[InCom-0]

Being curious is good ... nothing wrong with that. What I took issue with above is (what I see as) attempt to derail people into low level math when that is not the crux of the question at all.

Also: nobody who wants to run LLMs will write their own matrix multiplications. Nobody doing ML / AI comes close to that stuff ... its all abstracted and not something anyone actually thinks about (except the few people who actually write the underlying libraries ie. at Nvidia).

[antegamisou]

> attempt to derail people into low level math when that is not the crux of the question at all.

Is the barrier to entry to the ML/AI field really that low? I think no one seasoned would consider fundamental linear algebra 'low level' math.

[InCom-0]

What do you mean 'low'? :-)

The barrier to entry is probably epicly high because to be actually useful you need to understand how to actually train a model in practice, how it is actually designed, how existing practices (ie. at OpenAI or wherever) can be built upon further ... and you need to be cutting edge at all of those things. This is not taught anywhere, you can't read about it in some book. This has absolutely nothing to do with linear algebra ... or more accurately you don't get better at those things by understanding linear algebra (or any math) better than the next guy. It is not as if 'If I were better at math, I would have been better AI researcher or programmer or whatever' :-). This is just not what these people do or how that process works. Even the foundational research that sparked rapid LLM development ('Attention Is All You Need' paper) is not some math heavy stuff. The whole thing is a conceptual idea that was tested and turned out to be spectacular.