I am not AI researcher. (I have a feeling you may have mistaken me for one?)
I don’t expect much gain after first iteration of reflection (don’t know if it was attempted). When calling it recursive I was referring to Alpha Go Zero style of distillation and amplification: We have model producing Q->A, reflect on A to get A’ and update model in direction Q->A’. We got to the state where A’ is better than A, before if tried result probably would be distilled stupidity instead of distilled intelligence.
Such process, in my opinion, is significant step in direction of “capable of building new, better AI” worth explicitly noticing before we take such capability for granted.
Will you keep getting better results in the future as a result of having this done in the past?
Only if you apply “distillation and amplification” part, and I hope if you go too hard in absence of some kind of reality anchoring it may go off the rails and result in distilled weirdness. And hopefully you need bigger model anyway.
Agreed that this seems like the first step in something big. Methods will probably be discovered to operationalize this in a more end-to-end manner given a single prompt/problem, potentially along with compression techniques to distill and analyze many different iterations of a solution before finding and amplifying the right one to the user.
Incidentally, I think this is a pretty good video on these recent developments:
I am not AI researcher. (I have a feeling you may have mistaken me for one?)
I don’t expect much gain after first iteration of reflection (don’t know if it was attempted). When calling it recursive I was referring to Alpha Go Zero style of distillation and amplification: We have model producing Q->A, reflect on A to get A’ and update model in direction Q->A’. We got to the state where A’ is better than A, before if tried result probably would be distilled stupidity instead of distilled intelligence.
Such process, in my opinion, is significant step in direction of “capable of building new, better AI” worth explicitly noticing before we take such capability for granted.
Only if you apply “distillation and amplification” part, and I hope if you go too hard in absence of some kind of reality anchoring it may go off the rails and result in distilled weirdness. And hopefully you need bigger model anyway.
Agreed that this seems like the first step in something big. Methods will probably be discovered to operationalize this in a more end-to-end manner given a single prompt/problem, potentially along with compression techniques to distill and analyze many different iterations of a solution before finding and amplifying the right one to the user.
Incidentally, I think this is a pretty good video on these recent developments: