I find this question fascinating and my instinct is kinda yes, but no. I'm very curious what this google AI engineer's experience was like, and maybe the inter

I find this question fascinating and my instinct is kinda yes, but no. I'm very curious what this google AI engineer's experience was like, and maybe the internal models were way less tuned or focused then the models we get, but like, I talk to AI regularly and it feels like a frustratingly on rails experience. Maybe it's my psych background, I dunno, but it really feels almost like an evolution of LISA and not a revolution. Profoundly evolved mind you. But still sort of, I'm not sure that I could tell the difference between a decision tree with a few trillion items and Claude. https://www.scientificamerican.com/article/google-engineer-claims-ai-chatbot-is-sentient-why-that-matters/ https://www.scientificamerican.com/article/google-engineer-c... I think the question is. What is novelty? I think a more important observation may be like. Ok, there's infinite years in front of us. We can keep evolving, will we eventually have AI that can truly zero to one? Sure, but if it's 10,000 years from now. Is this what people are debating when they talk about AGI? I don't think so, I think most of the debates inherently include a years to decadesish timescale and not century or millenia. The more interesting question may be, like when AI does appear to produce novelty, what does that mean? And it may be something like "The density of available information for a subject area - Say mathematics and Computer science, allows AI to fill in gaps that humans may not have yet filled in." On the one hand, that is novelty, on the other hand, it may not transfer to subjects whose training data is quite so generable, in which case, that's not really AGI as I think we hope to define it. I don't think that's what we hope to mean by novelty because for example - I think I read an observation that one reason for the proliferation of simulation theory is that one use case for simulation would be to create world model training data for AI. But the interesting question would be say - if we could only go to a certain depth in physics say. Could we create a simulation rich enough for the model to discover new physics? Probably not, you need the model to train on meatspace, which means more innovations like LHC.