Now I’m inclined to think that just automating most of the tasks in ML research and engineering—enough to accelerate the pace of AI progress manyfold—is sufficient.
This seems to assume that human labor is currently the limiting bottleneck in AI research, and by a large multiplicative factor.
That doesn’t seem likely to me. Compute is a nontrivial bottleneck even in many small-scale experiments, and in particular is a major bottleneck for research that pushes the envelope of scale, which is generally how new SOTA results and such get made these days.
To be concrete, consider this discussion of “the pace of AI progress” elsewhere in the post:
That post is about four benchmarks. Of the four, it’s mostly MATH and MMLU that are driving the sense of “notably faster progress” here. The SOTAs for these were established by
MATH: Minerva, which used a finetuned PaLM-540B model together with already existing (if, in some cases, relatively recently introduced) techniques like chain-of-thought
MMLU: Chinchilla, a model with the same design and (large) training compute cost as the earlier Gopher, but with different hyperparameters chosen through a conventional (if unusually careful) scaling law analysis
In both cases, relatively simple and mostly non-original techniques were combined with massive compute. Even if you remove the humans entirely, the computers still only go as far as they go.
(Human labor is definitely a bottleneck in making the computers go faster—like hardware development, but also specialized algorithms for large-scale training. But this is a much more specialized area than “AI research” generally, so there’s less available pretraining data on it—especially since a large[r] fraction of this kind of work is likely to be private IP.)
This seems to assume that human labor is currently the limiting bottleneck in AI research, and by a large multiplicative factor.
That doesn’t seem likely to me. Compute is a nontrivial bottleneck even in many small-scale experiments, and in particular is a major bottleneck for research that pushes the envelope of scale, which is generally how new SOTA results and such get made these days.
To be concrete, consider this discussion of “the pace of AI progress” elsewhere in the post:
That post is about four benchmarks. Of the four, it’s mostly MATH and MMLU that are driving the sense of “notably faster progress” here. The SOTAs for these were established by
MATH: Minerva, which used a finetuned PaLM-540B model together with already existing (if, in some cases, relatively recently introduced) techniques like chain-of-thought
MMLU: Chinchilla, a model with the same design and (large) training compute cost as the earlier Gopher, but with different hyperparameters chosen through a conventional (if unusually careful) scaling law analysis
In both cases, relatively simple and mostly non-original techniques were combined with massive compute. Even if you remove the humans entirely, the computers still only go as far as they go.
(Human labor is definitely a bottleneck in making the computers go faster—like hardware development, but also specialized algorithms for large-scale training. But this is a much more specialized area than “AI research” generally, so there’s less available pretraining data on it—especially since a large[r] fraction of this kind of work is likely to be private IP.)