Faster tool development as the result of goal-driven search through the space of possibilities. Think something like Ed Boyden’s Tiling Tree Method semi-automated and combined with powerful search. As an intuition pump, imagine doing search in the latent space of GPT-N, maybe fine tuned on all papers in an area’s, embeddings.
Contrary to some of the comments from the talk, I weakly suspect NP-hardness will be less of a constraint for narrow AI scientists than it is for humans. My intuition here comes from what we’ve seen with protein folding and learned algorithms where my understanding is that hardness results limit how quickly we can do things in general but not necessarily on the distributions we encounter in practice. I think this is especially likely if we assume that AI scientists will be better at searching for complex but fast approximations than humans are. (I’m very uncertain about this one since I’m by no means an expert in these areas.)
Great post (or talk I guess)!
Two “yes, and...” add-ons I’d suggest:
Faster tool development as the result of goal-driven search through the space of possibilities. Think something like Ed Boyden’s Tiling Tree Method semi-automated and combined with powerful search. As an intuition pump, imagine doing search in the latent space of GPT-N, maybe fine tuned on all papers in an area’s, embeddings.
Contrary to some of the comments from the talk, I weakly suspect NP-hardness will be less of a constraint for narrow AI scientists than it is for humans. My intuition here comes from what we’ve seen with protein folding and learned algorithms where my understanding is that hardness results limit how quickly we can do things in general but not necessarily on the distributions we encounter in practice. I think this is especially likely if we assume that AI scientists will be better at searching for complex but fast approximations than humans are. (I’m very uncertain about this one since I’m by no means an expert in these areas.)