Robert Lange: prediction

Transcript context

…Very cool. And stepping stone collection. So that this is it came from Kenneth Stanley. It's a wonderful paper, Why Greatness Cannot Be Planned. And he said that it's it's better to have systems that don't converge. So in natural evolution, we are just trying all of these different things And greatness quite often follows a diverse path, which means you have to do things which initially seem quite stupid. And then later on, they turn out to be incredibly useful. Yeah. We're trying to design algorithms that can kind of allow for a population of slightly weird things. And and then we kind of lock in and and converge a little bit. So we we're still converging though. So we're still building systems that don't diverge forever. What are we losing? 1 1 thing I find extremely important after having done Schenker evolve is sort of this problem problem. Right? So with all of these systems so far, maybe except for the AI scientist, which we can also talk about, the problem is given. Right? So you have an evaluator, you have a correctness checker, and you sample programs only on that single problem. Right? But oftentimes, innovation for a specific problem might require first inventing a different problem. Right? So for example, I think in the matrix multiplication result that the alpha evolved people show, you can recursively apply sort of the algorithm to larger matrices, so it's actually an important result. Right? But sort of automatically coming up with this reduction or in like this, let's say, recursive nature of problem solving is something these systems right now not necessarily have built in intrinsically. Right? So I think going forward, it's gonna be really important to not only sort of do open ended, let's say, optimization of solutions, but sort of do the co evolution of problem and solution together in order to collect even more diverse stepping stones and to really kick off this this open ended process. Because also to me, like, of the the big life goals or achievements I would wanna see is really having a process that can run not only for, let's say, a week or many weeks, but, like, for years even potentially. Right? Collecting even more diverse interesting stepping stones. Yeah. I spoke to Joel Lemmon and he was talking about the and uncertainty, which is that machine learning algorithms aren't very good with unknown unknowns. And and in a sense, the unknown unknown is talking about these these stepping stones that might be useful later. Mhmm. And when we run these algorithms at the moment, it's the same with LLMs and reasoning systems, is that they're very, very good when we give them a specific thing. Mhmm. And what you're pointing to is we might need to invent a new unrelated problems and find the solutions which might then be related to what we're trying to do. So that feels like a bit of a catch 22 situation. Mhmm. Right? So we're saying, you know, circle packing. Mhmm. Here's my evaluation function, and I want you to sort of diversify and then, you know, kind of and then converge towards the solution. Mhmm. It's just I I had the same thought with Genie, by the way, that it it gives you exactly what what you ask for. So you put put a prompt in on, know, like a Swiss lake with, you know, with boats on the water and mountains on the side. And I was thinking, where are the birds? Oh, I forgot to put birds in the prompt. Mhmm. Right? So how can we meaningfully build systems that actually kind of bring in other unknown things that might be useful?…

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