I'd be very interested to see how it performs in complex ko fights. I suspect we'll get that chance.

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- Brady
Want to see videos of low-dan mistakes and what to learn from them? Brady's Blunders

Will Lee get to practice against it? He may discover its weaknesses. I suspect the kind of Go he usually plays to beat other humans may be quite different to the type of go that will do best against this AI.

Hmmm. I would know more about pro psychology if I were five stones stronger.

Some pros wouldn't want to change their personal style just "to win". Striking comment from Ishida when he first beat Rin to become Honinbo, that he had had to adjust his style and that wasn't too satisfactory. An underdog thing, maybe.

Prepared variations? Quite in the Korean repertoire, and there are zillions of unorthodox ways to play.

Wacky openings or lots of ko* were some of the sort of things I was thinking of, but there could be other subtle changes in style which cannot readily be put into words or understood by weaker players. I recall one of the earlier neural net bots had a strange weakness in one game in that it had captured some stones with the crane's nest tesuji, but when its opponent made what should have been a futile attempt to escape the neural net it let them do so, presumably because the training data of expert games did not feature these futile attempts and so it didn't know how to continue correctly.

* I asked one of the people involved:
Andrew Simons: Does it have any expert knowledge or special approach to deal with ko? That's something many bots struggle with. And congratulations on a remarkable achievement!
Lucas Baker: Ko is actually not too hard given good search! But some expert knowledge programming in general is definitely required.

I find myself thinking that if Google can analyze go to this depth then just think what they can do with all the data they collect against you every day when you use any of their products.

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Still officially AGA 5d but I play so irregularly these days that I am probably only 3d or 4d over the board (but hopefully still 5d in terms of knowledge, theory and the ability to contribute).

Is it just me or does that seem like a pretty bad dataset to try to beat Lee Sedol with?

To this layman, almost as surprising as the main news is the claim that


Does this mean that such version plays virtually instantly, even on off-the-shelf hardware? It seems hard to believe that such a program, without any tree search, would beat the existing programs that took years to refine.

This is really important, fellow go players. If AlphaGo wins, any player whose game is in that dataset can make the following claim:

"AlphaGo studied my games in order to beat Lee Sedol."

(You may already make this claim about Fan Hui, if you like.)

It will be at least a possibly true statement, in the same way that a member of a firing squad can claim that maybe it wasn't his/her own bullet that made the kill. That tesuji you played in that game a couple of years ago might just push the network over the critical threshold to find the winning move against Lee Sedol or anyone else AlpahaGo plays. (It could also be the mistake that was punished by your opponent, but thanks anyway.)

So, thanks, KGS 6-9d players! (Also, some of those datasets contain games that have at least 1 player that is in that range, so maybe weaker players are also included. Bettter check to be sure. )

How much did Google (a huge commercial venture) pay to use the KGS database (a private venture)? It should be enough to pay the KGS owner and developer (WMS) to retire and make all those requested improvements to the server. If Google can play Lee $1M, then it can pay KGS a portion of that for use of its data.

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Still officially AGA 5d but I play so irregularly these days that I am probably only 3d or 4d over the board (but hopefully still 5d in terms of knowledge, theory and the ability to contribute).

A bit beyond my pay grade, but it seems like they are using the games as a "seed" of sorts to train with: https://news.ycombinator.com/item?id=10982243.

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Occupy Babel!

From the Wired article on the subject, it sounds like the bulk of the game data came from allowing the engine to play itself. The article as a whole is a bit sensationalist, but I found that an interesting tidbit.

Also interesting is that no opening book was used. Previously my understanding is all the other computer go attempts have an opening book.

The mention of GPUs intrigues me. Does anyone know whether this means using something like CUDA to distribute their computations. As far as I can see modern NVIDIA cards contain thousands of CUDA cores. So does 8 GPUs represent another 20,000 cores and 176 GPUs represent another 400,000 cores of some kind? Does anyone know how appropriate they would be for the types of computations made by AlphaGo?

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Dave Sigaty
"Short-lived are both the praiser and the praised, and rememberer and the remembered..."
- Marcus Aurelius; Meditations, VIII 21

GPU's are (apparently) particularly well suited to deep learning applications. NVIDIA even has a deep learning SDK. This does use CUDA as part of the library.

They didn't seem to be using cuda to distribute things(may have used it in running the neural nets though). The GPU's mostly seemed to be used to run the neural networks asynchronously while the CPU is doing the rollout. Since the networks results are used in the rollout they need to get those values quickly.

As a snippet while they are describing their search


Also didn't see much mention that 5 informal games were played at the same time and the record for that was 3-2 in favor of alphago.
The informal games had 3 x30 byo-yomi time control. So when time gets tight it starts to struggle a bit.

Regarding the KGS 6-9d training games, in Deepmind's last paper they mentioned that a neural net trained on just the 9d games was better than on the 6-9d games so they probably use different subsets of the KGS games to make different versions of AlphaGo which then play each other and improve through reinforcement learning (a key advance).

This is so exciting! Enough to make me stop lurking here


I even tried my hand at blogging again after a five-year break. Hope I didn't misinterpret too much:
http://www.allaboutgo.com/articles/alph ... -champion/

Suddenly eager to learn more about this!



I wish KGS had more funding, but is the database not publicly accessible to some extent? I imagine it wouldn't be difficult to find a way to bulk download SGF files...

Or maybe WMS is not telling us something

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Publicly available doesn't mean there aren't terms that restrict bulk use (though KGS doesn't have lawyers). I worked on a prototype project that made use of bulk downloaded Google Maps Streetview data that was abandoned partly over legal concerns over the use of that publicly available data. So although I expect Deepmind are legally in the right a degree of moral quid pro quo would be appreciated.

those are all (mostly) just amateur games.
why didn't they use a collection of professional games, such as from GoGoD?

Five minute simplified explanation of neural networks and why you would use GPUs for them:

Neural networks can be visualized as a graph consisting of nodes and edges (connections between nodes). The nodes are some sort of mathematical function like f(x)=m*x+b. That node would have three input edges (x, m and b) it and one output edge. The inputs and outputs are connected to other nodes or can be inputs/outputs to things outside the network.

A neural net will consist of many nodes arranged in interconnected layers. For example, part of a neural net for a go playing program might be a 19x19 grid of nodes, each with an input representing a point on a go board (0, 1 and 2 for empty, black and white). Those 361 nodes performing a mathematical function on individual numeric inputs can also be represented as a single node performing the same function on a 19x19 matrix.

So, actual neural network programs boil down to massive amounts of math on vectors, matrices and higher-dimensional arrays of numbers called tensors. Video games graphics is also a bunch of linear algebra and math on vectors and matrices. And we already have commodity specialized processors (GPUs) for doing exactly that, and they're many times more efficient at it than traditional CPUs.

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KGS 4 kyu - Game Archive - Keyboard Otaku