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Comment by avar | Hacker News Reader

They've said it doesn't require that, AlphaGo running on a single machine beats the cluster they're using 25% of the time.

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So based on current data, Lee Sedol is exactly as good AlphaGo running on a single machine.

Even on a single machine it has the "memory" of virtually any Go game AlphaGo could be fed.

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.

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/2/1/6/8/21681226/877172...

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.

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.

And it took decades of play for Sedol to become a top player. I find the similarities a mix of satisfying and amusing.

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".

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%.

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