Again I do think these things have utility and the unreliability of LLMs is a bit incidental here. Symbolic systems in LISP are highly reliable, but they couldn’t possibly be extended to AGI without another component, since there was no way to get the humans out of the loop: someone had to assign the symbols semantic meaning and encode the LISP function accordingly. I think there’s a similar conceptual issue with current ANNs, and LLMs in particular: they rely on far too much formal human knowledge to get off the ground.
Barring a stunning discovery that will stop putting the responsibility for NN intelligence on synthetic training set – it looks like NN and symbolic AI may have to coexist, symbiotically.
