
Cracking Go (2007) - nl
http://spectrum.ieee.org/computing/software/cracking-go
======
stygiansonic
Here's the relevant quote:

" _Nevertheless, I believe that a world-champion-level Go machine can be built
within 10 years, based on the same method of intensive analysis—brute force,
basically—that Deep Blue employed for chess._ "

A question: I was under the impression that AlphaGo's techniques would not be
classified as "brute force", and if this is the case, is this indicative of
how this field has shifted in thought since 2007?

Also, how relevant are the techniques described in the article nowadays? i.e.:

" _When human players search through the Go game tree, they generally check
the live-or-dead status of each stone only once, then in effect cache the
result in their memories. In other words, they don 't check again unless they
have good reasons to do so. _" (seems like memoization)

~~~
mcphage
AlphaGo used Monte Carlo methods, which I believe qualify as "brute force"—you
generate a bunch of random positions, since the full game tree is way too
large.

~~~
cjbprime
AlphaGo uses neural networks trained on human play to generate candidate
moves, and then Monte Carlo Tree Search to evaluate them deeply. The neural
nets are a strong player by themselves.

AlphaGo primarily refutes this essay, rather than supports it. Prior to
AlphaGo we had many Monte Carlo bots, but none of them approached professional
level of play, much less world-class play. A strategy other than brute force
was required to get there.

\---

AlphaGo's loss in game four is interesting, by the way; I'm not sure how you
would fix it in their model. It appears that it lost because it failed to
predict the (creative, unusual) wedge move in the neural nets, did not spend
enough time evaluating it in MCTS, didn't give a strong response to it (which
was available through MCTS if it had looked), and didn't understand afterwards
that it was now dramatically losing the game and played some very strange
moves.

~~~
pmontra
Actually AlphaGo found that move and it rated it at a 1:10000 chance of being
played. Possibly because of that it didn't evaluate the possible
continuations. However it also took almost another ten moves before the value
network decided that it was losing the game.

------
argonaut
The comments here are really missing the forest for the trees. The point is:
_here is one computer Go researcher predicting computer Go dominance would be
achievable by 2017_. I keep reading in the media that "Go experts thought it
would take decades," but the fact is it's hard to find actual quotations from
computer Go experts.

You can easily find many professional Go players who didn't think Go programs
would surpass humans for decades, but these people aren't engineers. This
would be like asking a human off the street for predictions about AGI.

It doesn't really matter whether or not the researcher predicted the exact
methods of AlphaGo (he didn't - while AlphaGo uses tree search methods very
extensively, just as important were the policy/value nets). Does anyone really
believe that the people with predictions about the timeframe of AGI are going
to predict the exact methods used in AGI?

~~~
Houshalter
Here is a 2013 paper that tried to empirically estimate the rate of
improvement of Go playing software:
[https://intelligence.org/files/AlgorithmicProgress.pdf](https://intelligence.org/files/AlgorithmicProgress.pdf)

>Go programs have improved about one stone per year for the last three
decades. Hardware doublings produce diminishing Elo gains on a scale
consistent with accounting for around half of all progress.

They don't give a clear prediction for when it was expected to exceed the best
humans, but AlphaGo was clearly a discontinuous jump in ability.

According to this article, experts were extremely skeptical of this even a
year ago: [http://www.wired.com/2014/05/the-world-of-computer-
go/](http://www.wired.com/2014/05/the-world-of-computer-go/)

>After the match, I ask Coulom when a machine will win without a handicap. “I
think maybe ten years,” he says. “But I do not like to make predictions.”...

>Even with Monte Carlo, another ten years may prove too optimistic. And while
programmers are virtually unanimous in saying computers will eventually top
the humans, many in the Go community are skeptical. “The question of whether
they’ll get there is an open one,” says Will Lockhart, director of the Go
documentary The Surrounding Game. “Those who are familiar with just how strong
professionals really are, they’re not so sure.”

~~~
argonaut
Right. The only quotation is "I think maybe ten years," by Remi Coulon.
Obviously the community was mixed. But if you believed the way the media has
been spinning it, you'd think _the computer Go community as a collective
thought it would not be possible_.

Otherwise, the opinions of human Go players at the time (that were not
developers of computer Go programs) aren't relevant, nor is the opinion of a
WIRED article writer.

------
nickpsecurity
Here I was hoping someone was anouncing major vulnerability results for Go
language or runtime. Another time...

