Demis Hassabis: evaluation

Transcript context

…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 propose what I think is a super interesting conjecture that “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 be included in that? Biology, chemistry, physics, maybe cosmology, neuroscience? What are we talking about? Sure. Well, look, 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 Alpha X projects, so I’m thinking AlphaGo, of course, AlphaFold, what they really are is we are building models of very combinatorially, high dimensional spaces that if you try to brute force a solution, find the best move and go, or find the exact shape of a protein, and if you enumerated all the possibilities, there wouldn’t be enough time in 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 and that guided the search in a smart way and that makes it tractable. So if you think about protein folding, which is obviously a natural system, why should that be possible? How does physics do that? 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 shape them. And if that’s true, then you can maybe learn what that structure is. This perspective I think is a really interesting one. You’ve hinted it at it, which is almost like crudely stated, anything that can be evolved can be efficiently modeled. Think there’s some truth to that?…

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