
Why Go Still Foils the Computers - prostoalex
http://www.wsj.com/articles/why-go-still-foils-the-computers-1451493424?mod=e2fb
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chc
This article doesn't really explain what the title suggests it will. Instead,
it tells us:

1\. That computers have a hard time with Go.

2\. That many people think Go is deeper than other board games.

3\. That some people are working on making computers better at Go with neural
networks, which more closely resemble the human brains that do so well at Go.

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joe_the_user
Foil is relative. The article doesn't provide much background on contemporary
computer Go.

On Kgs where I play, the highest rated computers are at 5 dan. That is
basically better than 99% of the players on the server which I think is the
largest server in the English speaking world. Computer go has made
considerable strides since 2009 [1]. The main impetus for this has been Monte
Carlo Tree search[2]. Computers have not yet reached the level of professional
Go player unlike Chess but progress from existing methods seems quite
possible.

[1]
[https://en.wikipedia.org/wiki/Computer_Go](https://en.wikipedia.org/wiki/Computer_Go)
[2]
[https://en.wikipedia.org/wiki/Monte_Carlo_tree_search](https://en.wikipedia.org/wiki/Monte_Carlo_tree_search)

~~~
Jach
I read [http://arxiv.org/abs/1412.6564](http://arxiv.org/abs/1412.6564)
earlier this year and tldr they trained a deep net bot that managed to hold
about 1-2d on KGS. Since there's no searching going on, that's pretty good,
though not as good as CrazyStone or other MCTS bots. (I hadn't heard they went
from 6d to 5d, but sure enough
[http://www.gokgs.com/graphPage.jsp?user=CrazyStone](http://www.gokgs.com/graphPage.jsp?user=CrazyStone)
I wonder what happened?) I suspect combining the two methods will yield even
stronger programs, I've heard some folks at Facebook and Google are each
working on their own Go bots based on deep learning. If some company spent a
few years building dedicated hardware like Deep Blue I even bet they could
beat 9d pros with current methods. Go is close to falling.

~~~
cambel
a big, discontinuous jump in rankings like that suggests a couple of things 1)
the bot was taken offline and updated with a different engine hence different
rank and/or 2) KGS recalibrated it's relative kyu/stone ranks (which it does
from time to time)

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paulsutter
Expect something soon from Deepmind

[http://recode.net/2015/11/20/go-is-the-game-machines-cant-
be...](http://recode.net/2015/11/20/go-is-the-game-machines-cant-beat-googles-
artificial-intelligence-whiz-hints-that-his-will/)

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_ak
Because combinatorial explosion?

~~~
eli_gottlieb
Then how do human beings play Go? I've heard a lot of claims about human
intelligence been non-replicable in-silico, but I've never heard the claim
that it can somehow "defeat" a combinatorial explosion of possibilities just
to plan a sales trip or play a board game.

~~~
KirinDave
The answer is actually: We don't know. Some people suggest comllex visual
pattern matching is at play, but this seems really unlikely when you get to
know the space because:

1\. Good Go players can often reconstruct an entire game just by looking at
the board, if they have some idea how it started. Given that good go players
can substantially alter the board in the course of play (called "playing under
the stones" in many books), this suggests more than simple visual recognition.

2\. Some go players can even play "one color go" which is pretty amazing to
watch. Its basically a game of who can keep every move in their head. This
isn't a silly stunt, some people really practice this.

Personally, I think that actually Go is more like a contextual NL problem than
a vision definition problem. The existence of things like "joseki" and the
fact that small board games play out in such a radically different way than
big board games suggests that a variety of human cognitive shortcuts are at
play.

It is absolutely the case that with just a few months of modest practice
almost anyone can beat the pants off the best go playing computers.

~~~
Someone
[https://en.m.wikipedia.org/wiki/Computer_Go#Performance](https://en.m.wikipedia.org/wiki/Computer_Go#Performance):

 _" In 2009, the first such programs appeared which could reach and hold low
dan-level ranks on the KGS Go Server also on the 19x19 board."_

I know virtually nothing about what it takes to become a low-level dan ranked
player, but I would think it would take more than "just a few months" to "beat
the pants off" them.

Back to the subject at hand: I think we will solve mathematical go before we
solve chess (where 'solve' is used in the mathematical sense, so that, for
example, we can prove _" chess is a win for white, in 53 moves"_, and
mathematical go is as described in
[https://math.berkeley.edu/~berlek/cgt/gobook.html;](https://math.berkeley.edu/~berlek/cgt/gobook.html;)
its difference with regular go variants is the way half stones are counted).

Reason is that both games, even with extensive pruning, are too complex for an
full search of their game tree, and go has a simpler structure, making it
easier to reason about it without doing that exhaustive search.

[I also doubt I'll live to see either happen]

~~~
gizmo686
It has been demonstrated that Go (when generalized to an arbitrarily large
board, and an arbitrary initial configuration of stones) is PSPACE hard.

This means that an analytical solution would either need to be an optimization
of brute force, or exploit some additional structure that results from
starting with an empty go board.

As an added wrinkle, such a solution may involve assuming that White [0] plays
optimally. Ironically, this means that it may still be possible to beat
someone who has fully analyzed and "solved" go, by making incorrect moves that
puts the board in a position where their analysis only says that a winning
move exists.

[0] I am assuming that Black has the winning strategy.

~~~
jacquesm
This was my standard trick in chess when I still played a lot. If I would play
against someone strong on openings but a weak player otherwise make an
irrational opening move totally upsetting their apple-carts. If they were
strong players they'd take advantage but if they were only strong on book
knowledge you'd take them to the cleaners. Knowing your opponent in chess can
be as important as knowing the game.

