I think deep learning is both useful and overhyped. If we got in the habit of calling it something like "large-scale parameter fitting by gradient descent", we could talk about it more sensibly.

I have not met any actual deep learning researchers who think they are creating AGI. I think that it's non-experts who write about deep learning research who think that deep learning is creating AGI.

Well, I dunno. Yann Lecun and Yoshua Bengio have both given presentations on moving from existing perception tasks towards human-level AI, focusing on how to do so with neural nets whose parameters are fitted via gradient descent.

And I'm not even sure I would call it "parameter fitting". I'd call it, "continuous circuit searching via gradient descent", because that's all you're doing: searching for a nice analog circuit for your task within a large, continuous space of such circuits.