I don’t know the details of all of these task distributions, but clearly these are not remotely sampled uniformly from the set of all tasks necessary to automate AI R&D?
Yes, in particular the concern about benchmark tasks being well-specified remains. We’ll need both more data (probably collected from AI R&D tasks in the wild) and more modeling to get a forecast for overall speedup.
However, I do think if we have a wide enough distribution of tasks, AIs outperform humans on all of them at task lengths that should imply humans spend 1/10th the labor, but AI R&D has not been automated yet, something strange needs to be happening. So looking at different benchmarks is partial progress towards understanding the gap between long time horizons on METR’s task set and actual AI R&D uplift.
I don’t know the details of all of these task distributions, but clearly these are not remotely sampled uniformly from the set of all tasks necessary to automate AI R&D?
Yes, in particular the concern about benchmark tasks being well-specified remains. We’ll need both more data (probably collected from AI R&D tasks in the wild) and more modeling to get a forecast for overall speedup.
However, I do think if we have a wide enough distribution of tasks, AIs outperform humans on all of them at task lengths that should imply humans spend 1/10th the labor, but AI R&D has not been automated yet, something strange needs to be happening. So looking at different benchmarks is partial progress towards understanding the gap between long time horizons on METR’s task set and actual AI R&D uplift.
(agree, didn’t intend to imply that they were)