To all intents and purposes modern AI is pattern matching and good statistical inferences against a semantic or other contextualising model. Sometimes it includes a good trick like shifting phase space, or adopts approaches from linear programming or genetic algorithms or whatever, but back-propagation aside.. What it lacks is "discriminiation" in the sense of telling good facts from bad, and "inductive reasoning" which is so easy to say, but remarkably hard to do (hence, how long it takes us to do it beyond the trivial) -This has to be externally sourced. Systematic bias creeps in. Thus artists are sueing because this amazing "new art" is so highly derivative it points strongly to the hands whose works were "copied" (and I use that word deliberately) to make it. Time after time I see experts point out the GPT productions are word-vomit good, to style but lack substance and are often just plain wrong.
The stories about porn detection training to learn % image hues of skin-tone and little else come to mind. Face recognition is bloody good. I am seriously impressed by face and other specific search term models in image analysis. But its also lacking "the how" here.
Code is somewhat unique in being highly proscribed. That GPT does a good job of finding code examples in SQL does not point to it doing a good job of inductive reasoning of the applicability of maritime law to a specific problem in common carriers, or how velcro works, but causes more deaths than buttons because soldiers need to be silent. It can't "know" these burdens, it can only correlate (or not)
I think it's remarkable but the kind of implications in "transformative" are something people should not expect to see, until there is at least some attempt at science here. For now, its pretty much "shake the box and see what sticks" with some good theories about tuning the models, but no inherent claims to understand "why it works" and most importantly, how it relates to consciousness or intelligence as we implement it in wetware.
I really want to see people expose theory. How does it work? Not "moar GPU" or "Moar data" but how it actually forms output, without highly specific weighted trained processes from humans. What I hear is that training AI on AI outputs is fruitless. To me, thats as good as test as any: when you can use at scale an AI product to inform the training of an unrelated AI system I will be impressed.
Chat Bots are not impressive. TL;DR the turing test is not actually a determinant of anything except "humans can be fooled"
Thats how I see it. I am not in field. Happy to be corrected by people who are. I think people like Hinton are being extremely careful in their choice of words when they speak about AI, and people like Kurzeweil are not. I pay attention to what Geoffrey Hinton says.