Do you care to give an example? Are they more or less well defined than find-the-cat-in-the-picture problem?
> Producing a definitive, objective score for a paper clip collection is very difficult if not impossible.
Erm, producing of objective comparison of relative values of Go board positions is still not possible.
You mean like go over and feed the neighbor's cat while they're on vacation?
How about instead, being able to clean any arbitrary building?
Go isn't remotely similar to the real world. It's a board game. A challenging one, sure, and AlphaGo is quite a feat, but it's not exactly translatable to open ended tasks with variable environments and ill-specified rules (maybe the neighbor expects you to know to water the plants and feed the goldfish as well).
The limiting factor with the neighbors cat is the robotics of having a robust body and arm attachment. We know that the scope of current AI can:
1) Identify a request to feed a cat
2) Identify the cat, cat food and cat's bowl from camera data
3) Navigate an open space like a house
Being able to clean an arbitrary building is also more the challenge of building the robot than the AI identifying garbage on a floor or how to sweep something.
It is not clear there are hard theoretical limits on an AI any more. There are economic limits based on the cost of a programmer's attention. There are lots of hardware limits (including processor power).
For "cat in the picture", every picture must have the cat first identified by a person, so the training set is much smaller, and Google can't throw GPUs at the problem.
The field progresses swiftly. https://arxiv.org/abs/1602.00955
"Finally, it uses a simpler tree search that relies upon this single neural network to evaluate positions and sample moves, without performing any Monte Carlo rollouts."
In this new version, MCTS is not even used to evaluate a position! Speaking as a Go player, the ability for the neural network to accurately evaluate a position without "reading" ahead is phenomenal (again, read the Nature paper last page for details).
You may have a seemingly good position and in two turns it seems that you have lost the game already.
There's no way to train an AI like this for, say, health: We cannot simulate the human body to the level of detail that's required, and we definitely aren't going to be able to do it at the speed required for a system like this for a very long time. Producing a definitive, objective score for a paper clip collection is very difficult if not impossible.
AlphaGo/DeepMind represents a very strong approach to a certain set of well-defined problems, but most of the problems required for a general AI aren't well-defined.