Pivot
Demis Hassabis on AI, Game Theory, Multimodality, and the Nature of Creativity | Possible
Sat, 12 Apr 2025
How can AI help us understand and master deeply complex systems—from the game Go, which has 10 to the power 170 possible positions a player could pursue, or proteins, which, on average, can fold in 10 to the power 300 possible ways? This week, Reid and Aria are joined by Demis Hassabis. Demis is a British artificial intelligence researcher, co-founder, and CEO of the AI company, DeepMind. Under his leadership, DeepMind developed Alpha Go, the first AI to defeat a human world champion in Go and later created AlphaFold, which solved the 50-year-old protein folding problem. He's considered one of the most influential figures in AI. Demis, Reid, and Aria discuss game theory, medicine, multimodality, and the nature of innovation and creativity. For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/ Listen to more from Possible here. Learn more about your ad choices. Visit podcastchoices.com/adchoices
Chapter 1: What is the significance of AI in global industries?
Hi, I'm Reid Hoffman.
And I'm Aria Finger.
We want to know how, together, we can use technology like AI to help us shape the best possible future.
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.
This is possible. In the 13th century, Sir Galahad embarked on a treacherous journey in pursuit of the elusive Holy Grail. 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.
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.
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. How does a long line of amino acids configure itself into a 3D structure that becomes the building block of life itself?
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.
Demis Hassabis is a British artificial intelligence researcher, co-founder, and CEO of the AI company DeepMind. 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. He is considered one of the most influential figures in AI.
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Chapter 2: How did Demis Hassabis transition from chess to AI?
And I remember, you know, when I was in middle school, it was obviously the deep blue versus Garry Kasparov. And this was like a man versus machine moment. 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. Can you say more about that dichotomy?
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. I think it's fantastic training for the mind, just like doing maths or programming would be.
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. So you sort of learn all of that as a young kid, which is fantastic for anything else you're going to do. And as far as Deep Blue goes, you're right.
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 a kind of brute force search towards, in this case, making a good chess move. And I always had this, although I was fascinated by these Ailey 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 machine because 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. And so I thought, doesn't that speak to the wonderfulness of the human mind? And it also, more importantly, means something was missing from very fundamental from Deep Blue and these expert system approaches to AI, right?
Very clearly, because Deep Blue did not seem 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. 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.
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 should obviously play a simpler game trivially. And then also it was not general, right? In the way that the human mind is. And I think those are the hallmarks.
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Chapter 3: What made AlphaGo's Move 37 a landmark moment?
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. 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.
And then eventually you get an agent or a set of agents that are kind of the Nash distribution of agents. So no other strategy dominates them, but they dominate the most number of other strategies. And then you have this kind of Nash equilibrium, and then you pick out the top agents from that. And that succeeded very well with this type of very open-ended kind of gameplay.
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. And it's sort of a very ordered game. Something like StarCraft's much more chaotic. So it's sort of 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 it's not a perfect information game, which is another thing we wanted our systems to be able to cope with 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.
Usually you only have partial information and then you have to infer everything else in order to come up with the right strategies.
And part of the game side of this is, I presume you've heard that there's this kind of theory of homo ludens. Yes. That, you know, we're game players. Is that informing the kind of thinking about how, you know, games is both strategic, but also, you know, kind of framing for like science acceleration, framing for, you know, kind of the serendipity of innovation, right?
is in addition to the kind of the fitness function, the 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?
Well, look, I'm glad you brought up Homer Ludens and it's a wonderful book. And it basically argues that games playing is actually a fundamental part of being human, right? In many ways, that's the act of play. What could be more human than that, right? And then of course, it leads into creativity, fun, all of these things kind of get built on top of that.
I've always loved them as a way to practice and train your own mind in situations that you might only ever get a handful of times in real life, but they're usually very critical. What company to start, what deal to make, things like that. I think games is a way to... practice those scenarios.
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Chapter 4: How does AlphaStar differ from traditional AI in gaming?
Chapter 5: What role does creativity play in AI development?
Chapter 6: How can AI accelerate scientific discovery?
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. So in a sense, reinforcement learning systems are involved in their own learning process, right? Because they're active learners. And I think you can make a good argument that that's also required in the physical world.
Now, if it turns out, I'm not sure I believe that anymore because now with our systems, especially our video models, if you've seen VO2, our latest video models, completely state of the art, which we released late last year. And it...
It kind of shocked even me that even though we're building this thing, that it can sort of basically by watching a lot of YouTube videos, it can figure out the physics of the world. There's a sort of funny Turing test of, in some sense, Turing test in verb commas of video models, which is, can you chop a tomato?
Can you show a video of, you know, a knife chopping a tomato with the fingers and everything in the right place? 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.
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. It's hard to argue that it doesn't understand something about physics and the physics of the world. And it's done it without acting in the world and certainly not acting as a robot in the world.
Now, 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 robots as an embodied intelligence, as an application, because the types of models we've built, Gemini and also now Veo, and we'll be combining those things together at some point in the future, is we've always built Gemini, our foundation model, to be multimodal from the beginning.
And the reason we did that, and we still lead on all the multimodal benchmarks, is because for twofold. 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, and actually helps you
in the real world, like recommend things to you, help you navigate around, help with physical things in the world, like cooking, stuff like that. And 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 chatbot. You actually have to understand the 3D world I'm living in, right?
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Chapter 7: What challenges exist for synthetic data in AI?
And those 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 push that from the beginning. 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.
And it's always stuck with me as this fantastic puzzle 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.
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 know the 3D structure. So it's a fascinating problem. It goes to all of the computational things we were discussing earlier as well.
Can you enumerate, can you see through this forest of possibilities, all these different ways a protein could fold? Some people estimate that Leventhal, very famously in the 1960s, estimated an average protein can fold in 10 to 300 possible ways. So how do you enumerate those astronomical possibilities? And yet it is possible with these learning systems. And that's what we did with AlphaFold.
And then we spun out a company, Isomorphic, and I know Reid'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. 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. And then gave it to the world freely to use. And 2 million researchers around the world have used it. 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.
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.
And now to rapid fire. Is there a movie, song or book that fills you with optimism for the future?
There's lots of movies that I've watched that have been super inspiring for me. Things like, even like Blade Runner is probably my favorite sci-fi movie. But maybe it's not that optimistic. So if you want an optimistic thing, I would say the Culture series by Ian Banks.
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