The bias is on the people asserting people have innate capabilities that are not a derivative of pattern recognition.

You know, not all AI algorithms in use are derivative of statistical curve fitting.

But if you have some more general definition for "pattern recognition" than this, you should be perfectly able to notice that it's more general than what LLMs do.

Give an example of pattern recognition more general than what LLMs do.

The argument is that humans interact with the world across many different modalities and do their statistical learning through this complex of interactions, while LLMs do their statistical learning just by what has been written (by humans) in certain internet sites.

I think it is a quite bold and philosophically poor statement to equate the "human training set" of complex interactions with the environment with what is written on the internet.

You’re arguing that the training set is different. You haven’t identified any different capabilities. What are the capabilities that make humans different?

The training sets are different in nature, not in the sense that 2 different LLMs' training sets are different. And that does not even touch that humans do not just learn from "training sets" but from interacting with the world. More like RL but not like ChatGPT's fine tuning; humans take _actions_ and they _experience_ their results in their totality, not just a "good/bad answer" feedback.

I am not saying that we cannot produce an AI with capabilities of that sort. But LLMs offer nothing at all to that direction. They can be useful in certain practical stuff, they are overhyped as hell, but they are not a step towards AGI.