I think what this result says is thus: "Any tasks humans can do, an AI can now learn to do better, given a sufficient source of training data."
Games lend themselves to auto-generation of training data, in the sense that the AI can at the very least play against itself. No matter how complex the game, a deep neural net will find the structure in it, and find a deeper structure than human players can find.
We have now answered the question of, "Are deep neural nets going to be sufficient to match or exceed task-specific human performance at any well-specified task?" with "Yes, they can, and they can do it better and faster than we suspected." The next hurdle - which all the major companies are working on - is to create architectures that can find structure in smaller datasets, less well-tailored training data, and less well-specified tasks.
I think what this result says is thus: "Any tasks humans can do, an AI can now learn to do better, given a sufficient source of training data."
Yes, but that would likely require an extremely large amount of training data because to prepare actions for many kind of situations you'd have an exponential blow up to cover many combinations of many possibilities, and hence the model would need to be huge as well. It also would require high-quality data sets with simple correction signals in order to work, which are expensive to produce.
I think, abov...
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.