Nobody's on the ball on AI alignment
7 minute read
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Personal summary of Nobody’s on the ball on AGI alignment by Leopold Aschenbrenner.
There are are ~300 alignment researchers vs ~100k capabilities researchers. That’s ~300 times fewer. And according to Aschenbrenner, they’re not even tackling the hardest parts of the problem directly.
Alignment is not on track
Why Aschenbrenner is skeptical about the main research directions:
- Paul Christiano / ARC theory. It’s pure, sophisticated theory, whereas AI alignment is an ML problem, and ML problems are usually solved by dumb hacks.
- Mechanistic interpretability. Might give some useful insight, but it seems very unlikely that we’ll be ready to reverse-engineer full-size models in time.
- RLHF. Probably won’t scale because humans won’t be able to reliably supervise superhuman models.
- Scalable oversight. The closest thing we have to an actual plan, but it’s not clear it’ll work, and this will probably stay ambiguous until we try (so we can’t make informed decisions). Shouldn’t be our plan A.
- MIRI and friends. Too abstract and utterly disconnected from ML.
Scalable alignment is a real problem
You don’t need to believe in crazy x-risk scenarios. It won’t even be easy to figure out if the actions of GPT-7 are going to break the law.
Alignment is a solvable problem
We haven’t put that much effort into it yet, so we don’t have evidence that it’s all that hard (similar to one of the points in Where Christiano disagrees with Yudkowsky).
As AI systems advance, empirical research is getting more realistic. In particular, Aschenbrenner is excited about this style of research (within the “Low-hanging fruit” category in Shlegeris’s talk):
- Come up with some method that might plausibly scale to superhuman models (i.e. it’s unsupervised).
- Try it out on current models.
Better things are possible
We should be putting much more people and money into this!