I wonder how valuable you find some of the more math/theory focused research directions in AI safety. I.e., how much less impactful do you find them, compared to your favorite directions? In particular,
I'm generally bad at communicating about this kind of thing, and it seems like a kind of sensitive topic to share half-baked thoughts on. In this AMA all of my thoughts are half-baked, and in some cases here I'm commenting on work that I'm not that familiar with. All that said I'm still going to answer but please read with a grain of salt and don't take it too seriously.
...Vanessa Kosoy's learning-theoretic agenda, e.g., the recent sequence on infra-Bayesianism, or her work on traps in RL. Michael Cohen's research, e.g. the paper on imitation learning seems t
I don't think the onus should be on the reader to infer x-risk motivations. In academic ML, it's the author's job to explain why the reader should care about the paper. I don't see why this should be different in safety. If it's hard to do that in the paper itself, you can always e.g. write a blog post explaining safety relevance (as mentioned by aogara, people are already doing this, which is great!).
There are often many different ways in which a paper might be intended to be useful for x-risks (and ways in which it might not be). Often the motivation for... (read more)