Also, what do you mean by "see the current ML hype as a glass half empty"? I take it that you are also disappointed with the recent research
I'm just getting into the field, but it seems to me at least in computer vision, voice recognition, and text to speech there have been great strides in the recent years
I personally wasn't disappointed — I'm really glad I did this as my undergrad. AI research however tends to go through periods of hype followed by disillusionment. There's a history of promising developments that hit a wall or fizzle out in the long run. That's not to say there hasn't been progress (there's been tons!), but based on track record alone, it's prudent to be skeptical of overly optimistic pronouncements — we're probably much further from the "singularity" than one would think, based on current wave of ML hype anyway.
For your - and my - sake I hope that that is true. If not all bets are off and you might not like the end results.
But all these recent advances are not just hype. It’s real. Anyone who has been following this area for a long time knows that some big problems (like large-scale image classification) have been solved, and in an orderly way that builds on prior work going back to the 1990s and before. (My ML PhD was in 1995.)
Nobody here is referring to the “singularity” - that is obviously speculation that has nothing to do with the CMU program.