The vastness of the training distribution is certainly one feature of the AI situation. But another is an army of human developers of AI, eager to discover what isn’t in the training distribution, and what the AIs can’t currently do, so they can figure out how to give the AIs those new capabilities.
Is there any argument that LLMs, turned into recurrent networks via chain of thought, will still have inherent limitations when compared with humans?
Is there any argument that LLMs, turned into recurrent networks via chain of thought, will still have inherent limitations when compared with humans?
There are various arguments laid out by various people (Ilya, Nathan Lambert, Richard Sutton, Steven Byrnes, Yann LeCunn, Jeremy Howard (this post), Dwarkesh, as well as others many others), which more or less have the vibe of “lack of conceptual innovation/ood generalization/continual learning”. I also personally believe a version of this, but think it’s not as relevant to timelines discussions as one may suspect.
I’d be keen to spell out the case I believe in, as well as my model of the claims around such claims tends to look like. But your question is sufficiently vague such that I don’t have a good understanding of your model and what would be cruxy for you.
P.S. By referring to LLMs as RNNs via chain-of-thought, I’m assuming you’re alluding to the transformers forward pass being in TC^0 and the follow up CoT as P-complete (assuming polynomial # of intermediary tokens) results. But most of the above arguments aren’t blocked by that (i.e. traditional RNNs are also P-Complete but no one argues they’re AGI-complete).
The vastness of the training distribution is certainly one feature of the AI situation. But another is an army of human developers of AI, eager to discover what isn’t in the training distribution, and what the AIs can’t currently do, so they can figure out how to give the AIs those new capabilities.
Is there any argument that LLMs, turned into recurrent networks via chain of thought, will still have inherent limitations when compared with humans?
There are various arguments laid out by various people (Ilya, Nathan Lambert, Richard Sutton, Steven Byrnes, Yann LeCunn, Jeremy Howard (this post), Dwarkesh, as well as others many others), which more or less have the vibe of “lack of conceptual innovation/ood generalization/continual learning”. I also personally believe a version of this, but think it’s not as relevant to timelines discussions as one may suspect.
I’d be keen to spell out the case I believe in, as well as my model of the claims around such claims tends to look like. But your question is sufficiently vague such that I don’t have a good understanding of your model and what would be cruxy for you.
P.S. By referring to LLMs as RNNs via chain-of-thought, I’m assuming you’re alluding to the transformers forward pass being in TC^0 and the follow up CoT as P-complete (assuming polynomial # of intermediary tokens) results. But most of the above arguments aren’t blocked by that (i.e. traditional RNNs are also P-Complete but no one argues they’re AGI-complete).