Automated / strongly-augmented safety research.
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In light of recent works on automating alignment and AI task horizons, I'm (re)linking this brief presentation of mine from last year, which I think stands up pretty well and might have gotten less views than ideal:
The first automatically produced, (human) peer-reviewed, (ICLR) workshop-accepted[/able] AI research paper: https://sakana.ai/ai-scientist-first-publication/
There have been numerous scandals within the EA community about how working for top AGI labs might be harmful. So, when are we going to have this conversation: contributing in any way to the current US admin getting (especially exclusive) access to AGI might be (very) harmful?
[cross-posted from X]
I find the pessimistic interpretation of the results a bit odd given considerations like those in https://www.lesswrong.com/posts/i2nmBfCXnadeGmhzW/catching-ais-red-handed.
1I also think it's important to notice how much less scary / how much more probably-easy-to-mitigate (at least strictly when it comes to technical alignment) this story seems than the scenarios from 10 years ago or so, e.g. from Superintelligence / from before LLMs, when pure RL seemed like the dominant paradigm to get to AGI.
I pretty much agree with 1 and 2. I'm much more optimistic about 3-5 even 'by default' (e.g. R1's training being 'regularized' towards more interpretable CoT, despite DeepSeek not being too vocal about safety), but especially if labs deliberately try for maintaining the nice properties from 1-2 and of interpretable CoT.
I suspect current approaches probably significantly or even drastically under-elicit automated ML research capabilities.
I'd guess the average cost of producing a decent ML paper is at least 10k$ (in the West, at least) and probably closer to 100k's $.
In contrast, Sakana's AI scientist cost on average 15$/paper and .50$/review. PaperQA2, which claims superhuman performance at some scientific Q&A and lit review tasks, costs something like 4$/query. Other papers with claims of human-range performance on ideation or reviewing also probably have costs of <10$/idea or review.
Even the auto ML R&D benchmarks from METR or UK AISI don't give me at all the vibes of coming anywhere near close enough to e.g. what a 100-person team at OpenAI could accomplish in 1 year, if they tried really hard to automate ML.
A fairer comparison would probably be to actually try hard at building the kind of scaffold which could use ~10k$ in inference costs productively. I suspect the resulting agent would probably not do much better than with 100$ of inference, but it seems hard to be confident. And it seems harder still to be confident about what will happen even in just 3 years' time, given that pretraining compute seems like it will probably grow about 10x/year and that there might be stronger pushes towards automated ML.
This seems pretty bad both w.r.t. underestimating the probability of shorter timelines and faster takeoffs, and in more specific ways too. E.g. we could be underestimating by a lot the risks of open-weights Llama-3 (or 4 soon) given all the potential under-elicitation.