AlphaGo beats the world champion Lee Sedol in first of five matches

Re:AlphaGo beats the world champion Lee Sedol in first of fi... by gcr on Wednesday March 09, 2016 @01:23PM (#11254660)

This isn't about Moore's Law though. From the AlphaGo paper:

    > 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 methods

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Re:AlphaGo beats the world champion Lee Sedol in first of fi... by rictic on Wednesday March 09, 2016 @03:27PM (#11255510)

My understanding is that it is much more expensive for AlphaGo to evaluate a position than it was for Deep Blue. I'm not certain, but I would be surprised if AlphaGo did not need significantly more computation than Deep Blue.

edit: 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...

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Re:AlphaGo beats the world champion Lee Sedol in first of fi... by hosh on Wednesday March 09, 2016 @03:36PM (#11255568)

A brute force approach for Go doesn't work. It doesn't work for humans (deep reading skills) and doesn't work for computers. The Monte Carlo approach was the first one that allowed Go AIs to scale with the hardware. This was at least two years ago.

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.

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Re:AlphaGo beats the world champion Lee Sedol in first of fi... by rictic on Wednesday March 09, 2016 @03:42PM (#11255603)

Strong agreement that it wasn't purely about computational power, and that there were significant software advances. I just want to make the point that hardware has advanced considerably as well.

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Re:AlphaGo beats the world champion Lee Sedol in first of fi... by emcq on Saturday March 12, 2016 @04:50AM (#11272170)

This 1000 times. Extrapolating Deep Blue's 11GFlop supercomputer to today with Moore's law would be a 70TFlop cluster. AlphaGo is using 1+PFlops of compute (280GPUs referenced for competition in [0]). That's an insane amount of compute.

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...

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Re:AlphaGo beats the world champion Lee Sedol in first of fi... by hosh on Wednesday March 09, 2016 @01:30PM (#11254717)

I know that. You didn't read my comment very thoroughly.

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