when I graduated, I was very keen to get started in an AI safety research group straightaway. But I now think that, for most people in that position, getting 1-2 years of research engineering experience elsewhere before starting direct work has similar expected value
If you’d done this, wouldn’t you have missed out on this insight:
I’d assumed that the field would make much more sense once I was inside it, that didn’t really happen: it felt like there were still many unresolved questions (and some mistakes) in foundational premises of the field.
or do you think you could’ve learned that some other way?
Also, in your case, skilling up in engineering turned out to be less important than updating on personal fit and philosophising. I’m curious if you think you would’ve updated as hard on your personal fit in a non-safety workplace, and if you think your off-work philosophy would’ve been similarly good?
(Of course, you could answer: yes there were many benefits from working in the safety team; but the benefits from working in other orgs – e.g. getting non-EA connections – are similarly large in expectation.)
I do think that this turned out well for me, and that I would have been significantly worse off if I hadn’t started working in safety directly. But this was partly a lucky coincidence, since I didn’t intend to become a philosopher three years ago when making this decision. If I hadn’t gotten a job at DeepMind, then my underestimate of the usefulness of upskilling might have led me astray.
I agree it’s partly a lucky coincidence, but I also count it as some general evidence. Ie., insofar as careers are unpredictable, up-skilling in a single area may be a bit less reliably good than expected, compared with placing yourself in a situation where you get exposed to lots of information and inspiration that’s directly relevant to things you care about. (That last bit is unfortunately vague, but seems to gesture at something that there’s more of in direct work.)
Yepp, I agree with this. On the other hand, since AI safety is mentorship-constrained, if you have good opportunities to upskill in mainstream ML, then that frees up some resources for other people. And it also involves building up wider networks. So maybe “similar expected value” is a bit too strong, but not that much.
If you’d done this, wouldn’t you have missed out on this insight:
or do you think you could’ve learned that some other way?
Also, in your case, skilling up in engineering turned out to be less important than updating on personal fit and philosophising. I’m curious if you think you would’ve updated as hard on your personal fit in a non-safety workplace, and if you think your off-work philosophy would’ve been similarly good?
(Of course, you could answer: yes there were many benefits from working in the safety team; but the benefits from working in other orgs – e.g. getting non-EA connections – are similarly large in expectation.)
I do think that this turned out well for me, and that I would have been significantly worse off if I hadn’t started working in safety directly. But this was partly a lucky coincidence, since I didn’t intend to become a philosopher three years ago when making this decision. If I hadn’t gotten a job at DeepMind, then my underestimate of the usefulness of upskilling might have led me astray.
I agree it’s partly a lucky coincidence, but I also count it as some general evidence. Ie., insofar as careers are unpredictable, up-skilling in a single area may be a bit less reliably good than expected, compared with placing yourself in a situation where you get exposed to lots of information and inspiration that’s directly relevant to things you care about. (That last bit is unfortunately vague, but seems to gesture at something that there’s more of in direct work.)
Yepp, I agree with this. On the other hand, since AI safety is mentorship-constrained, if you have good opportunities to upskill in mainstream ML, then that frees up some resources for other people. And it also involves building up wider networks. So maybe “similar expected value” is a bit too strong, but not that much.