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A good amount of what gets into an ML or AI course, or book, is a couple matmuls and inversion.

Bayesian regression is technically analytical, too, I suppose. Really exposes the blurriness of such a vague term as even "machine learning" let alone "AI".

Bayesian regression is often not really considered AI, unless it's incorporated in a more complicated pipeline (e.g. Bayesian optimization). Same goes for linear regression, then: alone it is just a model.

and there are many more things like this. Back in the day "expert systems" were AI. For any given piece of software, it will meet some definition of AI from some time period.

The term AI is about as concrete as "awesome".

Also video game AI’s. I like to use that as a quick test of a definition of “AI” because video game AI’s span a very wide range of levels of sophistication as well as algorithms, many of which look like deterministic decision trees, most of which don’t use any ML or even regressions.

Arguably that’s a different overload of the term “AI” the way it’s used in business, but I think it’s a good reminder that AI as a field has a long history that developed separately from ML and data science.

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