I agree with the claim that a little inductive bias has to be there, solely because AIXI is an utterly unrealistic model of how future AIs will look like, and even AIXI-tl is very infeasible, but I think the closer claim that is that the data matters way more than the architecture bias, which does turn out to be true.
One example is that attention turns out to be more or less replacable by MLP mixtures (have heard this from Gwern, but can’t verify this), or this link below:
That sounds more like my intuition, though obviously there still have to be differences given that we keep using self-attention (quadratic in N) instead of MLPs (linear in N).
In the limit of infinite scaling, the fact that MLPs are universal function approximators is a guarantee that you can do anything with them. But obviously we still would rather have something that can actually work with less-than-infinite amounts of compute.
I agree with the claim that a little inductive bias has to be there, solely because AIXI is an utterly unrealistic model of how future AIs will look like, and even AIXI-tl is very infeasible, but I think the closer claim that is that the data matters way more than the architecture bias, which does turn out to be true.
One example is that attention turns out to be more or less replacable by MLP mixtures (have heard this from Gwern, but can’t verify this), or this link below:
https://nonint.com/2023/06/10/the-it-in-ai-models-is-the-dataset/
This is relevant for AI alignment and AI capabilities.
That sounds more like my intuition, though obviously there still have to be differences given that we keep using self-attention (quadratic in N) instead of MLPs (linear in N).
In the limit of infinite scaling, the fact that MLPs are universal function approximators is a guarantee that you can do anything with them. But obviously we still would rather have something that can actually work with less-than-infinite amounts of compute.