DeepMind is an algorithm which clearly improves a lot on traditional tree search. But how will a better tree search algorithm lead to AI taking over? Why does winning at Go mean AI is closer to taking over the world than when it won at Chess?
One way of looking at the significance of this is that it might tell us that relatively simple machine learning algorithms can capture key aspects of the versatile human cortical capacity to learn things like Go using sheer pattern recognition. (It's amazing that human visual cortex can do that.) If the human brain were more mystical in its power, then human-level ability to recognize Go patterns wouldn't have been penetrable at all to a comparatively simple neural algorithm like deep learning.
From another standpoint, this could show that we're reaching the point where, when an AI algorithm starts to reach interesting levels of performance, a much-encouraged Google dumps 100,000 tons of GPU time and 5 months of a few dozen researchers' time to improve the algorithm right past the human performance level. In N years from now when it's a more interesting AI system doing more interesting cognition, we could see a more interesting result materialize when a big organization, encouraged by some key milestone, invests 5 months of time and massively more computing power to further improve an AI system.
Really?
Why not?
EDIT: clarified my language to address below reply.
http://arxiv.org/abs/1509.02971
The fact that the (reinforcement) learning problem is hard or not is not directly related to whether the observation and action spaces are discrete or continuous.
- discrete spaces such as atari games and go, - continuous spaces such as driving a car, controlling a robot or bid on a ad exchange.
When you're talking about some other kind of evaluation function, such as Monte Carlo rollouts, you usually prefix that to the tree search (in the case of Monte Carlo rollouts, "Monte Carlo tree search" or MCTS) to indicate that besides the basic fundamental task common to almost all AIs (finding the optimal branches in a decision tree) it functions completely differently from the expert systems.
So is the case with this program, which (in a first pass) approximates subtrees by a trained neural net, rather than Monte Carlo rollouts or an expert system. So using terminology that suggests classical expert system tree search is bound to cause confusion (as you noticed).
Monte Carlo Tree Search was necessary and itself a massive improvement over minimax but not sufficient for creating a Go program to challenge professional players. The true innovation here is the neural networks. Without those networks to guide it AlphaGo plays far worse than existing programs.
The fact that those networks are sufficient is pretty incredible. We already knew that by inventing them we had created a very pure form of pattern recognition, but it's surprising that the pattern recognition coupled with some tree search seems to be all you need to play Go as well as humans.
It's not impressive to you that we've now reproduced a piece of human intelligence, "intuition", which was previously considered out of reach?
What if you train an AI to play an RTS where matter, energy, and time are the resources and the goal is to take over the world?
Heh, nice one.
Is it really all we need? Or it is more that they threw a lot of hardware to it? What if if a part of its efficiency is because they threw a lot of GPUs with a huge network, rather than having a NN efficient by itself?
We see that: "AlphaGos Elo when it beat Fan Hui was 3140 using 1202 CPUs and 176 GPUs. Lee Sedol has an equivalent Elo to 3515 on the same scale (Elos on different scales aren't directly comparable). For each doubling of computer resources AlphaGo gains about 60 points of Elo."
It's a lot of hardware.
It is the same question for the data they used. Facebook, Google and others seem to agree that, at the end, the quantity and quality of data are more important than the algorithm itself. So how much is it at play here? Knowing that will be able to show us why it is performing well and how much we can appreciate their work.
Basically, the ("lots of hardware") distributed implementation gets ~3100 points in the Elo rating against ~2900. ~2900 is still sufficient to win against Fan Hui. So I would say, that yes, this algorithm has most of the merit here.
This 10 years to beat go prediction shows that our time estimates are wildly ignorant.