The OP is highlighting maths because deep learning in particular makes use of some light calculus and linear algebra, and the OP is probably mixing together AI, machine learning and Deep learning (as is common today, unfortunately, and I can't blame the OP for that, everyone's doing it).

However, there is a lot more to AI than high-school maths and I don't just meean -more maths. I mean knowledge, lore if you like. It's a field with a long history, stretching back to the 1930's even (before it was actually named as "AI" in Dartmouth, in the 1950's). A lot of very capable people have worked on AI for a very long time and have actually advanced their respective sub-fields each with leaps and bounds and it's not very sensible to expect new leaps while being completely clueless of what has been achived before. You can't stand on the shoulders of giants if you don't know that there are giants and that they have shoulders you can stand on.

Unfortunately, most people who enter the field today know nothing of all that, or even that there was an "all that" before 2012 (if they even know what happened in 2012; and to be honest, one wouldn't understand what 2012 means if one doesn't know what came before). So on the one hand they are not capable of making leaps and on the other hand they don't even know what a leap would look like. And probably think that a "leap" is a 10% improvement of the state of the art for a standard classification benchmark.

I agree with you though that what is needed to make leaps in AI is curiosity. Lots and lots of curiosity. Vast amounts of curiosity. Curiosity of the kind that you only find in people who are a bit zbouked in the head. Or just people who have a lot of time in their hands, to study whatever their fancy tells them to.

So- not the kind of person who flashcards The Deep Learning Book, if nothing else because that means the person doesn't have the time to, you know, actually read the damn book well enough to grokk it.

I mean seriously, what the fuck is it with the bloody flashcards?

I found it to take much less discipline actually. I find an unstructured and curiosity-driven approach to learning math to be much more enjoyable and effective than the typical school approach. You are right about the solitude issue, although I’m unsure about whether this approach to learning is an intrinsically lonely pursuit or if there’s a possible society where it’s not.

I know I’m just speaking from my own experience and what works for me doesn’t necessarily work for everybody. But my claim isn't that everyone should do as I did, my claim is that you're wrong that a self-taught ML researcher would necessarily only be able to make superficial contributions because they are bad at math.