There isn't really any one "point of it all" for AI. If you ask different people, they'll tell you different things. Some want to replicate human intelligence, which may mean replicating the function of the brain; or not, because maybe we can make a machine behave like a human without it functioning like a brain. Some just want to make computers not stupid. Others want to get computers to exhibit human-like behaviour on a computer in order to understand human-like behaviour in humans.
For example, many of the pioneers of AI (Turing, Shannon, McCarthy, Michie, etc) were interested in chess and board game-playing AI because they thought that a computer playing chess like a human, would tell us something about how humans play chess. And that, in turn would tell us something about how humans think, because it's obvious that humans play chess by thinking about chess (and who knows what else).
It turns out that it's not necessary to think like a human, or to think at all, to play a game or chess, or at least to calculate the best move given a board position. We now have systems that can do it, and that can beat any human in chess. Yet those systems are based on specialised algorithms for board-game playing, that work nothing like humans do with their minds when we play chess, because no human plays chess by "running" alpha-beta minimax and Monte Carlo Tree Search in their head. And so those systems still tell us nothing about how humans play chess, or how humans think (McCarthy was really pissed off about that and he wrote an article blasting the state of AI chess research when Deep Blue beat Kasparov).
And that's because digital computers, and human brains, or human minds, are nothing like each other. So being able to do one thing with a computer tells us nothing about doing that thing with a human brain or mind, and vice-versa.
Which btw also means that we can't really look at human behaviour, and predict from it computer behaviour, just because we see some behaviour in a computer that looks superficially like human behaviour. There is always the question of what the computer is really doing, and whether it is at all like what the human is doing, and the answer to that is, so far, a resounding: no.
And because of all of the above, we learn nothing by simply trying to match ideas and concepts, and reuse terms, that we use to talk about computers, to talk about humans, and the other way around. In turn, when terminology is used in such a free-wheeling manner as in the article above, we learn nothing, because it means nothing. "Recursion" is a thing in computers. It's a different thing in humans. It's clear that the author is trying to do the computer-thing of recursion, but it's also clear they're not doing that, at all. And if they were trying to do "recursion" as in humans, then it's clear they're not doing that, either, because they're trying to do it like we do it in computers. So all the author's done is fudge some terminology, bodge together some code and call it "recursion", and achieve nothing but brief internet fame. But I suspect that was the only motivation.
>> I don't think its terribly important that "the model can't do any computation itself, because it's a model and not a computational device."
For me that's the whole point because the only model we have of what minds do is computational. And the only justification anyone can really give for AI, whatever they are trying to achieve with it, is that brains are like computers, minds are like programs, and a Universal Turing Machine can run any program that can be run on any other computing machine, so it should be possible to run the mind-program on a computer-brain.
Which we still don't know how to do in practice. We might not have the right theory of computation. Or we may not have the right kind of computer. Or we may even not have the right kind of mind, or brain. We'll know when we know, if we ever do.
