401–410 of 576 posts
Lee Sedol seemed to be doing well before he went into extra time (as far as I could follow from the commentators). How is it ensured that this is a fair game given the time constraints? I'm guessing adding more computing power to the AlphaGo program should definitely help it in this regard.
Jeopardy had the same complaints against Deep Blue. The machine was buzzing too fast. Humans knew the answers too but just couldn't buzz in.
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I'm not a Go player but play other competitive sports. Humans have a herd mentality...as Op mentioned there's certain styles of playing...which has their own strengths and weaknesses. Sometimes people will not examine other styles that may have better strengths and just focus on the exist one. Then comes along someone who 'thinks outside the box' with a new style and revolutionize the playing field. Think Bruce Lee a…
I've seen this happen with "modern tennis" versus how I was taught to play.
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For some reason this made me think of the Focused in A Deepness in the Sky - where real general AI isn't possible [at least where we are] so human minds are harnessed to solve problems in a deeply unpleasant way.
Is there a non-dystopian way we can do this? The immediate problems seem to be bandwidth of communication and ability to quickly generate shared culture and jargon. Are Bridgewater Capital's employees Ray Dalio's focused?
https://en.wikipedia.org/wiki/Quarantine_%28Greg_Egan_novel%...
Someone somewhere asked why a lot of people in the Go community is taking this in a somewhat hard way, here is my hypothesis: Go, unlike Chess, has deep mytho attached to it. Throughout the history of many Asian countries it's seen as the ultimate abstract strategy game that deeply relies on players' intuition, personality, worldview. The best players are not described as "smart", they are described as "wise". I thin…
> AI without emotion, philosophy or personality just comes in and brushes all of that aside and turns Go into a simple game of mathematics. Just wait until machines start producing top notch research in experimental fields such as chemistry and Physics...
As someone who studied AI in college and am a reasonably good amateur player, I have been following the matches between Lee and AlphaGo. AlphaGo plays some unusual moves that go clearly against any classically trained Go players. Moves that simply don't quite fit into the current theories of Go playing, and the world's top players are struggling to explain what's the purpose/strategy behind them. I've been giving it…
> It's both exciting and eerie. It's like another intelligent species opening up a new way of looking at the world (at least for this very specific domain). and much to our surprise, it's a new way that's more powerful than ours. I have been watching Myungwan Kim's commentary for the games - and it seems notable that a few moves he finds very peculiar immediately when they are made, he will later point out to as achi…
This. It's a fairly common feature of any AI that uses some form of tree search/minimax, and the effect is very pronounced in chess. Even the best human players can only think 6-8 plies into the feature versus ~18 for a computer. What we can (could?) do is apply smarter evaluation functions to the board states resulting from candidate plays and stop considering moves that look problematic earlier in the search (game tree pruning). AI tends to use very simple evaluation functions that can be computed quickly. They do so given that 1) it allows for deeper search, and a weak heuristic evaluated far in the future often beats a strong one evaluated a few plies prior and 2) for some games (like Go) it's really hard to codify the "intuitions" that human players speak of.
Because search based AI considers board states __very__ far in the future, the results are often completely counterintuitive in a game with an established theory of play. Those theories are born of humans, for humans.
The introduction of MCTS some years back was the first leap towards a human level Go AI (incidentally, MCTS is more human-like than exhaustive tree search in that it prunes aggressively by making early judgement calls as to what merits further consideration). AlphaGo's use of deep policy and evaluation networks to score the board is very cool, and the next step in that journey. What's interesting to me is that, unlike chess AI, AlphaGo might actually advance the human theory of Go. It's possible that these "strange moves" will lead to some very interesting insights if DeepMind traces them through the eval and policy networks and manages to back out a more general theory of play.
As someone who studied AI in college and am a reasonably good amateur player, I have been following the matches between Lee and AlphaGo. AlphaGo plays some unusual moves that go clearly against any classically trained Go players. Moves that simply don't quite fit into the current theories of Go playing, and the world's top players are struggling to explain what's the purpose/strategy behind them. I've been giving it…
As someone who studied AI in college and am a reasonably good amateur player, I have been following the matches between Lee and AlphaGo. AlphaGo plays some unusual moves that go clearly against any classically trained Go players. Moves that simply don't quite fit into the current theories of Go playing, and the world's top players are struggling to explain what's the purpose/strategy behind them. I've been giving it…
> AlphaGo plays some unusual moves that go clearly against any classically trained Go players. Moves that simply don't quite fit into the current theories of Go playing, and the world's top players are struggling to explain what's the purpose/strategy behind them. Could AlphaGO be winning in a way similar to left handed fencers having an advantage over right handers by wrong footing them rather than simply being bett…
Remember that AlphaGo has spent months developing its own style and theory of the game in a way that no human has ever seen. Its style is sure to have weaknesses, but humans will have a hard time figuring them out on first sight.
Similarly chess computers do better in some positions than others (they love open tactics!) and one of the games that Kasparov won against Deep Blue he won by playing an extreme anti-silicon style that took advantage of computer weaknesses. However Kasparov didn't have to figure out what that style was because there was a lot of knowledge floating around about how to do that.
Therefore I'd expect that Lee Sedol from a year from now could beat AlphaGo from today. And human Go will improve in general from trying to figure out what AlphaGo has discovered.
However that won't help humans going forward. AlphaGo is not done figuring out the game. At its current rate of improvement, AlphaGo a year from now, running on a single PC, should be able to beat the full distributed version of AlphaGo that is playing today. Now the march of progress is not whether computers can beat professionals. It is going to be how small a computing device can be and still beat the best player in the world.
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Sometimes optimal solutions don't make sense to the human mind because they're not intuitive. For instance, I developed a system that used machine learning and linear solver models to spit out a series of actions to take in response to some events. The actions were to be acted on by humans who were experts in the field. In fact, they were the ones from whom we inferred the relevant initial heuristics. Everyday, I wou…
What you said about the expert calling something a bug reminded me of how the commentator in the first game would see a move by alphaGo and say that it was wrong. He did this multiple times for alphaGo but never once questioned the human's move. Yet even with all those "wrong" moves alphaGo won. Didn't watch the second game, so not sure if he kept doing that.
Later, he did admit that the "overextension" on the north side of the board was more solid than he originally thought, and called it a good move.
He never explicitly said that a move was "good" or "bad", and always emphasized that as he was talking, his analysis of the game was relatively shallow compared to the players. But in hindsight, whenever he point out an "bad-juju feel" on the part of Lee's move, AlphaGo managed to find a way to attack the position.
Overall, you knew when either player made a good move, because the commentator would stop talking and just stare at the board for minutes, at least until the other commentator (an amateur player) would force a conversation, so that the feed wouldn't be quiet.
The vast, vast majority of the time, the English-speaking 9-dan was predicting the moves of both players, in positions more complicated than I could read. (Oh, but it was obvious both players would move there. There were clearly times when the commentator would veer off into a deep distant conversation with the predicted moves still on the demonstration board, because he KNEW both players were going to play out a sequence of maybe 6 or 7 moves forward).
They really got a world-class commentator on the English live feed. If you got 4 hours to spare, I suggest watching the game live.
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On a more realistic side note... Professional Go players devote decades in training ever since their youth, giving up normal educations and lots of other more lucrative opportunities for their lives. It's very easy to imagine their frustrations now that their life-time devotion actually means nothing in front of the AI. It's an upright denial to the way of life they so chose and devoted. IMHO Google should donate the…
Isn't this a good thing? Why are high IQ people devoting their entire lives to a game? Maybe this will make them shift their priorities to solving problems that only really smart humans (like them) can solve.