Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games | Lex Fridman Podcast #475

It's hard for us humans to make any kind of clean predictions about highly non-linear dynamical systems. But again, to your point, we might be very surprised what classical learning systems might be able to do about even fluid.

Yes, exactly. I mean, fluid dynamics, Navier–Stokes equations, these are traditionally thought of as very, very difficult, intractable problems to do on classical systems. They take enormous amounts of compute, you know, weather prediction systems. You know, these kind of things all involve fluid dynamics calculations. But again, if you look at something like Veo, our video generation model, it can model liquids quite well, surprisingly well, and materials, specular lighting. I love the ones where, you know, there's, there's people who generate videos where there's, like, clear liquids going through hydraulic presses and then s- being squeezed out. I, I used to write, uh, physics engines and graphics engines in, in my early days in gaming. And I know, uh, it's just so painstakingly hard to build programs that can do that, and yet somehow these systems are, you know, reverse engineering from just watching YouTube videos. So th- presumably what's happening is, it's extracting some underlying structure around how these materials behave. So perhaps there is some kind of lower dimensional manifold that can be learned if we actually fully understood what's going on under the hood. That's maybe, you know, maybe true of most of reality.

The following is a conversation with Demis Hassabis, his second time on the podcast. He is the leader of Google DeepMind and is now a Nobel Prize winner. Demis 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. This is the Lex Fridman Podcast. To support it, please check out our sponsors in the description and consider subscribing to this channel. And now, dear friends, here's Demis Hassabis. In your Nobel Prize lecture, you proposed what I think is a super interesting conjecture, that, quote, "Any pattern that can be generated or found in nature can be efficiently discovered and modeled by a classical learning algorithm." What kind of patterns or systems might in- be included in that, biology, chemistry, physics, maybe cosmology-

Yeah.

... neuroscience? What, what are we talking about?

Sure. Well, look, uh, 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, um, 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 tried to brute force a solution, find the best move in Go, or find the, the exact shape of a protein, and if you enumerated all the possibilities, you'd... there wouldn't be enough time in the, in the, you know, the time of the universe. So, you have to do something much smarter. And what we did in both cases was build models of those environments, um, and that guided the search in a, in a smart way, and that makes it tractable. So if you think about protein folding, which is obviously a natural system, you know, why should that be possible? How does physics do that? You know, proteins fold in milliseconds in our bodies, so somehow physics solves this problem that we've now also solved computationally. And I think the reason that's possible is that in nature, natural systems have structure because they were subject to evolutionary processes that, that shaped them. And if that's true, then you can maybe learn, uh, uh, what that structure is.