> Life experience directly prevents application of logic, as we shortcut to our knowledge associated with the word so that we can skip the expensive-and-hard "l

> Life experience directly prevents application of logic, as we shortcut to our knowledge associated with the word so that we can skip the expensive-and-hard "logic" thing Intelligence is prediction, and the simplest kind of prediction, and what literally comes to mind first is "this time will be same as next time", so in many familiar situations we are just reacting rather than reasoning/planning. It's when what comes to mind first doesn't work (we try it), or we can see the flaw without even trying, that we need to stop and think (reason), such as "hmm... how can I get this stuck lid off the jar - what do I have that can help?". > If the missing gap was as you say, then approaches like Cyc's from 40 years ago would be highly effective CYC vs LLM is an interesting comparison, and one that I've also made myself, but of course there are differences as well as similarities. The similarity is that both are rules based systems of sorts (maybe we could even regard an LLM as an expert system over the domain of natural language), and in both cases there is the wishful thinking that "scale it up and it'll become sentient and/or god-like"! The major difference is that CYC is essentially just using it's rules to perform a deductive closure over it's inputs (what can it deduce from inputs, using multiple applications of rules), whereas the LLM was trained explicitly with a predictive goal, and with it's domain of natural language it's able to predict (recall) human responses and therefore appear intelligent. I think prediction (= intelligence) is the key difference here. An LLM is still limited in it's intelligence/predictive ability, most obviously when it comes to multi-step reasoning, but it's natural language ability and flexible (key-based self-attention) predictive architecture make it quite capable when operating on "in distribution" inputs.