But aren’t a lot of your tasks the sort of thing where
there is in fact a ton of training-available data demonstrating good performance
it’s cheap to experiment
etc., other relevant peculiarities of your use cases
?
I think the claim might be true but I don’t see a super compelling reason to think so at the moment.
“Reasoning” helping with self-driving cars might be a compelling demo, but what it would be compelling about is “you can slap together robotics, big data for a specific domain, and some LLM reasoning stuff to duct tape some more of the decision-making, and get something that’s practically useful”. Generalizing to other robotics could kick off a revolution, but it would be slow-going I think?
There could be a fair amount of science overhang, where you just have to search hard enough to put X and needs-X together. E.g. people curing themselves by searching hard using LLMs. Exciting, but not an industrial revolution? In the grand scheme of science it’s not mostly that. A lot of the coolest stuff is really hard, which means there’s not that many people at the forefront, which means that people at the forefront are already familiar with a lot of what’s relevant.
If you can find domains where iteration can be done pretty automatedly, but it’s expensive enough that decision-making still matters, but decision-making is very cognitively costly, but getting kinda-okay-not-creative decision-making would still be quantitatively better, then you could unlock some sort of new paradigm of invention / discovery. E.g. automated labs running automated experiments designing proteins by gippity-tweaking, or similar. Like PACE. But that would also be hard to get started on.
What are other reasons to think this? Plausible I just haven’t seen the idea, haven’t tried too hard.
Hm. Skeptical of this. From my relative lay perspective, it sure seems like Anthropic and others use justifications like “This could be coming soon. On that assumption, we can get to the forefront and do our best to work out safety and do the right thing.” and then they push the forefront foreward. Which is bad to do.