One thing to remember about #ml (and, by extension, #ai) is that it is, at the end of the day, a technique for complex function approximation. No more, no less. Think back to Stone–Weierstrass theorem from the mathematical analysis course, just on a different scale.

It is hard to imagine writing down an analytical definition for the "human speech" function, but, amazingly, we can computationally arrive at something that is behaving very similarly, and we call our latest take at it "Large Language Models". The impressive thing about this is how unimpressive it really is for what it does.

When looking through that lens, it feels kind of silly to ascribe real intelligence to such models, since it's merely an imitation of the original phenomenon. But it does provoke some reflection on what the existence of such approximation tells us about the original.

I think it also indicates the limitations of the current generation of AI techniques: they can achieve great (perhaps arbitrarily great) accuracy when interpolating, that is, when we are working within the information space well-represented in the training dataset.

However, it's much harder to make assertions about extrapolation accuracy the ideas and knowledge not seen by the model before, never mind the ideas completely novel to the humanity entirely. To me this is a hint as to why AI is actually pretty bad at creativity. It's not so much because it's bad at creativity, it's because its extrapolation is rather unlikely to match what humans consider creative.

Does this make #AI useless for any art, or novel research, or other forms of innovation? Not at all, I don't think. For one, all innovation consists of 1% of actually new ideas and 99% of hard and boring implementation/testing/experimental work, and any help with those 99% could still be a massive help. And even within 1%, random flailing of AI models can inspire humans into actually useful ideas :)

All of that it say, AI is just a better brush and it's silly to pretend it doesn't exist.