Earlier quoted context omitted.
You didn't really address what og_kalu brought up. Which is that, it's possible that the model learns human like thinking, because that's the best way to accurately predict the human response itself. I generally agree with you, but still, I do think that this is the current question. What it is the model is learning that it then uses for predictions? Because you're assuming it's learning some purely token correlation…
I wasnt getting the sense it was worthwhile to engage, as my views werent being accurately understood. By I can address this. The meaning of words is, roughly, states of the world. If I say, "pass me the salt" that is satisfied if you, in fact, pass me the salt. If I say, "that tree is green" this is true if that tree which we are both talking about has the property of causing a perceptual state "seeming green" in bo…
You claim the data isn't there to learn human like thinking. But it's not substantiated. It's possible the sum total of all human writing does encode the core reasoning logic and function of humans, and that ML models could learn it from that.
You also seem to claim that without agency you cannot have real intelligence or human reasoning. But AI can be given agency, and it's already being worked on. And when it comes to tastes, convictions, etc., it's also something you can impart by just randomly seeding the AI a particular way which leans it towards certain preferences.
I do agree with you, the current AI models don't have our capacity to have a self driven learning process. They can't think of experiments to conduct to gather the missing data they think would help them know things with more certainty. We're able to learn and infer concurrently, and the two feed into each other almost in real time, and we have the capability to look for data, test hypothesis, etc., all in real time again.
The part I'm not convinced here either though, is that this can't be achieved with LLMs either.