In the brain, the same circuitry that is used to solve vision is used to solve most of the rest of cognition - vision is 10% of the cortex. Going from superhuman vision to superhuman Go suggests superhuman anything/everything is getting near.
The reason being that strong Go requires both deep slow inference over huge data/time (which DL excels in, similar to what the cortex/cerebellum specialize in), combined with fast/low data inference (the MCTS part here). There is still much room for improvement in generalizing beyond current MCTS techniques, and better integration into larger scale ANNs, but that is increasingly looking straightforward.
It's tempting to assume that the "keystone, foundational aspect" of intelligence is learning essentially the same way that artificial neural networks learn.
Yes, but only because "ANN" is enormously broad (tensor/linear algebra program space), and basically includes all possible routes to AGI (all possible approximations of bayesian inference).
But humans can do things like "one-shot" learning, learning from weak supervision, learning in non-stationary environments, etc. which no current neural network can do, and not just because a matter of scale or architectural "details".
Bayesian methods excel at one shot learning, and are steadily integrating themselves into ANN techniques (providing the foundation needed to derive new learning and inference rules). Progress in transfer and semi-supervised learning is also progressing rapidly and the theory is all there. I don't know about non-stationary as much, but I'd be pretty surprised if there wasn't progress there as well.
Thus I think it's fair to say that we still don't know what the foundational aspects of intelligence are.
LOL. Generalized DL + MCTS is - rather obviously - a practical approximation of universal intelligence like AIXI. I doubt MCTS scales to all domains well enough, but the obvious next step is for DL to eat MCTS techniques (so that super new complex heuristic search techniques can be learned automatically).
In the brain, the same circuitry that is used to solve vision is used to solve most of the rest of cognition
And in a laptop the same circuitry that it is used to run a spreadsheet is used to play a video game.
Systems that are Turing-complete (in the limit of infinite resources) tend to have an independence between hardware and possibly many layers of software (program running on VM running on VM running on VM and so on). Things that look similar at a some levels may have lots of difference at other levels, and thus things that look simple at some level...
DeepMind's go AI, called AlphaGo, has beaten the European champion with a score of 5-0. A match against top ranked human, Lee Se-dol, is scheduled for March.