[mtlb]

The biological neural structures that encode behavior are “trained” through evolution, but even the most advanced animals rely mostly on conditioned (= learned during lifetime) reflexes, and not on the ones “hardcoded” evolutionary.

Certainly not much evolutionary “training” in the human brain has happened in the last 3000 years, yet advancement in our understanding of the world has been plentiful. But human thinking (including rationality, mathematics, etc.) is on a different level to even learned animalistic behavior. Some great apes were taught language and even showed basic abstract conceptual thinking, but were never able to reach the level of 3-5 years old human kids.

The problem with GPTs and other statistical models is that they can learn incredibly complex patterns in anything we can express as bytes, but not learn the simplest concepts of maths despite being trained on the whole corpus of mathematical texts available on the internet, while kids need classes that can be covered in a single textbook to understand them, and adults may need just a textbook for this.

[mewpmewp2]

The claim about those models being "statistical". Why wouldn't you consider human brain to be statistical or animal's brains as "statistical"?

Because in the end human brains as well as any brains it seems they could be thought of statistical results from long periods of training and producing output from input. Where am I wrong?

I assume by statistical you mean that the ending result of state of neurons can be represented as numbers and pathways leading through these as probabilities of going through a certain pathway - but it occurs to me that same is with human brain, no?

[mtlb]

You are not wrong, but too abstract. Saying that brain is statistical and therefore can be represented with statistical model such as artificial neural net is like saying that brain is computational and therefore can be represented with a Turing-complete system, or that brain is physical and can be represented via physical simulation, etc.