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Comment by 0x3f | Hacker News Reader

Not sure about that. How would a university test scaling hypotheses in AI, for example? The level of funding required is just not there, as far as I know.

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Universities are also not suited to test which race car is the fastest, but that does not obviate the need for academic research in mechanical engineering.

Perhaps but the fastest race car is not possibly marshalling in the end of human involvement in science, so you might consider these of considerably different levels of meriting the funding.

>marshalling in the end of human involvement in science

Good riddance! But not relevant in the least.

Impact size is not relevant to funding allocation?

Your attempts to smuggle your conclusions into the conversation are becoming tiresome. Profiling a private company's computer program is not impactful research. The best-fit parameters AI people call scaling exponents are not properties like the proton lifetime or electron electric dipole moment. Rest assured, there remain scientists at universities producing important work on machine learning.

There are a million other research things to do besides running huge pretraining runs and hyperparam grid search on giant clusters. To see what, you can start with checking out the best paper and similar awards at neurips, cvpr, iccv, iclr, icml etc.

This issue of accessibility is widely acknowledged in the academic literature, but it doesn’t mean that only large companies are doing good research.

Personally I think this resource mismatch can help drive creative choice of research problems that don’t require massive resources. To misquote Feynman, there’s plenty of room at the bottom

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