If you are referring to this:
If we institute a pause, we should expect to see (counterfactually) reduced R&D investment in improving hardware capabilities, reduced investment in scaling hardware production, reduced hardware production, reduced investment in research, reduced investment in supporting infrastructure, and fewer people entering the field.
This seems an extreme claim to me (if these effects are argued to be meaningful), especially “fewer people entering the field”! Just how long do you think you would need a pause to make fewer people enter the field? I would expect that not only would the pause have to have lasted say 5+ years but there would have to be a worldwide expectation that it would go on for longer to actually put people off.
Because of flow on effects and existing commitments, reduced hardware R&D investment wouldn’t start for a few years either. Its not clear that it will meaningfully happen at all if we want to deploy existing LLM everywhere also. For example in robotics I expect there will be substantial demand for hardware even without AI advances as our current capabilities havn’t been deployed there yet.
As I have said here, and probably in other places, I am quite a bit more in favor of directly going for a hardware pause specifically for the most advanced hardware. I think it is achievable, impactful, and with clearer positive consequences (and not unintended negative ones) than targeting training runs of an architecture that already seems to be showing diminishing returns.
If you must go for after FLOPS for training, then build in large factors of safety for architectures/systems that are substantially different from what is currently done. I am not worried about unlimited FLOPS on GPT-X but could be for >100* less on something that clearly looks like it has very different scaling laws.
In terms of the big labs being inefficient, with hindsight perhaps. Anyway I have said that I can’t understand why they aren’t putting much more effort into Dishbrain etc. If I had ~$1B and wanted to get ahead on a 5 year timescale I would give it more probability expectation etc.
For
I am here for credibility. I am sufficiently highly confident they are not X-risk to not want to recommend stopping. I want the field to have credibility for later.
Yes, but I don’t think stopping the training runs is much of an otherwise good thing if at all. To me it seems more like inviting a fire safety expert and they recommend a smoke alarm in your toilet but not kitchen. If we can learn alignment stuff from such training runs, then stopping is an otherwise bad thing.
OK I’m not up with the details but some experts sure think we learnt a lot from 3.5/4.0. Also my belief about it often being a good idea to deploy the most advanced non X-risk AI as defense. (This is somewhat unclear, usually what doesn’t kill makes stronger, but I am concerned about AI companion/romantic partner etc. That could weaken society in a way to make it more likely to make bad decisions later. But that seems to have already happened and very large models being centralized could be secured against more capable/damaging versions.)