I enjoyed this post a lot but in the weeks since reading it, one unaddressed aspect has been bugging me and I’ve finally put my finger on it: the recommendation to “Specialize in Things Which Generalize” neglects the all-important question of “how much?” Put a different way, at least in my experience, one can always go deeper into one of these subjects—probability theory, information theory, etc. -- but doing so takes time away from expanding one’s breadth. Therefore, as someone looking to build general knowledge, you’re constantly presented with the trade-off of continuing to learn one area deeply vs. switching to the next area you’d like to learn.
If I try to inhabit the mindset of the OP, I can generate two potential answers to this quandary, but none of them are super satisfying:
Learn enough to be able to leverage what you’ve learned for novel problems.
Learn enough to be able to build gears-level models using what you’ve learned.
The post mentions a few different use-cases of learned knowledge, and those different use-cases require different depth of study. So one reasonable answer is: figure out what use-case(s) we care about, and study enough to satisfy those.
A different angle: it’s useful to be lazy. Put off learning things until we need them, assuming that we won’t be under too much time pressure later. The problem with that approach is that it won’t be obvious that a particular technique or area or frame is relevant until after we’ve studied it. However, as long as we understand X enough that we can reliably recognize when it applies in the wild, we can safely put off learning more about X until it comes up. So, being able to recognize relevant problems/situations in the wild is the “most important” use-case, in the sense that it’s the use-case which we can’t put off until later.
I enjoyed this post a lot but in the weeks since reading it, one unaddressed aspect has been bugging me and I’ve finally put my finger on it: the recommendation to “Specialize in Things Which Generalize” neglects the all-important question of “how much?” Put a different way, at least in my experience, one can always go deeper into one of these subjects—probability theory, information theory, etc. -- but doing so takes time away from expanding one’s breadth. Therefore, as someone looking to build general knowledge, you’re constantly presented with the trade-off of continuing to learn one area deeply vs. switching to the next area you’d like to learn.
If I try to inhabit the mindset of the OP, I can generate two potential answers to this quandary, but none of them are super satisfying:
Learn enough to be able to leverage what you’ve learned for novel problems.
Learn enough to be able to build gears-level models using what you’ve learned.
The post mentions a few different use-cases of learned knowledge, and those different use-cases require different depth of study. So one reasonable answer is: figure out what use-case(s) we care about, and study enough to satisfy those.
A different angle: it’s useful to be lazy. Put off learning things until we need them, assuming that we won’t be under too much time pressure later. The problem with that approach is that it won’t be obvious that a particular technique or area or frame is relevant until after we’ve studied it. However, as long as we understand X enough that we can reliably recognize when it applies in the wild, we can safely put off learning more about X until it comes up. So, being able to recognize relevant problems/situations in the wild is the “most important” use-case, in the sense that it’s the use-case which we can’t put off until later.