Human-driven research is also brute-force but with a more efficient search strategy. One can think of a parameter that represents research-search-space-navigation efficiency. RL-trained agents will inevitably optimize for that parameter. I agree with your statement insomuch as the value of that efficiency parameter is lower for agents than humans today.
It's really hard to imagine that they __won't__ exceed the human value for that efficiency parameter rather soon given that 1. there are plenty of scalar value functions that can represent research efficiency, of which a subset will result in robust training, and 2. that AI labs have a massive incentive to increase their research efficiency overall, along with billions of dollars and really good human researchers working on the problem.
>Human-driven research is also brute-force but with a more efficient search strategy
No it's not. Is there anything to back that up? There's a creative aspect to human research that I've yet to see with gen AI. All it does is regurgitate stuff and get some "new" ideas via the latent space of the distribution it models. But a generative model cannot by definition create anything new. Just estimate its data well enough that it can sample it well enough to fake novelty.
To massively increase the reliability to get GPUs, you can use something like SkyPilot (https://github.com/skypilot-org/skypilot) to fall back across regions, clouds, or GPU choices. E.g.,
$ sky launch --gpus H100
will fall back across GCP regions, AWS, your clusters, etc. There are options to say try either H100 or H200 or A100 or <insert>.
Essentially the way you deal with it is to increase the infra search space.
From what I know this idea underpins a few FAANG-level companies' data transfer systems. OP's value = a simple implementation of the idea that's OSS and applied to AI.
Thanks! We used SkyPilot (an open source cloud GPU worker management tool) to help out with both our small (single node) and large (many node) training runs.
> Its revenue run rate has spiked this year and now sits at around $30 million to $50 million, three sources said — with one noting that it had more that tripled compared to the start of the year.
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