Of the vast uncertainties and philosophical exercises that we must face to bridge the chasm between where we are now, and where we will be when we understand intelligence, I can take comfort in claiming, with 100% accuracy, that our biology is not based on technology invented by Google in 2017.
Like what?
What specific biological structure in the brain could be doing anything other than producing output as a function of 1) current electrical/chemical/thermal inputs and 2) previous electrical/chemical/thermal inputs?
The way they make LLM solve problems is by adding a lot of logical jumps into its data, or break down different problems etc, and then as it predicts the text it predicts these logical jumps and then solves the problem. That is very different from how humans learn to solve problems, you don't feed them a billion different state transitions they have to encode to be able to navigate math, they learn to become proficient at math from a few hundred to a few thousand examples, that is fundamentally different from how LLM can learn.
That LLM are so slow learners that requires massive amount of data is a big reason its hard to make them smarter, and its caused by them being next token predictors. And the reason humans can learn with so little data is because we are not just next token predictors.
You changed the definition there, for it to be like an LLM it should be:
> transforming an input into an output trying to mimic inputs that part of the brain has previously been exposed to
Anyone can see how that limits you a lot, and why that makes it so much harder for LLM to learn things properly than it is for humans.
Pre-training is just direct mimicry. A pre-trained LLM is very stupid and mostly useless. To become useful they are post-trained with a reward function.