new workflow um to take like maximum advantage of that.

Speaker 17 But I think when they do, it will be probably incredible for those people. They'll be like 10x more productive.

Speaker 18 So I want to go back to the

Speaker 18 world of multimodal that we were talking about before with sort of robots in the real world.

Speaker 18 And so right now, most AI doesn't need to be multimodal in real time because the internet is not multimodal.

Speaker 18 And for our listeners, that means absorbing many types of input, voice, text, vision at once. And so can you go deeper in what you think the benefits of truly real-time multimodal AI will be?

Speaker 18 And like, what are the challenges to get to that point?

Speaker 17 I think, first of all, we live in a multimodal world, right? That's that, and we have our five senses and that's what makes us human.

Speaker 17 So if we want our systems to be brilliant tools or fantastic assistants, I think in the end, they're going to have to understand the world, the spatial temporal world that we live in, not just our linguistic maths world, right?

Speaker 17 Abstract thinking world. I think that they'll need to be able to act in and plan in and process things in the real world and understand the real world.

Speaker 17 I think that computer, you know, sort of the potential for robotics is huge. I don't think it's had its

Speaker 17 chat GPT or its alpha fold moment yet, say in science and language, right? Or alpha go moment. I think that's to come.
But I think, I think we're close.

Speaker 17 And as we talked about before, I think in order for that to happen, I think that the shortest path I see that happening on now is these general multimodal models being eventually good enough.

Speaker 17 And maybe we're not very far away from that to sort of install on a robot, perhaps a humanoid robot with the cameras.

Speaker 17 Now, there's additional challenges of you've got to fit it locally on maybe on the local chips to have the latency fast enough and so on.

Speaker 17 But, you know, as every, as we all know, just wait a couple of years and

Speaker 17 those systems that stay of the art today will fit on a little mobile chip tomorrow. So I think it's very exciting multimodal

Speaker 17 from that point of view, robotics, assistance.

Speaker 17 And then finally, I think also for creativity, I think we're the first model in the world, Gemini 2.0, that you can try now in AI Studio that allows native image generation.

Speaker 17 So, not calling a separate program, you know, in this separate model, in our case, ImageN3, you know, which you can try separately, but actually Gemini itself natively coming up in the in the chat flow of images.

Speaker 17 And I think people seem to be really enjoying using that. So it's sort of like you're now talking to a multimodal chat bot, right?

Speaker 17 And so you can get it to express emotions in pictures, or you can give it a picture and then tell it to modify it and then continue to work on it with word descriptions.

Speaker 17 You know, can you remove that background? Can you do this? So this goes back to the other earlier thing we said about, you know, programming or any of these creative things in a new workflow.

Speaker 17 I think we're just seeing the glimpse of that if you try out this new Gemini 2 experimental model

Speaker 17 of how that might look in image creation. And that's just the beginning.
Of course, it will work with video and coding and all sorts of things.

Speaker 15 So in the land of the real world and multimodal,

Speaker 15 one of the things that frequently people speculate is geolocation of AI work.

Speaker 15 And obviously, in the US, we intensely track everything that's happening on the West Coast.

Speaker 15 We also intensely track DeepMind and then somewhat less Mistral,

Speaker 15 you know, and others.

Speaker 15 What's some of the stuff that's really key for the world to understand about what's coming out of Europe?

Speaker 15 What's the benefit of having there be multiple major centers of innovation and invention, you know, not just within the West Coast, but also obviously DeepMind and London and Mistral and Paris and others.

Speaker 15 And what are some of the things

Speaker 15 for people to pay attention to, why it's important and what's happening, especially within the UK and European AI ecosystem?

Speaker 17 We started deep mine in London and

Speaker 17 still headquartered here for several reasons. I mean, this is where I grew up, as what I know.
It's where I had all the contacts that I had.

Speaker 17 But the competitive reasons were that we felt that the talent in the UK and in Europe was the coming out of universities was the equivalent of the top US ones.

Speaker 17 You You know, Cambridge, Mylmata and Oxford, they're up there with the MITs and Harvards and the Ivy Leagues, right?

Speaker 17 I think they're sort of, you know, they're always in the top 10 there together on the university world tables.

Speaker 17 But if you, this is certainly true in 2010, if you were coming, say you had a PhD in physics out of Cambridge and you didn't want to work in finance at a hedge fund in the city, but you wanted to stay in the UK and be intellectually challenged, there were not that many options for you, right?

Speaker 17 There are not that many deep tech startups.

Speaker 17 So we were the first, really and to prove that could be done and actually we were a big draw for the whole of europe so we got the best people from the technical universities in you know munich and in switzerland and so on and for a long while that was a huge competitive advantage and um also salaries were were cheaper here and then in the in the west coast and you weren't competing against the big incumbents right uh and also it was conducive the other reason i chose to do that was I knew that AGI, which was our plan from the beginning, you know, solve intelligence and then use it to solve everything else.

Speaker 17 That was our, where we articulated our mission statement. And I still like that, that framing of it.
It was a 20-year mission.

Speaker 17 And if you're on a 20-year mission, and we're now 15 years in, and I think we're sort of on track, unbelievably, right?

Speaker 17 Which is strange for any 20-year mission, but is you don't want to be too distracted on the way in a deep science, deep technology, deep scientific mission. So

Speaker 17 one of the issues I find with Silicon Valley is lots of benefits, obviously, contacts and support systems and funding and amazing things and the amount of talent there, the density of talent.

Speaker 17 But it is quite distracting, I feel. Like everyone and their dog is trying to do a startup, you know, that they think it's going to change the world, but it's just a photo app or something.

Speaker 17 And then, you know, the cafes are filled with this.

Speaker 17 Of course, it leads to some great things, but it's also a lot of noise if one actually wants to commit to a long-term mission that you think is the most important thing ever.

Speaker 17 And you don't want to be too, you know, you and your staff and want to be too distracted and like, oh, I could make a, maybe I could make a hundred million though if i jumped and did this you know quickly did this gaming app or something right and and i think that's sort of the the the milieu that you're in uh in the valley at least at least back then maybe this is less true now there's probably more mission focused uh startups now but i also i kind of also wanted to prove it could be done elsewhere and then the final reason i think it's important is that ai is going to affect the whole world right it's going to affect every industry it's going to affect every country it's it's going to be the most transformative technology ever, in my opinion.

Speaker 17 So if that's true, and it's going to be like electricity or fire, you know, more impactful than even the internet or

Speaker 15 mobile, then

Speaker 17 I think it's important that the whole world participates in its design and with the different value systems that we think are out there that are, you know, philosophies that are, you know, are good philosophies and, you know, from democratic values, you know, Western Europe, US, you I think it's important that it's not just 100 square miles of a patch of California, right?

Speaker 17 I do actually think it's important that we get these other inputs, the broader inputs, not just geographically, but also, and I know you agree with this, Reed, like different subjects, philosophy, social sciences, economists.

Speaker 17 academia,

Speaker 17 civil society, not just the tech companies, not just the scientists involved in deciding how this gets built and what it gets used for.

Speaker 17 And I feel that I've always felt that very strongly from the beginning. And I think having some European involvement and some UK involvement at the top table of the innovation is a good thing.

Speaker 18 So Demos, one of the areas of AI that when anyone asks me like, hey, Aria, I know you're interested in AI, but like, well, you can write my emails. Like, why is it so special?

Speaker 18 I just say, no, think about what it can do. In medicine, I always talk about AlphaFold.
I tell them about what Reed is doing. Like, I'm just so excited for those breakthroughs.

Speaker 18 Can you give us just a little bit? You had this seminal breakthrough in AlphaFold, and what is it going to do for the future of medicine?

Speaker 17 I've always felt that like, what are the most important things AI can be used for? Right. And I think there are two.
One is human health. That's number one, trying to solve and cure terrible diseases.

Speaker 17 And then number two is to help with, you know, energy, sustainability, and climate, the planet, the planet's health, let's call it. Right.
So there's human's health and then there's a planet's health.

Speaker 17 And those are are the two areas that we have focused on in our science group, which I think is, you know, fairly unique amongst the AI labs, actually, in terms of how much we pushed that from the beginning.

Speaker 17 And then, and protein folding specifically was this canonical for me. I sort of came across it when I was an undergrad in Cambridge, you know, 30 years ago.

Speaker 17 And it's always stuck with me as this fantastic puzzle.

Speaker 17 That would unlock so many possibilities, you know, the structure of proteins. Everything in life depends on proteins.
And we need to understand the structure so we know their function.

Speaker 17 And if we know the function, then we can understand what goes wrong in disease and we can design drugs and molecules that will bind to the right part of the surface of the protein if you understand if you know the 3D structure.

Speaker 17 So it's a fascinating problem.

Speaker 17 It goes to all of the computational things we were discussing earlier as well.

Speaker 17 Can you enumerate, can you see through this forest of possibilities, you know, all these different ways a protein could fold?

Speaker 17 You know, some people estimate Levental very famously in the 60s, 1960s estimated an average protein can fold in 10 to the 300 possible ways, right?

Speaker 17 So how do you, how do you get, you know, enumerate those and astronomical possibilities? And yet it is possible with these learning systems. And that's what we did with AlphaFold.

Speaker 17 And then we spun out a company, Isomorphic, and I know Reed's very interested in this area too with his new company of like, if we can reduce the time it takes to discover a protein structure from, it used to take a PhD student their entire PhD as a rule of thumb to discover one protein structure.

Speaker 17 So four or five years. And there's 200 million proteins known to science.
And we folded them all in one year. So we did a billion years of PhD time in one year is another way you can think of it.

Speaker 17 And then gave it to the world, you know, freely to use. And, you know, 2 million researchers around the world have used it.

Speaker 17 And we spun out a new company, Isomorphic, to try and go further downstream now and develop the drugs needed and try and reduce that time.

Speaker 18 I mean, it's just amazing. I mean, Demis, there's a reason they give you the Nobel Prize.
Thank you so much for all of your work in this area. It's truly amazing.

Speaker 15 And now to Rabid Fire. Is there a movie, song or book, that fills you with optimism for the future?

Speaker 17 There's lots of movies that I've watched that have been super inspiring for me. Things like even like Blade Runner.

Speaker 17 um is probably my my favorite sci-fi movie um but maybe it's not that optimistic so if you want an optimistic thing, I would say the culture series by Ian Banks.

Speaker 17 I think that's the best depiction of a post-AGI universe where, you know, AIs and you've basically got societies of AIs and humans and kind of alien species actually, and sort of maximum human flourishing across the galaxy.

Speaker 17 That's a kind of amazing, compelling future that I would hope for humanity.

Speaker 18 What is a question that you wish wish people asked you more often?

Speaker 17 The questions

Speaker 17 I sort of often wonder why people don't discuss a lot more, including with me, are some of the really fundamental properties of reality that actually drove me in the beginning when I was a kid to think about building AI to help us sort of this ultimate tool for science.

Speaker 17 So for example, you know, I don't understand why people don't worry more about what is time? What is, what is, you know, what is gravity?

Speaker 17 What, what, you know, they're basically the fundamental fabric of reality, like, which is sort of staring us in the face all the time, all these very obvious things that impact us all the time.

Speaker 17 And we, we don't really have any idea how it works. And I don't know why that doesn't trouble people more.
It troubles me. And,

Speaker 17 you know, I'd love to have more debates with people about those things. But actually, most people don't seem to, you know, they seem to sort of shy away from those topics.

Speaker 15 Where do you see progress or momentum outside of your industry that inspires you?

Speaker 17 That's a tough one because AI is so general. It's almost touching, you know, what industry is outside of the AI industry.
I'm not sure there's many.

Speaker 17 Maybe, you know, the progress going on in quantum is kind of interesting.

Speaker 17 I still believe AI is going to get built first and then will maybe help us perfect our quantum systems.

Speaker 17 But I have, you know, ongoing bets with some of my quantum friends like Hartman Nebin on they're going to build quantum systems first and then that will help us accelerate AI.

Speaker 17 So I always keep a close eye on the advances going on with quantum computing systems.

Speaker 18 Final question. Can you leave us with a final thought on what is possible over the next 15 years if everything breaks humanity's way? And what's the first step to get there?

Speaker 17 Well, what I hope for next 10, 15 years is what we're doing in medicine to really have new breakthroughs.

Speaker 17 I think maybe in the next 10, 15 years, we can actually have a real crack at solving all disease, right?

Speaker 17 That's the mission of isomorphic. And I think with AlphaFold, we showed what the potential was

Speaker 17 to sort of do what I like to call science at digital speed. And why can that also be applied to finding medicines?

Speaker 17 And so my hope is 10, 15 years time, we'll look back on the medicine we have today a bit like how we look back on medieval times and how we used to do medicine then, you know, and that would be, I think, the most incredible benefit we could imagine from ai

Speaker 15 possible is produced by wonder media network it's hosted by aria finger and me read hoffman our showrunner is sean young possible is produced by katie sanders edie allard sarah schleid vanessa handy aliyah yates palomo moreno jimenes and malia Agudelo.

Speaker 15 Jenny Kaplan is our executive producer and editor.

Speaker 18 Special thanks to Surya Yalamanchili, Sayeda Sepieva, Vanasi Dilos, Ian Alice, Greg Beato, Parth Patel, and Ben Relis. And a big thanks to Layla Hajaj, Alice Talbert, and Denise Owusu-Afriye.

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