[InCom-0]

These are technical details of computations that are performed as part of LLMs.

Completely pointless to anyone who is not writing the lowest level ML libraries (so basically everyone). This does now help anyone understand how LLMs actually work.

This is as if you started explaining how an ICE car works by diving into chemical properties of petrol. Yeah that really is the basis of it all, but no it is not where you start explaining how a car works.

[jasode]

>This is as if you started explaining how an ICE car works by diving into chemical properties of petrol.

But wouldn't explaining the chemistry actually be acceptable if the title was, "The chemistry you need to start understanding Internal Combustion Engines"

That's analogous to what the author did. The title was "The maths ..." -- and then the body of the article fulfills the title by explaining the math relevant to LLMs.

It seems like you wished the author wrote a different article that doesn't match the title.

[InCom-0]

'The maths you need to start understanding LLMs'.

You don't need that math to start understanding LLMs. In fact, I'd argue its harmful to start there unless your goal is to 'take me on a epic journey of all the things mankind needed to figure out to make LLMs work from the absolute basics'.

[bryanrasmussen]

>Completely pointless to anyone who is not writing the lowest level ML libraries (so basically everyone). This does now help anyone understand how LLMs actually work.

maybe this is the target group of people who would need particular "maths" to start understanding LLMS.

[antegamisou]

Find someone on HN that doesn't trivialize fundamental math yet encourages everyone to become a PyTorch monkey that ends up having no idea why their models are shite: impossible.

[49pctber]

Anyone who would like to run an LLM would need to perform their computations on hardware. So picking hardware that is good at matrix multiplication is important for them, even if they didn't develop their LLM from scratch. Knowing the basic math also explains some of the rush to purchase GPUs and TPUs on recent years.

All that is kind of missing the point though. I think people being curious and sharpening their mental models of technology is generally a good thing. If you didn't know an LLM was a bunch of linear algebra, you might have some distorted views of what it can or can't accomplish.

[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).

[saagarjha]

If you're just piecing together a bunch of libraries, sure. But anyone who is adjacent to ML research should know how these work.

[InCom-0]

Anyone actually physically doing ML research knows it ... but doesn't write the actual code for this stuff (or god forbid write some byzantine math notations somewhere), doesn't even think about this stuff except through X levels of higher level abstractions.

Also, those people understand LLMs already :-).

[ivape]

Also, people need to accept that they’ve been doing regular ass programming for many years and can’t just jump into whatever they want. The idea that developers were well rounded general engineers is a myth mostly propagated from within the bubble.

Most people’s educations right here probably didn’t even involve Linear Algebra (this is a bold claim, because the assumption is that everyone here is highly educated, no cap).