As far as I can tell he's the person that people reach for when they want to justify their beliefs. But surely being this wrong for this wrong should eventually lead to losing ones status as an expert.
(em-dash avoided to look less AI)
Of course, the main issue with the field is the critics /should/ be correct. Like, LLMs shouldn't work and nobody knows why they work. But they do anyway.
So you end up with critics complaining it's "just a parrot" and then patting themselves on the back, as if inventing a parrot isn't supposed to be impressive somehow.
Not sure I’d agree that SA has been any more consistently right. You can easily find examples of overconfidence from him (though he rarely says anything specific enough to count as a prediction).
You can see this in this article too.
The real question you should be asking is if there is a practical limitation in LLMs and LRMs revealed by the Hanoi Towers problem or not, given that any SOTA model can write code to solve the problem and thereby solve it with tool use. Gary frames this as neurosymbolic, but I think it's a bit of a fudge.
Must be some sort of cognitive sunk cost fallacy, after dedicating your life to one sect, it must be emotionally hard to see the other "keep winning". Of course you'd root for them to fall.
A LLM with tool use can solve anything. It is interesting to try and measure its capabilities without tools.
I think the second is interesting for comparing models, but not interesting for determining the limits of what models can automate in practice.
It's the prospect of automating labour which makes AI exciting and revolutionary, not their ability when arbitrarily restricted.
It would draw on many previously written examples of algorithms to write the code for solving Hanoi. To solve a novel problem with tool use, one needs to work sequentially while staying on task, notice where you've gone wrong, and backtrack.
I don't want to overstate the case here, I'm sure there is work where there's enough intersection between previously existing stuff in the dataset and few enough sequential steps required that useful work can be done, but idk how much you've tried using this stuff as a labour saving device, there's less low hanging fruit than one might think, but more than zero.
There's a more substantial savings to be had in research scenarios. The AI can read more and synthesize more, and faster, than I can on my own, and provide references for checking correctness.
I'm not confident enough to say that the approaches being taken now have a hard stopping point any time soon or are inherently bound to a certain complexity.
Human minds can only cope with a certain complexity too and need abstraction to chunk details into atomic units following simpler rules. Yet we've come a long way with our limited ability to cope with complexity.
What current models can automate is not what the paper was trying to answer.