I agree that just because we’ve thought hard and made a big list, doesn’t mean the list is exhaustive. Indeed the longer the list we CAN find, the higher the probability that there are additional things we haven’t found yet...
But I still think having a list would be pretty helpful. If we are trying to grok the shape of the problem, it helps to have many diverse examples.
Re: metacognition: OK, that’s a pretty broad definition I guess. Makes the “why is this important for doing science” question easy to answer. Arguably GPT4 already does metacognition to some extent, at least in ARC Evals and when in an AutoGPT harness, and probably not very skillfully.
ETA: so, to be clear, I’m not saying you were wrong to move from the draft list to making models; I’m saying if you have time & energy to write up the list, that would help me along in my own journey towards making models & generally understanding the problem better. And probably other readers besides me also.
I agree that just because we’ve thought hard and made a big list, doesn’t mean the list is exhaustive. Indeed the longer the list we CAN find, the higher the probability that there are additional things we haven’t found yet...
But I still think having a list would be pretty helpful. If we are trying to grok the shape of the problem, it helps to have many diverse examples.
Re: metacognition: OK, that’s a pretty broad definition I guess. Makes the “why is this important for doing science” question easy to answer. Arguably GPT4 already does metacognition to some extent, at least in ARC Evals and when in an AutoGPT harness, and probably not very skillfully.
ETA: so, to be clear, I’m not saying you were wrong to move from the draft list to making models; I’m saying if you have time & energy to write up the list, that would help me along in my own journey towards making models & generally understanding the problem better. And probably other readers besides me also.