Prioritizing subjects to self-study (advice wanted)
I plan to do some self-studying in my free time over the summer, on topics I would describe as “most useful to know in the pursuit of making the technological singularity go well”. Obviously, this includes technical topics within AI alignment, but I’ve been itching to learn a broad range of subjects to make better decisions about, for example, what position I should work in to have the most counterfactual impact or what research agendas are most promising. I believe this is important because I aim to eventually attempt something really ambitious like founding an organization, which would require especially good judgement and generalist knowledge. What advice do you have on prioritizing topics to self-study and for how much depth? Any other thoughts or resources about my endeavor? I would be super grateful to have a call with you if this is something you’ve thought a lot about (Calendly link). More context: I’m a undergraduate sophomore studying Computer Science.
So far, my ordered list includes:
Productivity
Learning itself
Rationality and decision making
Epistemology
Philosophy of science
Political theory, game theory, mechanism design, artificial intelligence, philosophy of mind, analytic philosophy, forecasting, economics, neuroscience, history, psychology...
...and it’s at this point that I realize I’ve set my sights too high and I need to reach out for advice on how to prioritize subjects to learn!
Pick one (1) technical subject[1]. Read the textbook carefully (maybe take notes). Do all the exercises (or at least try to spend >20 minutes on exercises you can’t solve). Potentially make flashcards. Study those flashcards. Do the real thing.[2]
I regret having spent so much time reading philosophy, and not learning technical subjects. I have gained remarkably little from “learning how to learn” (except the stuff above) or productivity or epistemology (excluding forecasting)[3]. I remember reading about a heuristic (might’ve been on Gwerns site, but I can’t find it right now): Spend 90% of your time on object-level stuff, 9% of time on meta stuff, 0.9% of time on meta-meta stuff, and so on).
Learning forecasting is great. Best learned by doing a thousand forecasts (flows through to probability theory).
I think linear algebra, causal inference or artificial intelligence are good candidates. I am unsure about game theory, it’s been useful only in metaphors in my own life—too brittle and dependent on initial conditions. But in general anything where you can do exercises (so most things from 6.) and have them be wrong or right is good (so stuff like coding is better than math because checking a proof depends on knowing what a good proof looks like).
Prioritizing subjects to self-study (advice wanted)
I plan to do some self-studying in my free time over the summer, on topics I would describe as “most useful to know in the pursuit of making the technological singularity go well”. Obviously, this includes technical topics within AI alignment, but I’ve been itching to learn a broad range of subjects to make better decisions about, for example, what position I should work in to have the most counterfactual impact or what research agendas are most promising. I believe this is important because I aim to eventually attempt something really ambitious like founding an organization, which would require especially good judgement and generalist knowledge. What advice do you have on prioritizing topics to self-study and for how much depth? Any other thoughts or resources about my endeavor? I would be super grateful to have a call with you if this is something you’ve thought a lot about (Calendly link). More context: I’m a undergraduate sophomore studying Computer Science.
So far, my ordered list includes:
Productivity
Learning itself
Rationality and decision making
Epistemology
Philosophy of science
Political theory, game theory, mechanism design, artificial intelligence, philosophy of mind, analytic philosophy, forecasting, economics, neuroscience, history, psychology...
...and it’s at this point that I realize I’ve set my sights too high and I need to reach out for advice on how to prioritize subjects to learn!
Some advice (with less justification):
Pick one (1) technical subject[1]. Read the textbook carefully (maybe take notes). Do all the exercises (or at least try to spend >20 minutes on exercises you can’t solve). Potentially make flashcards. Study those flashcards. Do the real thing.[2]
I regret having spent so much time reading philosophy, and not learning technical subjects. I have gained remarkably little from “learning how to learn” (except the stuff above) or productivity or epistemology (excluding forecasting)[3]. I remember reading about a heuristic (might’ve been on Gwerns site, but I can’t find it right now): Spend 90% of your time on object-level stuff, 9% of time on meta stuff, 0.9% of time on meta-meta stuff, and so on).
Learning forecasting is great. Best learned by doing a thousand forecasts (flows through to probability theory).
I think linear algebra, causal inference or artificial intelligence are good candidates. I am unsure about game theory, it’s been useful only in metaphors in my own life—too brittle and dependent on initial conditions. But in general anything where you can do exercises (so most things from 6.) and have them be wrong or right is good (so stuff like coding is better than math because checking a proof depends on knowing what a good proof looks like).
I predict you won’t finish the textbook. No problem.
I think I learned more from a course on social choice theory than all philosophy from before 1950 I have read.