5 ms·

How frigging smart Sedol is?

• Tempest1981 11y ago
  Kind of cool to know you're as smart as algorithms that require 1920 CPUs and 280 GPUs
  • avar 11y ago
    They've said it doesn't require that, AlphaGo running on a single machine beats the cluster they're using 25% of the time.
    • sltkr 11y ago
      So based on current data, Lee Sedol is exactly as good AlphaGo running on a single machine.
      • lettergram 11y ago
        Even on a single machine it has the "memory" of virtually any Go game AlphaGo could be fed.
      • Houshalter 11y ago
        Probably better. If we assume a uniform distribution across possible win rates of Sedol vs AlphaGo, then update it with bayes rule, we get 33% chance that Sedol will win the next match. That's not factoring in other information, like Sedol now being familiar with alphaGo's strategies and improving his own strategies against it. So there is a good chance he is now evenly matched with AlphaGo, and likely much better than the single machine version.
      • emcq 11y ago
        Doubtful; I don't think comparing such small W/L distributions will be illustrative. On the other hand, the Nature paper shows the single 8 GPU machine performs similar to the 64 GPU cluster, but the larger clusters perform a comfortable margin better. [0] By a single machine winning many games relative to the distributed version, really it's just saying that the value/policy network is more important than the monte carlo tree search. The main difference is the number of tree search evaluations you can do; it doesn't seem like they have a more sophisticated model in the parallel version. The figure suggests that there are systematic mistakes that the single 8 GPU machine makes compared to the distributed 280 GPU machine, but MCTS can smooth some of the individual mistakes over a bit. [0] http://www.milesbrundage.com/uploads/2/1/6/8/21681226/8771727_orig.png http://www.milesbrundage.com/uploads/2/1/6/8/21681226/877172...
      • cjbprime 11y ago
        There's a bit of a slight of hand in this statistic -- yes, they can do runtime on a single machine, but it took the compute power of a small country to train the neural nets that are loaded onto that one machine.
        • taneq 11y ago
          That's not really sleight of hand. Lots of things take more energy to produce than to run. It's like saying a 400W electric motor can put out as much power as a fairly fit human, but it's 'sleight of hand' because it took a whole factory to make the motor.
        • Obi_Juan_Kenobi 11y ago
          And it took decades of play for Sedol to become a top player. I find the similarities a mix of satisfying and amusing.
          • massemphasis 11y ago
            You're conveniently forgetting that this "AI" is a representation of tens of millions of amateur plays which is far more than a few decades in total time. Not that Lee Sedol needs someone like me to defend him, but remarks like yours are very misleading. A human is far, far, leagues, more efficient at learning than today's AIs. These AI requires millions of hours of man time of data to even come close to competing at the level of an expert person which did the same, and even arguably far better, in a "few decades".
      • massemphasis 11y ago
        If you look at DeepMind's numbers, AlphaGo is meant to win more than 50% of the time against human players (expert). Right now, my opinion is that its actual capability in wins is less than 50%.
  • CydeWeys 11y ago
    We're in the very infancy of the field though. Give it a year and it's likely you'll have a neural-net-based Go program that runs on a single machine that can defeat every single human professional. Human Go players have been getting better for millennia; don't assume that a program that's only two years old has no room for improvement left. It took some serious hardware for Deep Blue to defeat Garry Kasparov. Now there are smartphone apps with that same level of Chess-playing skill. And if anything the AlphaGo approach is more amenable to running on lower-specced hardware without requiring help from Moore's Law (because you simply train it better).
• massemphasis 11y ago
  It is believed now that Lee Sedol has a really good ability in reading AlphaGo's moves now (see: Micheal Redmond and AGA Game 4 commentary). Game 5 is going to be very interesting. I'm pretty sure the DeepMind team is just as excited. After game 4, they were noticeably far more ecstatic in the after-game press review. Hassabis and Silver kind of reminded me of developers given the details of a bug that was notoriously difficult to find. edit; I can't wait for the reviews of the entire 5 game series. If a book came out I'd very likely buy it. A book discussing both Go and AlphaGo AI at the same time consisting of people among the top of their respective fields would be amazing.