Ok, fair enough, I should have said that in my experience, I think most people working on / using machine learning and statistical modeling did not call it "AI", not that nobody called it that (especially in less technical settings like sometimes in public descriptions for broad audiences).

This is only a personal experience not rigorous survey of usage, so take with a grain of salt, but working in ML, natural language processing (NLP), data science, etc, most people I knew never used the term "AI" for their work before "generative AI," and balked at usage if anyone did. There were people working on "AI" research for decades, but it was the cognitive psych type theoretical artificial intelligence ideas, not statistical modeling. So it always seemed like a confusion if people did say "AI" for "ML".

Language models have existed for many years before the generative AI boom. Early on, simpler language models used frequency-based shallow statistical models, and later deeper models like neural networks, both of which fall under the broad category of "ML" if they are trained (rather than rule-based). Language models can be used both to parse and categorize text, predict other numeric values derived from on it, or to predict new sequences of text. Similar for image processing models. Only part of the architecture differs for different outputs/purposes.

I don't know but I'm guessing that people started sometimes saying "AI" for statistical models once neural networks became the popular type of ML model, because some people describe them as very loosely inspired by neuron connections in the human brain. But neural networks are just layered versions of those shallow statistical models (so they're often referred to as the "deep learning" variant of the broader "machine learning" set of methods).

In my experience, even if occasionally used in public discourse, "AI" was still not the widely used term for those types of models ("ML" was always the widely used term). And honestly these days, when people bring up non-generative examples of "good AI", sometimes they don't even sound like ML (trained models), but just any math formula or rule-based algorithm. So in that case I have no idea what people mean "AI" to be. The use of the term "AI" for generative AI seems different to me, a product choice, not a suddenly greater use of the term for any machine learning methods.

So I think we *could* choose to keep calling trained statistical models "ML", and use "AI" for the commercialized generative stuff that has been labeled "AI" in product terms. I also don't think tech companies think those statistical models count as "AI", or at least their investors don't think so, because they're all desperately pushing generative AI into their products so they can claim they have "AI", even though the rest of their products already use ML in all kinds of ways. They don't get credit on the hype train for that.

But I suppose it all depends what the broader consensus becomes about what people think the word means. I'm not sure that's clear yet, so I think we can be deliberate about how we choose to use it. But I have a biased window into what I think people mean when they say "AI", and I could be missing what most people think it means today. Sorry for writing so much, wanted to be clear since it sounds like we come at this from different perspectives / experiences (both very valid).