FWIW my vibe is closer to Thane’s. Yesterday I commented that this discussion has been raising some topics that seem worthy of a systematic writeup as fodder for further discussion. I think here we’ve hit on another such topic: enumerating important dimensions of AI capability – such as generation of deep insights, or taking broader context into account – and then kicking off a discussion of the past trajectory / expected future progress on each dimension.
Some benchmarks got saturated across this range, so we can imagine “anti-saturated” benchmarks that didn’t yet noticeably move from zero, operationalizing intuitions of lack of progress. Performance on such benchmarks still has room to change significantly even with pretraining scaling in the near future, from 1e26 FLOPs of currently deployed models to 5e28 FLOPs by 2028, 500x more.
FWIW my vibe is closer to Thane’s. Yesterday I commented that this discussion has been raising some topics that seem worthy of a systematic writeup as fodder for further discussion. I think here we’ve hit on another such topic: enumerating important dimensions of AI capability – such as generation of deep insights, or taking broader context into account – and then kicking off a discussion of the past trajectory / expected future progress on each dimension.
Some benchmarks got saturated across this range, so we can imagine “anti-saturated” benchmarks that didn’t yet noticeably move from zero, operationalizing intuitions of lack of progress. Performance on such benchmarks still has room to change significantly even with pretraining scaling in the near future, from 1e26 FLOPs of currently deployed models to 5e28 FLOPs by 2028, 500x more.