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.
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.
This isn’t exactly the same, but nothing in the book Paradigms of Artificial Intelligence would be considered AI today.
Microsoft now calls everything AI (actually mostly "Copilot"). YouTube now calls everything AI (including genuine LLMs and generative features, but also everything it used to call machine learning). Google now calls everything AI (including everything it used to call machine learning). Apple is seemingly the only one immune.
My argument is not that no one ever used "AI" to refer to a product that utilized machine learning, but rather that the term of art in the industry for machine learning itself was actually "machine learning", not "AI", until LLMs took over and made it "AI".
You would not pull a library off the shelf for "AI", it would be for machine learning. You would not implement and perform "AI", but machine learning. Even central parts of the AI ecosystem like PyTorch advertise as being for "deep learning", which is a subset of machine learning. Not "AI".
Machine learning was AI. The specific wording was a branding choice, because "AI" was a deeply stigmatized brand. ( https://en.wikipedia.org/wiki/AI_winter ) But there was not a conceptual division.
There's a close analogue to how modern genetic researchers are happy to tell you that your genome is not informative as to your "race", but it is informative as to your "ancestry".
So machine learning became the marketing.
There's a book from 1995, called Artificial Intelligence: A Modern Approach, by by Stuart Russell and Peter Norvig, which gives the definition "AI is the study of agents that receive percepts from the environment and perform actions." but doesn't define intelligence.
https://aima.cs.berkeley.edu/2nd-ed/preface.html
If we go with that, though, I'd say AI is anything that uses a model to make decisions instead of hand written "if" statements.
> If we go with that, though, I'd say AI is anything that uses a model to make decisions instead of hand written "if" statements.
Hand-written if-statements are a model though...
I would say AI is anything with the intention of performing as a human does. In my opinion, LLMs are AI because they're designed to either perform a human role, or be addressable like a human to a user. (In general I do not agree with implying intelligence by humanity or that it is exclusive to humanity, but it is genuinely the easiest way for me to explain this here.)
Even LLMs that are used as part of harnesses or backend services would count for this, because you could technically replace the model with an equivalent human and they would be able to read and write in its place.
And pre-LLM AI would also count for this, because the whole selling point of AI is that it's like a human in some way or could perform as a human does in some way. Even if it's purely for entertainment value or whatever, if the entertainment value includes that it seems like a human.
But uses of ML that have nothing to do with this would not count, like for example camera image enhancement. You could say a human could sit there and work through some algorithm manually (like from a book, etc) but that's not what image enhancement algorithms are doing. Now if you let an LLM have access to human-UX-ful photo editing tools and let it iterate or etc. then that would be AI but that's another thing.
It's difficult to define precisely exactly what this definition is though. Different people have different ideas of what "performing as a human does" means. If the ML algorithm is executing machine code instructions to perform a task then could a human execute the same machine code instructions by hand to prove that it's AI? No, because the human has to be in place of it, not emulating it. The human would have to take its inputs and give its outputs, without depending on its definition. So, then, it depends on where you define the boundaries of the system. LLMs typically output in tokens, so would a human have to think in tokens? No, because the use of tokens is as a text encoding, and humans are perfectly free to use tools in their work, etc. A human being able to read and write text in English would still prove that a language model that reads and writes the same but in tokens is AI as long as they would be more or less interchangeable.
Of course, today's LLMs are not really true replacements for humans but I'm not talking about performance here, just where they can be positioned. AI to me is more of an intention than a technology -- any technology can be AI depending on how you use it. ML however is a technology, no matter the intention.