One area where I’ve changed my mind compared to my previous beliefs is that I now think for the systems we care about, capabilities either come from a relatively expensive General Purpose Search or something like a mesa-optimizer that does the GPS indirectly, and I no longer believe that mostly imitating humans will be the largest source of AI capabilities with high probability (I’d now put 35% on either a non-mesaoptimizer model or a non-GPS model say automating away AI research as an example with practical compute and data, compared to my over >50% back in 2023.)
Part of this comes down to me believing that AI companies will pay the inefficiency in compute to have models implement General Purpose Search more, and another part of this is I’m much more bearish on pure LLM capabilities than I used to, and in particular I think the reasons why current AIs aren’t mesa-optimizers (for the model-free version of RL) or have a General Purpose Search (for the model-based version of RL) map on pretty well to the reasons why current AIs are much less performant than benchmarks imply, which is that currently in-context learning is way too weak and context windows so far have not been enough to allow the LLM to compound stuff over months or years of thinking, and allow it to deal with problems that aren’t tag-teamable.
More generally, I expect more coherence in AI than I used to, due to my view that labs will spend more on compute to gain semi-reliable insights in AIs.
One area where I’ve changed my mind compared to my previous beliefs is that I now think for the systems we care about, capabilities either come from a relatively expensive General Purpose Search or something like a mesa-optimizer that does the GPS indirectly, and I no longer believe that mostly imitating humans will be the largest source of AI capabilities with high probability (I’d now put 35% on either a non-mesaoptimizer model or a non-GPS model say automating away AI research as an example with practical compute and data, compared to my over >50% back in 2023.)
Part of this comes down to me believing that AI companies will pay the inefficiency in compute to have models implement General Purpose Search more, and another part of this is I’m much more bearish on pure LLM capabilities than I used to, and in particular I think the reasons why current AIs aren’t mesa-optimizers (for the model-free version of RL) or have a General Purpose Search (for the model-based version of RL) map on pretty well to the reasons why current AIs are much less performant than benchmarks imply, which is that currently in-context learning is way too weak and context windows so far have not been enough to allow the LLM to compound stuff over months or years of thinking, and allow it to deal with problems that aren’t tag-teamable.
More generally, I expect more coherence in AI than I used to, due to my view that labs will spend more on compute to gain semi-reliable insights in AIs.