You should listen better. The University of Edinburgh had an entire Department of Artificial Intelligence when I was an undergrad there in the 1990s, and one of the things it researched was machine learning.
That example kind of illustrates my point though? ML being in an AI book doesn't mean ML is AI, just as being in the dairy section does not make almond milk dairy.
It illustrates that some people can’t distinguish between a useful label by association for the general public, and their own desperate compulsion to litigate hair-splitting category distinctions.
Someone who was truly on the ball on this matter might’ve observed that Edinburgh in the 90s was so balkanized by internecine personality conflicts that most research that might later be strictly labelled “machine learning” actually took place in adjacent units and not directly under the DAIry. But I suppose you haven’t heard that, either.
Exactly what do you think my argument is? It's not "I didn't hear of [anything], therefore it doesn't exist / is not admissible". It's roughly "I didn't see machine learning referred to as 'AI' for years, until LLMs happened, at which point most companies that used to say 'machine learning' started calling the same things 'AI'".
You don't have to be snarky about it. I've heard it's okay to not know things. Is that wrong too?
Also:
> desperate compulsion to litigate hair-splitting category distinctions.
All I'm saying is that neither one is a strict subset of the other. Even though AI and ML are incredibly related to the point of even mostly overlapping in practice, they're not the same thing! AI is an outcome and ML is a mechanism. You can use the mechanism to achieve the outcome, or you can use a different mechanism to achieve the outcome, or you can use the mechanism to achieve a different outcome. That's all. If that's a hair-splitting category distinction to you, then so be it.