#475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games
Episode publishedPodcast: Lex Fridman PodcastLex Fridman Podcast
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The following is a conversation with Demas Hassabis, his second time on the podcast. He is the leader of Google DeepMind and is now a Nobel Prize winner. Demas is one of the most brilliant and fascinating minds in the world today working on understanding and building intelligence and exploring the big mysteries of our universe. This was truly an honor and a pleasure for me. And now a quick few second mention of each sponsor. Check them out in the description or at lexfreeman.com/sponsors. It's the best way to support this podcast. We've got Hampton for connecting with founders and CEOs, Fin fo
Well, look, I I felt that it's sort of a tradition, I think, of Nobel Prize lectures that you're supposed to be a little bit provocative, and I wanted to follow that tradition. What I was talking about there is if you take a step back and you look at, all the work that we've done, especially with the AlphaX projects, so I'm thinking AlphaGo, of course, AlphaFold. What they really are is we're building models of very combinatorially high dimensional spaces that, you know, if you try to brute force a solution, find the best move and go, or find the the exact shape of a protein, and if you enumer
you think because you're also a fan of theoretical computer science and complexity, do you think we can come up with a kinda complexity class, like a complexity zoo type of class where maybe it's the set of learnable systems, the set of learnable natural systems, l n s? Yeah. This is a I know. New class of systems that could be actually learnable by classical systems in this kind of way, natural systems that can be, modeled efficiently. Yeah.
I mean, I'm I've always been fascinated by the p equals m p question and what is modelable by classical systems, I. E. Non quantum systems, you know, Turing machines in effect. And that's exactly what I'm working on actually in kind of my few moments of spare time with a few colleagues about is should there be, you know, maybe a new class of problem that is solvable by this type of neural network process and kind of mapped onto these natural systems. So, you know, the things that exist in physics and have structure. So I think that could be a very interesting, new way of thinking about it. And
So when you think of the universe as an informational system, then the p equals n p question is a is a physics question. That's right. And it's a question that can help us actually solve the entirety of this whole thing going on. Yeah.
I think it's one of the most, fundamental questions actually if you think of physics as informational, and and the answer to that, I think, is gonna be, you know, very enlightening.
More specific to the p n NP question, this again, some of the stuff we're saying is kinda crazy right now, just like the Christian, the Nobel Prize speech controversial thing that he said sounded crazy, and then you went and got a Nobel Prize for this with John Jumper, solved the problem. So let me let me just stick to the p equals m p. Do you think there's something in this thing we're talking about that could be shown if you get can do something like, polynomial time or constant time compute ahead of time and construct this gigantic model, then you can solve some of these extremely difficult
Yeah. I think that there are actually a huge class of problems that could be couched in this way, the way we did AlphaGo and the way we did AlphaFold where, you know, you you model what the dynamics of the system is, the the the the properties of that system, the environment that you're trying to understand, and then that makes the sear
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