This post claims that having the necessary technical skills probably means grad-level education, and also that you should have a broad technical background. While I suppose these claims are probably both true, it’s worth pointing out that there’s a tension between them, in that PhD programs typically aim to develop narrow skillsets, rather than broad ones. Often the first year of a PhD program will focus on acquiring a moderately broad technical background, and then rapidly get progressively more specialized, until you’re writing a thesis, at which point whatever knowledge you’re still acquiring is highly unlikely to be useful for any project that isn’t very similar to your thesis.
My advice for people considering a PhD as preparation for work in AI alignment is that only the first couple years should really be thought of as preparation, and for the rest of the program, you should be actually doing the work that the beginning of the PhD was preparation for. While I wouldn’t discourage people from starting a PhD as preparation for work in AI alignment work, I would caution that finishing the program may or may not be a good course of action for you, and you should evaluate this while in the program. Don’t end up like me, a seventh-year PhD student working on a thesis project highly unlikely to be applicable to AI alignment despite harboring vague ambitions of working in the field.
Strong agree. A lot of the technical material which I think is relevant is typically not taught until the grad level, but that does not mean that actually finishing a PhD program is useful. Indeed, I sometimes joke that dropping out of a PhD program is one of the most widely-recognized credentials by people currently in the field—you get the general technical background skills, and also send a very strong signal of personal agency.
This post claims that having the necessary technical skills probably means grad-level education, and also that you should have a broad technical background. While I suppose these claims are probably both true, it’s worth pointing out that there’s a tension between them, in that PhD programs typically aim to develop narrow skillsets, rather than broad ones. Often the first year of a PhD program will focus on acquiring a moderately broad technical background, and then rapidly get progressively more specialized, until you’re writing a thesis, at which point whatever knowledge you’re still acquiring is highly unlikely to be useful for any project that isn’t very similar to your thesis.
My advice for people considering a PhD as preparation for work in AI alignment is that only the first couple years should really be thought of as preparation, and for the rest of the program, you should be actually doing the work that the beginning of the PhD was preparation for. While I wouldn’t discourage people from starting a PhD as preparation for work in AI alignment work, I would caution that finishing the program may or may not be a good course of action for you, and you should evaluate this while in the program. Don’t end up like me, a seventh-year PhD student working on a thesis project highly unlikely to be applicable to AI alignment despite harboring vague ambitions of working in the field.
Strong agree. A lot of the technical material which I think is relevant is typically not taught until the grad level, but that does not mean that actually finishing a PhD program is useful. Indeed, I sometimes joke that dropping out of a PhD program is one of the most widely-recognized credentials by people currently in the field—you get the general technical background skills, and also send a very strong signal of personal agency.