My eternally grumpy point is that if we are on the cusp of human-level AGI, then surely we can make frog-level and fish-level AI right now. But ANN-powered robots (or AI agents in physics-based video games) seem extremely slow and stupid compared to a frog or a fish. And instead of asking difficult scientific questions about what intelligence really means, AI researchers keep shoving human facts and heuristics in LLMs to give an illusion of human-level intelligence. This is precisely the same mistake yesteryear's AI researchers made when developing Lisp expert systems: ignore any tricky science, focus on trivia, tricks, and (most importantly) vibes.[1]

I am not even convinced transformer ANNs are as smart as spiders, and I don't mean the cleverer jumping spiders. Here is my thought experiment: lets say you trained a transformer-powered robo-spider on one gazillion examples of spiderwebs in nature - between rocks, bushes, etc - and verified that in such natural environments you had "superspider" performance (whatever that means). Now test the robo-spider on a pantry, attic, garage, etc. Will the robo-spider be able to reliably spin a functional indoor web as well as a real spider? I doubt it.

I could be wrong of course! Maybe transformers can figure out the underlying geometric/physical principles in a reliable way. But zooming out a bit: despite the success of Be My Eyes / etc, I don't think any of us will live long enough to see an AI replacement for a seeing-eye dog. ("We are very sorry about your mother, there was an edge case where the AI didn't realize that green trucks were dangerous.")

[1] More people should seriously consider that LLMs are similar to Lisp expert systems with an easier user interface, but trading reliability for breadth and ease of development. I use Scheme all the time, clearly Lisp expert systems are useful, as are LLMs. But it is also clear that Lisp will never be a model for human/etc intelligence. See also Drew McDermott's classic paper, "Artificial Intelligence Meets Natural Stupidity" https://dl.acm.org/doi/10.1145/1045339.1045340