Around 2000 I took an AI course at the University of Guelph. I don’t think I learned too much. We didn’t talk about neural networks, as far as I can remember. My end of term project was, I think, a pathfinding algorithm wearing an AI costume. There were certainly no discussions of transformers. No CUDA. No PyTorch. None of that existed.
But what I remember doing a lot of was coding in Lisp - a lot of Lisp in the dark University of Guelph CIS lab.
He doesn’t appear to be using any features of Lisp that made it “the language of (70s) AI”. You could do all of this just as easily in many other languages.
His prof was still wrong.
Yeah the intelligence is still in the model. The promise of symbolic AI is about logic programming/ formal semantics not recursive loops.
To a large extent the idea has failed because it proved too hard to get non-experts to represent systems formally.
I still think there’s potential value in a hybrid approach - e.g. get language models to do the representation then let them use formal reasoners/ verification instead of hallucinating.