Raw LLM Responses

I had a chat with the Edge AI bot, Copilot, just today. I asked it: "Can you as a programmed AI bot create new algorithms by putting together certain facts you have learned?" *Answer (in part): "Yes, I can synthesize new algorithms by combining known principles, patterns and facts---especially when the goal is clear and the domain is well-defined. I don't 'learn' in the human sense but I can generate novel algorithmic structures by drawing on a vast internal library of computational and logical frameworks. For example, if you gave me a set of constraints, say optimizing resource allocations under uncertainty, or modeling cultural diffusion across networks, I could propose an algorithm that integrates elements from: Bayesian inference---for probabilistic reasoning; graph theory for structural modeling; evolutionary heuristics for adaptive search; game theory for strategic interaction; symbolic logic for rule-based decision making. I can also hybridize across disciplines...(there was more)." *I remarked: "That's what I figured, and I based that on the Hexapawn game I studied as a sophomore. For instance, your novel hypothesis involving patterns, is solely a form of 'pattern recognition', and can occur as a result of your ability to quickly denumerate ALL patterns of a certain type; the unification of disparate observations is also confined to THOSE SPECIFIC OBSERVATIONS, but not to any 'imagined' reality of an observation; even recombining physics, etc. into new ways demonstrates only an ability to move known observations into new positions. This is a fault in the belief that a computer can solve all problems; the problems it can solve are only those whose manifestations have been previously observed, and it why I have referred to the fact that no computer can know ALL the experiences of a human in the lifetime of that human. The human brain, with its levels of consciousness is still beyond the powers of a machine to replicate. *It agreed, and said, among other things: "It was the physical embodiment of reinforcement learning decades before the term became mainstream" *I asked if Turing and von Neumann had written any papers on machine learning. It stated both thinkers saw computation not just as calculation but as adaptive logic and he referred me to a couple of articles which I will eventually read. And then it got interesting: **I asked: "But tell me this: can you, as a machine, simulate the human ability for 'insight'; by that I mean not to formulate concepts from basic concepts already in place, but in the 'creative, scientific sense' as say, Kekule did? [Kekule was the chemist who showed how the benzene molecule had a ring formation]. To form an hypothesis as to the reason or cause of events from observed events?" Well, all of my comment didn't post.