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One member of the deepmind team responded to this very question during the interview at the beginning of part 3. He said that the training data set size is muc

One member of the deepmind team responded to this very question during the interview at the beginning of part 3. He said that the training data set size is much, much larger than the number of lee sedol games. It is like a drop in the ocean and not enough to significantly influence the resulting policy network.

gus_massa 11y ago
Perhaps the computer didn't know that it was playing against Lee Sedol, but from Wikipedia "As of February 2016, he ranks second in international titles (18), behind only Lee Chang-ho (21)." I don't know the details of the algorithm and perhaps it doesn't give more explicit weight to his games. But I wouldn't be surprised if after some iterations the algorithm decided to give more implicit weight to the games of the world leaders.
- sdenton4 11y ago
  Well, I'm not even sure if they attached individual identities to the games fed in as features...
  - arcticfox 11y ago
    The implication is that because Sedol wins more tournaments than almost anyone else, if you feed "Tournament winner" or "ELO" etc. as a feature, his games will be much more weighted than others. So even if AlphaGo isn't explicitly designed to play against his style, it's implicitly trained in it. But it's pretty subtle and I'm guessing that the volume of high-level tournaments overwhelms any effect, as the AlphaGo team said.
- lambda 11y ago
  They mentioned what the training input to AlphaGo was in the paper; a database of a few hundred thousand games from dan ranked players on KGS. This means mostly amateur players, though there are a few professionals who play on KGS as well. However, this only gets you so far, and the training set is fairly small compared to what you want to really train both the policy and value networks well. So then they had it play millions of games against different versions of itself, training both a new policy network and the value network based on that. It's unlikely that Lee Sedol's games made much if any impact on AlphaGo's training. It was bootstrapped off of high-level amateur, and some casual pro, play, but from then on it just trained against itself.

gus_massa 11y ago

Perhaps the computer didn't know that it was playing against Lee Sedol, but from Wikipedia "As of February 2016, he ranks second in international titles (18), behind only Lee Chang-ho (21)." I don't know the details of the algorithm and perhaps it doesn't give more explicit weight to his games. But I wouldn't be surprised if after some iterations the algorithm decided to give more implicit weight to the games of the world leaders.

sdenton4 11y ago
Well, I'm not even sure if they attached individual identities to the games fed in as features...
- arcticfox 11y ago
  The implication is that because Sedol wins more tournaments than almost anyone else, if you feed "Tournament winner" or "ELO" etc. as a feature, his games will be much more weighted than others. So even if AlphaGo isn't explicitly designed to play against his style, it's implicitly trained in it. But it's pretty subtle and I'm guessing that the volume of high-level tournaments overwhelms any effect, as the AlphaGo team said.
lambda 11y ago
They mentioned what the training input to AlphaGo was in the paper; a database of a few hundred thousand games from dan ranked players on KGS. This means mostly amateur players, though there are a few professionals who play on KGS as well. However, this only gets you so far, and the training set is fairly small compared to what you want to really train both the policy and value networks well. So then they had it play millions of games against different versions of itself, training both a new policy network and the value network based on that. It's unlikely that Lee Sedol's games made much if any impact on AlphaGo's training. It was bootstrapped off of high-level amateur, and some casual pro, play, but from then on it just trained against itself.