What about when it doesn't work as intended and fails ridiculously, even though it usually works perfectly well?
http://www.labsix.org/physical-objects-that-fool-neural-nets...
Imagine some sort of Robocop deciding to neutralize someone for holding a turtle toy.
Are you serious? You think we're done?
All the tasks humans still earn money doing. And given that we're nowhere near full automation, I'd say it's quite a few tasks.
Industry/Economy lacks behind state-of-the-art technology by decades.
A better example is plumbing. How would you go about automating a human plumber who handles all sorts of piping and crawl spaces in a large variety of settings?
Sure, plumbing is a much more challenging example. But it is an economic problem. The cost to automate plumbing is much higher than the utility of it.
Your comment sounds like Lord Kelvin proclaiming that physics is "over" a couple years before people figured out there were huge holes in the theory which eventually led to quantum mechanics. Our understanding of intelligence is probably less complete than our understanding of physics _back then_.
2. ML is really underwhelming if you measure it against actually intelligent behavior. Figuring out cool regression mechanisms is neat and all, but that's what it is, and it has nothing to do with intelligence in the general sense, much like expert systems had nothing to do with actual domain knowledge, they were just one of the most primitive models, low-hanging fruit that we could exploit.
We did it already. Compter understand language, translate it, react to it. They can recognize items on a picture. Is there a task left which can't be done by computers better and faster than by humans?
>Almost by definition, if we can analytically understand it, it's not going to be interesting enough.
I think current ML is magic. I understand the math behind it. But still, I'm amazed every time when the training is over and it actually works like intended. Everything which is big enough is more than the sum of it's parts.