This seems to be arguing that the big labs are doing some obviously-inefficient R&D in terms of advancing capabilities, and that government intervention risks accidentally redirecting them towards much more effective R&D directions. I am skeptical.
If such training runs are not dangerous then the AI safety group loses credibility.
It could give a false sense of security when a different arch requiring much less training appears and is much more dangerous than the largest LLM.
It removes the chance to learn alignment and safety details from such large LLM
I’m not here for credibility. (Also, this seems like it only happens, if it happens, after the pause ends. Seems fine.)
I’m generally unconvinced by arguments of the form “don’t do [otherwise good thing x]; it might cause people to let their guard down and get hurt by [bad thing y]” that don’t explain why they aren’t a fully-general counterargument.
If you think LLMs are hitting a wall and aren’t likely to ever lead to dangerous capabilities then I don’t know why you expect to learn anything particularly useful from the much larger LLMs that we don’t have yet, but not from those we do have now.
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.)
This seems to be arguing that the big labs are doing some obviously-inefficient R&D in terms of advancing capabilities, and that government intervention risks accidentally redirecting them towards much more effective R&D directions. I am skeptical.
I’m not here for credibility. (Also, this seems like it only happens, if it happens, after the pause ends. Seems fine.)
I’m generally unconvinced by arguments of the form “don’t do [otherwise good thing x]; it might cause people to let their guard down and get hurt by [bad thing y]” that don’t explain why they aren’t a fully-general counterargument.
If you think LLMs are hitting a wall and aren’t likely to ever lead to dangerous capabilities then I don’t know why you expect to learn anything particularly useful from the much larger LLMs that we don’t have yet, but not from those we do have now.
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.)