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bogus comments on [Link] AlphaGo: Mastering the ancient game of Go with Machine Learning - Less Wrong Discussion
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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.
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).
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.
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).
"Enormously broad" is just another way of saying "not very useful". We don't even know in which sense (if any) the "deep networks" that are used in practice may be said to approximate Bayesian inference; the best we can do, AIUI, is make up a hand-wavy story about how they must be some "hierarchical" variation of single-layer networks, i.e. generalized linear models.
Specifically I meant approx bayesian inference over the tensor program space to learn the ANN, not that the ANN itself needs to implement bayesian inference (although they will naturally tend to learn that, as we see in all the evidence for various bayesian ops in the brain) .