I guess this perspective is informed by empirical ML / AI safety research. I don’t really do applied math.
For example: I considered writing a survey on sparse autoencoders a while ago. But the field changed very quickly and I now think they are probably not the right approach.
In some sense I think big, comprehensive survey papers on techniques / paradigms only make sense when you’ve solved the hard bottlenecks and there are many parallelizable incremental directions you can go in from there. E.g. once people figured out scaling pre-training for LLMs ‘just works’, it makes sense to write a survey about that + future opportunities.
Quite right. AI safety is moving very quickly and doesn’t have any methods that are well-understood enough to merit a survey article. Those are for things that have a large but scattered literature, with maybe a couple of dozen to a hundred papers that need surveying. That takes a few years to accumulate.
I guess this perspective is informed by empirical ML / AI safety research. I don’t really do applied math.
For example: I considered writing a survey on sparse autoencoders a while ago. But the field changed very quickly and I now think they are probably not the right approach.
In contrast, this paper from 2021 on open challenges in AI safety still holds up very well. https://arxiv.org/abs/2109.13916
In some sense I think big, comprehensive survey papers on techniques / paradigms only make sense when you’ve solved the hard bottlenecks and there are many parallelizable incremental directions you can go in from there. E.g. once people figured out scaling pre-training for LLMs ‘just works’, it makes sense to write a survey about that + future opportunities.
Quite right. AI safety is moving very quickly and doesn’t have any methods that are well-understood enough to merit a survey article. Those are for things that have a large but scattered literature, with maybe a couple of dozen to a hundred papers that need surveying. That takes a few years to accumulate.