[hintymad]

> It has no real world model, no ability to learn in any but superficial ways

I also think so, and in the meantime I have to admit a lot of people don't learn deeply either. Take math for example, how many STEM students from elite universities truly understood the definition of limit, let alone calculus beyond simple calculation? Or how many data scientists can really intuitively understand Bayesian statistics? Yet millions of them were doing their job in a kinda fine way with the help of the stackexchange family and now with the help of AI.

[Spivak]

Well part of that is because STE folks aren't typically required to take any kind of theoretical maths. It's $Math for Engineers and it eschews theoretical underpinnings for application. I don't think it's any kind of failing, it's just different. My statistics class was a dense treatise in measure theory. Anyone who took the regular stats class is almost surely way better than me at designing an experiment, but I can talk your ear off about Lebesgue measure to basically zero practical end.

[hintymad]

I was not talking about theoretical foundations like Analysis or measure theory, but just basics in college-level math class. There can be other examples. The point is that many people didn’t have intuitive understanding of what they use everyday — in a way they are like AI, only slower and know less than AI