I currently think broad technical knowledge is the main requisite, and I think self-study can suffice for the large majority of that in principle. The main failure mode I see would-be autodidacts run into is motivation, but if you can stay motivated then there’s plenty of study materials.
For practice solving novel problems, just picking some interesting problems (preferably not AI) and working on them for a while is a fine way to practice.
Why not AI? Is it that AI alignment is too hard? Or do you think it’s likely one would fall into the “try a bunch of random stuff” paradigm popular in AI, which wouldn’t help much in getting better at solving hard problems?
What do you think about the strategy of instead of learning a textbook e.g. on information theory, or compilers you try to write the textbook and only look at existing material if you are really stuck. That’s my primary learning strategy.
It’s very slow and I probably do it too much, but it allows me to train to solve hard problems that aren’t super hard. If you read all the text books all the practice problems remaining are very hard.
My POV is you are either hitting the hard core problems, in which case you aren’t practising, you’re trying to do the real thing, or you are advancing AI capabilities by solving some other problem, which is bad given the current strategic situation.
Write the textbook is an interesting study strategy. It’s impossible with math though, in which each chapter might be the entire life’s work of multiple a skilled mathematician. This is probably also true of other fields.
The idea is that you write the textbook yourself until you have aquired all the skills about doing original thinking. It’s not about never looking up things. Though aquiring the skill of thinking by reinventing things seems better, because the research frontier has much harder problems. So hard that they are not the right difficulty to efficiently learn the skill of “original problem solving”.
(That broad technical knowledge is the main thing (as opposed to tacit skills) why you value a physics PhD is a really surprising response to me, and seems like an important part of the model that didn’t come across from the post.)
I currently think broad technical knowledge is the main requisite, and I think self-study can suffice for the large majority of that in principle. The main failure mode I see would-be autodidacts run into is motivation, but if you can stay motivated then there’s plenty of study materials.
For practice solving novel problems, just picking some interesting problems (preferably not AI) and working on them for a while is a fine way to practice.
Why not AI? Is it that AI alignment is too hard? Or do you think it’s likely one would fall into the “try a bunch of random stuff” paradigm popular in AI, which wouldn’t help much in getting better at solving hard problems?
What do you think about the strategy of instead of learning a textbook e.g. on information theory, or compilers you try to write the textbook and only look at existing material if you are really stuck. That’s my primary learning strategy.
It’s very slow and I probably do it too much, but it allows me to train to solve hard problems that aren’t super hard. If you read all the text books all the practice problems remaining are very hard.
My POV is you are either hitting the hard core problems, in which case you aren’t practising, you’re trying to do the real thing, or you are advancing AI capabilities by solving some other problem, which is bad given the current strategic situation.
Write the textbook is an interesting study strategy. It’s impossible with math though, in which each chapter might be the entire life’s work of multiple a skilled mathematician. This is probably also true of other fields.
The idea is that you write the textbook yourself until you have aquired all the skills about doing original thinking. It’s not about never looking up things. Though aquiring the skill of thinking by reinventing things seems better, because the research frontier has much harder problems. So hard that they are not the right difficulty to efficiently learn the skill of “original problem solving”.
(That broad technical knowledge is the main thing (as opposed to tacit skills) why you value a physics PhD is a really surprising response to me, and seems like an important part of the model that didn’t come across from the post.)