I find this model pretty fascinating. Said another way, there are two aspects necessary for AGI as you see it: - Independence + continual improvement (it's alw

I find this model pretty fascinating. Said another way, there are two aspects necessary for AGI as you see it: - Independence + continual improvement (it's always either doing something useful whether you tell it to or not) - Reliable, prompt and accurate learning of new material The first one is interesting because it's theoretically possible to try right now (just put an LLM scanning in a background job), but clearly not trivial. But I think the second issue you raise is the more fundamental one. There have been a couple papers I've seen that have demonstrated that most major LLMs struggle with recalling things in a way that makes use of equivalence and substitution -- for example, if I tell GPT that X is Y's son, then if it will know that if I ask who is the father of Y, it understands the answer is X. If it cannot show basic competence in equivalence and substitution, then it begins to break through the 4th wall of a "sentient entity" and phenomenologically reveal its true colors as a stochastic parrot. It's fascinating. There are conceptual issues, as you mention, and they're not just theoretical, but they're very noticeable (for now). Here's the question I would ask you -- do you think even if we're no where near AGI, the world has fundamentally changed since the mass distribution of LLMs? I have felt a noticeable shift as the technology's adoption is beginning to alter how society conceives of and digests media. It feels like a discrete new evolutionary state of the internet. Social media fabric feels like it's in a new phase compared to even a year ago as new weapons of mass production have been released.