Anything you study for a few months you can become the world's leading expert on. It's a lesson I learned while doing my Ph. D. That's all it takes. After a few weeks, you get to the point where there are only a few others in the world that have read and are able to understand what you've read. A few months on, you are generating new ideas and insights. They might be wrong. But they won't be uninformed.
John Carmack did not start from zero. He already has a firm grasp on algorithms related to linear algebra. Basically machine learning is a whole bunch of matrix manipulation. He's been doing that for 3 decades. The rest is just absorbing concepts about how to apply linear algebra to ML. I'd say he's probably uniquely qualified to really absorb a lot of knowledge quickly on this. It's not about publishing papers, it's about reading and understanding the right papers. I have no doubt he can chew his way through lots of research material in a week or so.
If it is simply about linear algebra, can you please read this ML Paper[1], go through all the proofs and lemmas over a week? You already have a PhD in ML, should be easy. Every kid graduating in STEM understands/should understand linear algebra. Knowing linear algebra is such a low bar.
Frankly, one does not need this paper to get towards the AI. Adam the optimization algo you might need (and even there I am not sure). And it is very readable. The fact that this particular proof of Adam's convergence is complicated is largely irrelevant.
That's not really an argument for why understanding this is needed to move the field forward.
Even your point rests on an assumption, that there's no proof for Adam convergence, that a high school student could understand, which is just a guess at best.
John Carmack did not start from zero. He already has a firm grasp on algorithms related to linear algebra. Basically machine learning is a whole bunch of matrix manipulation. He's been doing that for 3 decades. The rest is just absorbing concepts about how to apply linear algebra to ML. I'd say he's probably uniquely qualified to really absorb a lot of knowledge quickly on this. It's not about publishing papers, it's about reading and understanding the right papers. I have no doubt he can chew his way through lots of research material in a week or so.