> During the match against Fan Hui, AlphaGo evaluated thousands of times
> fewer positions than Deep Blue did in its chess match against
> Kasparov; compensating by selecting those positions more intelli-
> gently, using the policy network, and evaluating them more precisely,
> using the value network—an approach that is perhaps closer to how
> humans play. Furthermore, while Deep Blue relied on a handcrafted
> evaluation function, the neural networks of AlphaGo are trained
> directly from gameplay purely through general-purpose supervised and
> reinforcement learning methodsedit: some actual estimates. Deep Blue had 11.38 GFLOPS[1]. According to the paper in Nature, distributed AlphaGo used 1202 CPUs and 176 GPUs. A single modern GPU can do between 100 and 2000 double precision GFLOPS[2]. So from GPUs alone AlphaGo had access to 4-5 orders of magnitude more computing power than Deep Blue did.
1] https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)
2] https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_proces...
AlphaGo went way beyond that. It actually learned more like how a Go player does. It was able to examine and play a lot of games. That's why it was able to beat a 2p pro, and within less than half a year, challenge a 9p world-class player at least on even terms.
The big thing isn't that AlphaGo is able to play Go at all at that level, but that learned a specific subject much faster than a human.
While it's fun to hate on IBM, it's not really fair to say Deep Blue was throwing hardware at the problem but AlphaGo isn't. Based on the paper AlphaGo will perform much worse in terms of ELO ranking on a smaller cluster.
[0] http://www.economist.com/news/science-and-technology/2169454...
The AlphaGo that beat the 2p European champion five months ago was not as strong as the AlphaGo that beat Lee Sedol (9p). I don't think this was just the AlphaGo team throwing more hardware. I think they had been constantly running the self-training during the intervening months so that AlphaGo was improving itself.
If that is so, then the big thing here isn't that AlphaGo is the first AI to win an official match with the currently world's strongest Go player. It's that within less than half a year, AlphaGo was able to learn and grow to go from challenging a 2p to challenging the world's strongest player. Think about that.
And AI is just one strand. There are several strands that are as deeply changing, that is happening simultaneously.
I remember someone speaking about the shift between classical hard sci fi and more current sci-fi authors like Neal Stephenson or Peter Hamilton. The classical authors like Heinlein or Asimov might do world building where they just change one thing. What would the world be like if that one thing changed? After a certain point though, things were changing so fast that later authors didn't do that. There were too many things that changed at the same time.
Except if a big solar flare hits us ;}
Various commentators mentioned how both players, human and synthetic, made a few mistakes. Even I caught a slow move made by the AI. So whether Lee Sedol was at the top of his peformance, or not, is a bit of a debate. But the AI was clearly on the same level, whatever that means.
It was an intense fight throughout the game, with both players making bold moves and taking risks. Fantastic show.
The slow move might just mean that this was sufficiently big and safer.
Fan Hui said the machine played extremely consistently 6 months ago. He said playing the computer was "like pushing against a wall" - just very strong, very consistent performance.
Also, the people working on it flat out told the world that today's version of AlphaGo beats October's version literally all the time.
There are different strategies depending upon how much emphasis is placed upon early territorial gains as opposed to "influence" which is used for later later territorial gains.
Similarly, playing "passive" moves that make territory without starting a "fight" versus agressively contesting for every piece of territory available.
I am tremendously unfamiliar with recent A.I developments.
AlphaGo's architecture resembles much closer to how humans think and learn.
I initially learned Go to be able to have some chance of an AI. I then had some transformative experiences that coincided with my early kyu learning of basic Go lessons. On of the big lessons in Go is to learn how to let go of something. Taking solace in anything on the Go board is one of the blocks you work through when you develop as a Go player.
I had already known about two years ago that just the Monte Carlo approach was already scalable. If Moore's Law continues, it was a matter of time before the Monte Carlo approach would start challenging the professional ranks -- it had already gotten to the point where you just needed to throw more hardware at it.
AlphaGo's architecture adds a different layer to it. The Deep Learning isn't quite as flexible as the human mind, but it can do something that humans can't: learn non-stop, 24/7 on one subject. We're seeing a different tipping point here, possibly the same kind of tipping point when we witnessed the web browser back in the early 90s, and the introduction of the smartphone in the mid '00s. This is way bigger (to use a Go terminology) than what happened with chess.