Kenneth Stanley: evaluation

Transcript context

…Pick breeder, I think what's especially what was at play was like the evolution of evolvability because people are only selecting for what they wanted. Right? What the what looked good. But implicitly, there's also an implicit selection pressure for evolvable things. So if there's, like, 2 versions of the skull, which is 1 is, like, spaghetti and 1 is, like, very modular and composable, after a few generations of evolution, the 1 that's more evolvable will be the 1 that wins out. Right? Just like in natural evolution, the evolution of evolvability. And this evolvability combined with the serendipity is what I think gives you these nice representations. The thing that I think makes this really intriguing is that it gives you something that otherwise could never exist, which is a counterexample that there actually do exist networks that don't have that issue. You would think that that's just intrinsic to neural representation, that somehow they just look like kind of entangled messes, and that's just the way life is. But clearly, it's not how life has to be. This leaves us with a choice, the path of a singular goal orientated kind of optimization, which creates brittle fractured imposters or the path of open ended exploration, which ostensibly creates robust unified models. They argue that this choice fundamentally impacts 3 important things that we want from AI, which is to say generalization, creativity, and continual learning.…

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