When I mentioned prompt engineering, I more so meant that people where explaining what to do in a if/else manner to get the LLM to play tiktaktoe (not chain of thoughts or any of those techniques).
In my opinion, learning is both 1) acquiring new skills, and 2) improving upon those skills with repetition. I think it’s very debatable if an LLM could learn something truly novel (or even something like an existing game with some new rules, I.e., chess but with the game pieces in different positions) with in context learning. Secondly, no matter how much you play tiktaktoe with an LLM, it will never improve at the game.
This is just my two cents on why I don’t believe LLMs to fit the criteria of “emerging AGI” that the researchers laid out. Imo I think that to fit that criteria they would need to implement some type of online learning but I definitely could be wrong.
I more so meant that to learn something new the model would have to update its own weights (I have my reasoning for this in another reply in this thread).
When I said “fundamentally unable to” I meant that current LLM architectures do not have the capability to update their own weights (although I probably should’ve worded that a bit differently)