4 ms·

Wow, based on this graph from the article, it's not surprising that Go computers are competitive with top humans now: http://spectrum.ieee.org/image/MjUxNjkxNQ

Wow, based on this graph from the article, it's not surprising that Go computers are competitive with top humans now: http://spectrum.ieee.org/image/MjUxNjkxNQ http://spectrum.ieee.org/image/MjUxNjkxNQ

ktRolster 11y ago
And actually, it looks like the author of the article more or less realized that: >Schaeffer now finds it plausible that a >computer will beat Go’s grand masters soon
nsajko 11y ago
I don't think that graph makes much sense. The labels "Grand Master", "Master", "average club player" apply to Chess, not Baduk; and the vertical dimension in the graph seems arbitrarily chosen.
- andreyk 11y ago
  I think the scaling is roughly reasonable. In chess National Master is is ELO 2200–2399, and above that is Grand Master, with lower ELO being skilled and club players. In Go, 0-30kyu is amateur (club player trough master), 1-7 dan is advanced amateur (basically master, based on there being few in this category), and 1-9p is grand master. Based on that the graph accurately portrays the rise of Go AIs up to 2011: Year KGS Rank 2009 1-dan 2010 3-dan 2011 4-dan 2012 5-dan The scaling is not exact but I think it conveys the basic facts correctly - by 2011/2012 there well Go programs that could play well better than the average club player.
mjn 11y ago
Fwiw, the point where the Go curve massively changes slope is when Monte-Carlo Tree Search (MCTS) began to be used in its modern form. I think that's been an underreported part of AlphaGo's success: deep networks get the lion's share of the press, but AlphaGo is a hybrid deep-learning / MCTS system, and MCTS is arguably the most important of the algorithmic breakthroughs that led to computer Go being able to reach expert human level strength.
tim333 11y ago
Yeah, I find it a bit annoying when people say experts didn't expect computers to win at go for decades. Anyone who'd looked at that graph and extrapolated would have estimated some time around now. And if they didn't look at that stuff they probably were not very expert.

ktRolster 11y ago

And actually, it looks like the author of the article more or less realized that: >Schaeffer now finds it plausible that a >computer will beat Go’s grand masters soon

nsajko 11y ago

I don't think that graph makes much sense. The labels "Grand Master", "Master", "average club player" apply to Chess, not Baduk; and the vertical dimension in the graph seems arbitrarily chosen.

andreyk 11y ago
I think the scaling is roughly reasonable. In chess National Master is is ELO 2200–2399, and above that is Grand Master, with lower ELO being skilled and club players. In Go, 0-30kyu is amateur (club player trough master), 1-7 dan is advanced amateur (basically master, based on there being few in this category), and 1-9p is grand master. Based on that the graph accurately portrays the rise of Go AIs up to 2011: Year KGS Rank 2009 1-dan 2010 3-dan 2011 4-dan 2012 5-dan The scaling is not exact but I think it conveys the basic facts correctly - by 2011/2012 there well Go programs that could play well better than the average club player.

mjn 11y ago

Fwiw, the point where the Go curve massively changes slope is when Monte-Carlo Tree Search (MCTS) began to be used in its modern form. I think that's been an underreported part of AlphaGo's success: deep networks get the lion's share of the press, but AlphaGo is a hybrid deep-learning / MCTS system, and MCTS is arguably the most important of the algorithmic breakthroughs that led to computer Go being able to reach expert human level strength.

tim333 11y ago

Yeah, I find it a bit annoying when people say experts didn't expect computers to win at go for decades. Anyone who'd looked at that graph and extrapolated would have estimated some time around now. And if they didn't look at that stuff they probably were not very expert.