Some career do-nothing-but-make-noise in my organization hired a firm to 'Do AI' on some shitty data and the outcome was basically linear regression. It turns out that you can impressive executives with linear regression if you deliver it enthusiastically enough.

This is why I am programming now in Ocaml, files themselves are AI ( ml ).

This is essentially what any relu based neural network approximately looks like (smoother variants have replaced the original ramp function). AI, even LLMs, essentially reduce to a bunch of code like

    let v0 = 0
    let v1 = 0.40978399*(0.616*u + 0.291*v)
    let v2 = if 0 > v1 then 0 else v1

    let v3 = 0
    let v4 = 0.377928*(0.261*u + 0.468*v)
    let v5 = if 0 > v4 then 0 else v4...

Historically this was very much in the field of AI, which is such a massive field that saying something uses AI is about as useful as saying it uses mathematics. Since the term was first coined it's been constantly misused to refer to much more specific things.

From around when the term was first coined: "artificial intelligence research is concerned with constructing machines (usually programs for general-purpose computers) which exhibit behavior such that, if it were observed in human activity, we would deign to label the behavior 'intelligent.'" [1]

[1]: https://doi.org/10.1109/TIT.1963.1057864

I am somewhat cynically waiting for the AI community to rediscover the last half a century of linear algebra and optimisation techniques.

At some point someone will realise that backpropagation and adjoint solves are the same thing.