[6gvONxR4sf7o]

> I think it’s worth asking why people feel the need to lie about the future of AI?

I wonder if it's because it's so vague and fuzzy, or because the techniques are so general. Like with self driving cars. The will someday probably be safer than people and that's huge. People get excited. We want that! Self driving cars save lives! But in those four sentences we went from "will someday probably" to let's do it now. The class of thing we're talking about now and the class of thing in the future are one and the same, so it's hard to talk about the future versions as distinctly separate from today's.

AI will probably be able to talk to people well. We have AI today that talks to people. They're not the same thing, but these two sentences don't make that clear, because it's all AI.

In principle, polynomials can learn any function! And we have polynomials today! We can learn anything! Rinse and repeat with fourier series or (as a totally random example) deep learning and it sounds like tomorrow's techniques are the same as today's, so we're done, right?

Or maybe it's on lay people's poor math and stats skills and lack of understanding of the simple stuff. If I tell lay people I do stats, they think I'm taking an average with a lot of bureaucracy they don't really get. They won't think I'm using simple logistic regression to do really cool stuff like classify documents. They didn't know "stats" could do that! So they might be even more misled about what I do if I call it "statistics" than if I call it "AI." If they're mislead whatever I call it, we're already screwed.