There's a school of thought that says that "People set a bar for AI, and whenever computers achieve that bar it's moved. And this is because people are protective of their intelligence, so they treat anything a computer can do as clearly not *real* intelligence."
And I can understand objections to that - it feels unfair to keep moving the bar. The problem is that lots of people have either no working definition of "intelligence". They have an inductive "feeling" for what intelligence is, and most of the time that works just fine. And it certainly used to feel like, for instance, "being able to beat a human at chess" would require intelligence from a human, so presumably if a computer could do it then it would be an artificial intelligence. So they set the bar wherever it feels like "If the computer can solve this task then surely it must be intelligent".
And actually that's kinda true, if your definition of intelligence is "Can consider lots of possibilities about a chess board, and find the one that's the most effective." The problem is that they then got an "AI" that could only apply its "Intelligence" to chess. And it did't really understand chess, it just had a set of steps to follow that allow it to do well at chess. If you gave the set of steps to a person who had never played chess, and got them to follow the steps then they'd be just as likely to win a game. But they'd have no mental model of chess, because that's not how (most) chess engines work.
And that idea of a mental model is where my definition of intelligence comes from - "The ability to form models from observations and extrapolate from them."
If something is able to form those models and then use them to make predictions, or to analyse new situations, or to extend the models and test them, then it's intelligent. They might be models of how car engines work, or how French works, or how numbers work, or how humans (or their societies) work. Or, indeed, of how to catch updrafts while hunting fieldmice, or where the best grazing is that's safe for your herd, or how to get the humans to deliver the best treats. These are all things that one can create mental models of, and then use those models to understand them and predict how they interact with the world.
I mention this because of the recent excitement about
Large Language Models. The kind of thing which GPT is an example of, and exploded onto the scene with extremely impressive examples of conversational ability. These models are, to put it mildly, incredibly impressive. They were trained on huge amounts of text, and they can do an awesome job of taking a prompt and generating some text which looks (mostly) like a human wrote it. It is, frankly, amazing how well it can do this.
And, as you'd expect, some people have come out and said "If a computer can solve this task then surely it must be intelligent." Particularly because we are very used to judging people's intelligence based on how they write (particularly on the internet, when that's frequently all we have to go on). But "This looks like a person wrote it" is exactly what GPT is designed to do. To
quote François Chollet,
Saying "ChatGPT feels intelligent to me so it must be" is an utterly invalid take -- ChatGPT is literally an adversarial attack on your theory-of-mind abilities.
To be fair, though, LLMs _do_ have models. They make models of what well-written answers to questions look like. Impressively good ones. But that shouldn't be confused with
understanding those questions, or having any kind of model of the world. It's great that, when asked "What does a parrot looks like?", it can say "A parrot is a colorful bird with a distinctive curved beak and zygodactyl feet, which means that they have two toes pointing forward and two pointing backward." - because it knows which words are associated with describing what things look like, what words are associated with the word "parrot", how to structure a sentence, etc. But that doesn't mean it has any idea what a curve actually looks like. The word "curved" means something to you because when you were very young people showed you curves and said the word "curve" enough times that you made the connection between experience and language. LLMs have no experience, they only have language. And no matter how much language you pile onto a model, and how many words you link to each other, if none of them ever link to a real experience of a real thing then there's no "there" there - it's all language games. And that's why these systems will regularly say things with no connection to reality - they don't understand what they're saying, they aren't connected to reality, they're just making sentences that look plausible.
Simply put, LLM is amazing, but it's amazing at understanding language patterns and working out what piece of language comes next to look like a person wrote it. And language is only meaningful if it's connected to concepts, and you connect those by starting with, for instance, experiencing dogs and *then* learning the word "dog". Or experiencing dogs, learning the word "cat" and then being told that dogs are like cats except for certain differences.
However! This doesn't mean that there couldn't be a "there", that a system not unlike an LLM couldn't learn how to interact with the reality, to associate words with physical things, and develop an intelligence that was rooted in understanding of the world. I suspect it will need to be significantly bigger than existing models, and to be able to work with huge amounts of memory in order to store the context that needs for various situations. But the idea of building models based on huge amounts of input, and then extrapolating them is clearly one that's not going anywhere.
In the meantime, I can't give you a better idea of what large language models are, and why they produce the things they do than
this rather wonderful description.
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