Specialized AI is all about X,Y pairs. Given X, predict Y. There are other problems it's good at too, like given X, choose a Y to optimize Z, but at it's core it's largely the same. On the fringes, you have stuff about exploration, which is AWESOME, but still pretty niche. At least 99% of the "AI" you hear about is of the X,Y variety. More to your point, if we can make generalized AI from "given X, predict Y," then nobody's figured out how to do it, and nobody has super promising research tracks to get it.
I think a lot of the early AI research (not my specialty) had the idea that if we made a bunch of systems that were good at their own piece of the puzzle, then we could just tack them together and get real intelligence. It just didn't turn out that way. Something I'm more familiar with is graphical models, and while they in principal could do amazing things when you stick little expert components together, we've proved the complexity grows pretty badly in the most general cases that would have been really amazing. I'd bet similar things happened in other "let's put a bunch of specialized systems together" tracks. Maybe we can do it, but not the naive way that would have been great.
Then you can get interesting and philosophical about it, where you might even say that emulating intelligence and intelligence are different. Like the chinese room thing, or even a character in a story vs a physical person. I'd rather not weigh in on that right now, but there are good interesting arguments both ways.