AlphaEvolve uses search to push beyond known solutions

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 30:58

   yet get enough attention is AlphaEvolve. We talked about evolution a little bit, but it's the Google DeepMind system that evolves algorithms. Are these kinds of evolution-like techniques promising as a component of future superintelligence systems. So for people who don't know, it's kind of, I don't know if it's fair to say it's LLM guided evolution search. Yeah. Evolutionary algorithms are doing the search and LLMs are telling you where.

2. 31:25

   Yes, exactly. So LLMs are kind of proposing some possible solutions and then you use evolutionary computing on top to find some novel part of the search space. So actually, I think it's an example of very promising directions where you combine LLMs or foundation models with other computational techniques. Evolutionary methods is one, but you could also imagine Monte Carlo tree search, basically many types of search algorithms or reasoning algorithms sort of on top of or using the foundation models as a basis. So I actually think there's quite a lot of interesting things to be discovered probably with these sort of hybrid systems, let's call them.

3. 32:06

   but not to romanticize evolution yeah i'm only human but you think there's some value in whatever that mechanism is because we already talked about natural systems do you think where there's a lot of low-hanging fruit of us understanding being being able to model uh being able to simulate evolution and then using that whatever we understand about that nature inspired mechanism to then do search better and better and better?

4. 32:33

   Yes. So if you think about, again, breaking down the solar systems we've built to their really fundamental core, you've got the model of the underlying dynamics of the system. And then if you want to discover something new, something novel that hasn't been seen before, then you need some kind of search process on top to take you to a novel region of the search space. And you can do that in a number of ways.

5. 33:01

   evolutionary computing is one. With AlphaGo, we just use Monte Carlo Tree Search, right? And that's what found Move 37, the new kind of never seen before strategy in Go. And so that's how you can go beyond potentially what is already known. So the model can model everything that you currently know about, right? All the data that you currently have, but then how do you go beyond that. So that's starts to speak about the ideas of creativity. How can these systems create something new, discover something new? Obviously, this is super relevant for scientific discovery or pushing science and medicine forward, which we want to do with these systems. And you can actually bolt on some fairly simple search systems on top of these models and get you into a new region of space. Of course, you also have to make sure that you're not searching that space totally

6. 33:49

   randomly, it would be too big. So you have to have some objective function that you're trying to optimize and hill climb towards and that guides that search. But there's some mechanism of evolution that are interesting, maybe in the space of programs, but then the space of programs is an extremely important space because you can probably generalize to everything. But, you know, for example, mutation. So it's not just Monte Carlo tree search where it's like a search.

7. 34:16

   you could every once in a while combine things yeah combine things alter like sub like a components of a thing yes so then you know what evolution is really good at is not just the natural selection it's combining things and building increasingly complex hierarchical systems yes so that component is super interesting especially like with alpha evolve in the space of programs yeah exactly so there's a you can get a bit of an extra property out of evolutionary systems, which is some new emergent capability may come about, right? Of course, like happened with life. Interestingly, with naive sort of traditional evolutionary computing methods without LLMs and the modern AI, the problem with them, they were very well studied in the 90s and early 2000s and some promising results. But the problem was they could never work out how to

8. 35:05

   evolve new properties, new emergent properties. You always had a sort of subset of the properties that you put into the system. But maybe if we combine them with these foundation models, perhaps we can overcome that limitation. Obviously, natural evolution clearly did, because it did evolve new capabilities, right? So bacteria to where we are now. So clearly that it must be possible with evolutionary systems to generate new patterns, you know, going back to the first thing we talked about, and new capabilities and emergent properties. And maybe we're on the cusp of discovering how to do that yeah listen uh alpha vol is one of the coolest things i've