9 ms·

AlphaGo beats Ke Jie again to wrap up three-part match

133 points by iandanforth 9y ago

partycoder 9y ago
I did not see Alpha Go's invasion of the top left corner coming. The black group consisting of 3 stones (B13, C12, E12) did not seem alive at first, but it turned out to not only live but completely reduce white's corner.
- partycoder 9y ago
  Deepmind published a summary video for the game here: https://www.youtube.com/watch?v=5fOmbqjH7zI https://www.youtube.com/watch?v=5fOmbqjH7zI
kgdinesh 9y ago
Well, we are all fucked aren't we?
- wavesandwind 9y ago
  As in AI taking over the world? No, since AlphaGo is optimized to do just one thing.
  - ggreer 9y ago
    The underlying technology is far more general than than you think. In addition to playing Go, it has already been used to play video games[1], diagnose diseases[2], and make Google's datacenters more efficient.[3] 1. https://deepmind.com/blog/enabling-continual-learning-in-neural-networks/ https://deepmind.com/blog/enabling-continual-learning-in-neu... 2. https://deepmind.com/applied/deepmind-health/ https://deepmind.com/applied/deepmind-health/ 3. https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/ https://deepmind.com/blog/deepmind-ai-reduces-google-data-ce...
    - yorwba 9y ago
      In each case with lots of manual tweaking to get the best performance. Deep learning is not magic; you need to find the right kind of architecture for your problem, and that requires prior knowledge. Humans will still be in the loop for at least the next decade or so.
      - Filligree 9y ago
        > Humans will still be in the loop for at least the next decade or so. Reassuring. Really.
- Aron 9y ago
  'Humans' are fucked one way or another. We were always fucked. Our parents and grandparents were always fucked. Did you expect to live forever? The future will contain our children, and they will be better in each and every way. And they won't be human.
strin 9y ago
Not surprising. However, I am not buying the claim that the algorithm is going to transform many other domains: " The system could help optimize power grids, he says, or streamline shipping routes, or refine scientific research." by Wired https://www.wired.com/2017/05/googles-alphago-levels-board-games-power-grids/ https://www.wired.com/2017/05/googles-alphago-levels-board-g... The main concern is data efficiency. AlphaGo essentially baked good movies into value and policy network by playing millions of times. In reality, unless you have a really good simulator, deep reinforcement learning is almost never applicable.
- deleted 9y ago
  [deleted]
- Aron 9y ago
  'data efficiency' -- that's a good candidate for the next buzz imo. People talk a lot about how massive amounts of data regressed over giant networks of dumb functions happens to perform well on many tests, but they ignore the fact that the performance of the system is sick. It fails if you tweak the input by small amounts that would never confuse a human. You shouldn't NEED that much data. You need better priors. Current ML systems rely on arbitrary coincidences to succeed at multiple choice tests. I did that in courses I didn't go to class. They are primitive and childish and won't scale with pure data/processing. We are still missing something.
  - strin 9y ago
    Prior is in some sense the fruit of data. Humans develop prior by accumulating experiences. Laws of physics are discovered through experimentation and introspection over many data points. So what we really need is a life-long learner - a machine learning algorithm that could extract knowledge from many tasks and store that in its long-term memory. The fact that we don't have an architecture for long-term memory is the main road blocker towards this goal.
- londons_explore 9y ago
  There are a lot of problems for which you effectively have limitless data. For example, optimizing the shape of an aerofoil, wing, wind turbine, ship, pump, etc. The "real" data is the result of computationally costly fluid dynamics simulations. The network uses as much of those as it likes to modify the design to be better. There are also a lot of problems where there is nearly limitless data (ie. > 10^12 bytes of data). Examples would be MRI/CT scans, all internet video, all TV channels, etc.
RcouF1uZ4gsC 9y ago
I wonder what would be the result if the rules of the game were tweaked slightly (in an arbitrary fashion) just before a game, then the match went ahead as before. My guess would be Ke Jie would be fine and would adapt fairly quickly and AlphaGo would look pretty bad. I think the AI/human crossover boundaries are at meta boundaries. As we get more meta, humans do better over AI. AI has been eating up the lower meta levels for some time now. It has been a very long time since a human could do division faster than a computer. The question is how far does this meta staircase ascend, and if/when will AI climb all the way to the top?
- grenoire 9y ago
  What sorts of modifications could even be made to Go that it doesn't turn into an imbalanced mess?
  - wapz 9y ago
    You can make huge fundamental changes like "you cannot connect 3 stones in a row" or you cannot make a certain shape with your stones. There are 'bad' shapes in go that you learn when you're young that almost never surface in higher levels of play (of course you would make a non-bad shape illegal). You can say every 15 turns you get 2 moves in a row and they are stagged (this is game-breaking). You can ban certain josekis ('standard moves' early in the game). You can make each captured stone worth 2 points instead of 1.
  - londons_explore 9y ago
    Every move, you flip 6 coins. If all 6 come up as heads, you swap seats and colors with your opponent. This means that an advantageous board position is much less important, and it will be in both players favour to keep the board position very near evenly matched.
    - Someone 9y ago
      Not always. A player that thinks he will lose a game that's tied in the last 8-ish moves will have an incentive to create a highly unbalanced game. That way, that player gets a 50% chance of winning.
hacker_9 9y ago
I was impressed when this happened last year. Funny how all the AI fear mongering was the same back then. But a year later the result is just driving a car poorly around a GTA map. Oh and AlphaGo still winning Go matches. Scary indeed.
tosh 9y ago
Naive question: is something like AlphaGo also better at chess compared to previous approaches to chess? Can someone elaborate?
- Recursing 9y ago
  Two approaches used by the previous version of AlphaGo (MCTS and a NN for position evaluation) have been tried in the past in computer chess with relatively poor results, while in the past few years they were already giving good results for Go. The current version might be using some different techniques that could be more useful, though. They should release some details in the next few weeks.
  - wapz 9y ago
    Can't they effectively "solve" chess with the power of AlphaGo? Someone posted on the last thread that the AI plays one game of go every 2 seconds against itself to train (that's ~150 moves/second). Wouldn't chess be solved for all combinations in a matter of days?
    - panic 9y ago
      AlphaGo's strength is in choosing which moves to consider. Solving a game means considering every move (up to alpha-beta pruning) -- AlphaGo's techniques don't help with this.
    - Madmallard 9y ago
      There are effectively limitless possibilities in terms of playouts of chess
    - jackmott 9y ago
      no
    - BoiledCabbage 9y ago
      Short answer is no. One estimate says there are 10^120th possible chess games. I'm not doing the math now, but I believe that falls into the not-physically possible within our lifetime using known physics. An "If you had started calculating at the beginning of the universe, you wouldn't have even tackled a sliver of the work by now" type of problems. http://www.popsci.com/science/article/2010-12/fyi-how-many-different-ways-can-chess-game-unfold http://www.popsci.com/science/article/2010-12/fyi-how-many-d...
IMTDb 9y ago
When Ke Jie says : "I also thought I was very close to winning the game in the middle but maybe that’s not what AlphaGo was thinking". Do we know what AlphaGo was thinking ?
- Ajedi32 9y ago
  From what I've heard AlphaGo does maintain an estimate of what its odds of winning are at any given point in the game. Not sure if anyone from DeepMind has released any of that data from game 2 though.
  - IMTDb 9y ago
    It would be quite interesting to compare Ke Jie or other experts feeling about the game with those data :)
  - arcanus 9y ago
    Pretty early on Denis tweeted that AlphaGo believed his opponent had been playing a perfect game, up to that point: https://twitter.com/demishassabis/status/867584056095002624 https://twitter.com/demishassabis/status/867584056095002624
romaniv 9y ago
The official tournament page says it was only the second game. Why does the article suggest it was the third? https://events.google.com/alphago2017/ https://events.google.com/alphago2017/
- skybrian 9y ago
  If you win two games of a three game match, the last game doesn't matter.
devy 9y ago
If AlphaGo has a dan rating, it would have been rated at 100 dan by now.
- partycoder 9y ago
  The problem with professional dan ranks is that they're capped at 9p, and each association has different rules for awarding that rank. Because of this there is a lot of variance among 9p. For instance, Fernando Aguilar 6d (aguilar1 and aguilar on KGS), an amateur dan from Argentina, has defeated Hasegawa Sunao 9p as well as Yo Kagen 9p. In theory this should not have happened. If the cap didn't exist, I think Alpha Go may be around 13p, based on the fact that Lee Sedol and Ke Jie may be able to give reverse komi to many top 9p players, and that Alpha Go may be about 3 stones stronger than both. Park Yeong-hun is less often mentioned but he may be playing at a higher level to that of Lee Sedol.
  - wapz 9y ago
    I don't know Fernando Aguilar but is the reason he's 6D not that he's capped at it before becoming professional? IIRC when I played in SF bay area (probably all of USA in the AGA), you couldn't get higher than 6D(7D?) without becoming a pro. I think in tournament lineup you would see something like Mr. A 6D (7.2) where the 7.2 was his estimated rating but it was a long time ago.
    - partycoder 9y ago
      Not entirely sure about caps at the amateur level. At least go servers don't have such cap, and there's certain mapping between KGS ranks to offline amateur ranks. The highest attainable rank at Go servers is 9d, aguilar is 6d there. https://www.gokgs.com/graphPage.jsp?user=aguilar1 https://www.gokgs.com/graphPage.jsp?user=aguilar1 An informal mapping among ranking systems can be seen here: http://senseis.xmp.net/?RankWorldwideComparison http://senseis.xmp.net/?RankWorldwideComparison Servers usually manually override ranks for professional players... But going back to my point, a 9p should be consistently stronger than a 6 dan amateur, but these counterexamples challenge that.

133 points by iandanforth 9y ago

I did not see Alpha Go's invasion of the top left corner coming. The black group consisting of 3 stones (B13, C12, E12) did not seem alive at first, but it turned out to not only live but completely reduce white's corner.

partycoder 9y ago
Deepmind published a summary video for the game here: https://www.youtube.com/watch?v=5fOmbqjH7zI https://www.youtube.com/watch?v=5fOmbqjH7zI

kgdinesh 9y ago

Well, we are all fucked aren't we?

wavesandwind 9y ago
As in AI taking over the world? No, since AlphaGo is optimized to do just one thing.
- ggreer 9y ago
  The underlying technology is far more general than than you think. In addition to playing Go, it has already been used to play video games[1], diagnose diseases[2], and make Google's datacenters more efficient.[3] 1. https://deepmind.com/blog/enabling-continual-learning-in-neural-networks/ https://deepmind.com/blog/enabling-continual-learning-in-neu... 2. https://deepmind.com/applied/deepmind-health/ https://deepmind.com/applied/deepmind-health/ 3. https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/ https://deepmind.com/blog/deepmind-ai-reduces-google-data-ce...
  - yorwba 9y ago
    In each case with lots of manual tweaking to get the best performance. Deep learning is not magic; you need to find the right kind of architecture for your problem, and that requires prior knowledge. Humans will still be in the loop for at least the next decade or so.
    - Filligree 9y ago
      > Humans will still be in the loop for at least the next decade or so. Reassuring. Really.
Aron 9y ago
'Humans' are fucked one way or another. We were always fucked. Our parents and grandparents were always fucked. Did you expect to live forever? The future will contain our children, and they will be better in each and every way. And they won't be human.

strin 9y ago

Not surprising. However, I am not buying the claim that the algorithm is going to transform many other domains: " The system could help optimize power grids, he says, or streamline shipping routes, or refine scientific research." by Wired https://www.wired.com/2017/05/googles-alphago-levels-board-games-power-grids/ https://www.wired.com/2017/05/googles-alphago-levels-board-g... The main concern is data efficiency. AlphaGo essentially baked good movies into value and policy network by playing millions of times. In reality, unless you have a really good simulator, deep reinforcement learning is almost never applicable.

deleted 9y ago
[deleted]
Aron 9y ago
'data efficiency' -- that's a good candidate for the next buzz imo. People talk a lot about how massive amounts of data regressed over giant networks of dumb functions happens to perform well on many tests, but they ignore the fact that the performance of the system is sick. It fails if you tweak the input by small amounts that would never confuse a human. You shouldn't NEED that much data. You need better priors. Current ML systems rely on arbitrary coincidences to succeed at multiple choice tests. I did that in courses I didn't go to class. They are primitive and childish and won't scale with pure data/processing. We are still missing something.
- strin 9y ago
  Prior is in some sense the fruit of data. Humans develop prior by accumulating experiences. Laws of physics are discovered through experimentation and introspection over many data points. So what we really need is a life-long learner - a machine learning algorithm that could extract knowledge from many tasks and store that in its long-term memory. The fact that we don't have an architecture for long-term memory is the main road blocker towards this goal.
londons_explore 9y ago
There are a lot of problems for which you effectively have limitless data. For example, optimizing the shape of an aerofoil, wing, wind turbine, ship, pump, etc. The "real" data is the result of computationally costly fluid dynamics simulations. The network uses as much of those as it likes to modify the design to be better. There are also a lot of problems where there is nearly limitless data (ie. > 10^12 bytes of data). Examples would be MRI/CT scans, all internet video, all TV channels, etc.

RcouF1uZ4gsC 9y ago

I wonder what would be the result if the rules of the game were tweaked slightly (in an arbitrary fashion) just before a game, then the match went ahead as before. My guess would be Ke Jie would be fine and would adapt fairly quickly and AlphaGo would look pretty bad. I think the AI/human crossover boundaries are at meta boundaries. As we get more meta, humans do better over AI. AI has been eating up the lower meta levels for some time now. It has been a very long time since a human could do division faster than a computer. The question is how far does this meta staircase ascend, and if/when will AI climb all the way to the top?

grenoire 9y ago
What sorts of modifications could even be made to Go that it doesn't turn into an imbalanced mess?
- wapz 9y ago
  You can make huge fundamental changes like "you cannot connect 3 stones in a row" or you cannot make a certain shape with your stones. There are 'bad' shapes in go that you learn when you're young that almost never surface in higher levels of play (of course you would make a non-bad shape illegal). You can say every 15 turns you get 2 moves in a row and they are stagged (this is game-breaking). You can ban certain josekis ('standard moves' early in the game). You can make each captured stone worth 2 points instead of 1.
- londons_explore 9y ago
  Every move, you flip 6 coins. If all 6 come up as heads, you swap seats and colors with your opponent. This means that an advantageous board position is much less important, and it will be in both players favour to keep the board position very near evenly matched.
  - Someone 9y ago
    Not always. A player that thinks he will lose a game that's tied in the last 8-ish moves will have an incentive to create a highly unbalanced game. That way, that player gets a 50% chance of winning.

hacker_9 9y ago

I was impressed when this happened last year. Funny how all the AI fear mongering was the same back then. But a year later the result is just driving a car poorly around a GTA map. Oh and AlphaGo still winning Go matches. Scary indeed.

tosh 9y ago

Naive question: is something like AlphaGo also better at chess compared to previous approaches to chess? Can someone elaborate?

Recursing 9y ago
Two approaches used by the previous version of AlphaGo (MCTS and a NN for position evaluation) have been tried in the past in computer chess with relatively poor results, while in the past few years they were already giving good results for Go. The current version might be using some different techniques that could be more useful, though. They should release some details in the next few weeks.
- wapz 9y ago
  Can't they effectively "solve" chess with the power of AlphaGo? Someone posted on the last thread that the AI plays one game of go every 2 seconds against itself to train (that's ~150 moves/second). Wouldn't chess be solved for all combinations in a matter of days?
  - panic 9y ago
    AlphaGo's strength is in choosing which moves to consider. Solving a game means considering every move (up to alpha-beta pruning) -- AlphaGo's techniques don't help with this.
  - Madmallard 9y ago
    There are effectively limitless possibilities in terms of playouts of chess
  - jackmott 9y ago
    no
  - BoiledCabbage 9y ago
    Short answer is no. One estimate says there are 10^120th possible chess games. I'm not doing the math now, but I believe that falls into the not-physically possible within our lifetime using known physics. An "If you had started calculating at the beginning of the universe, you wouldn't have even tackled a sliver of the work by now" type of problems. http://www.popsci.com/science/article/2010-12/fyi-how-many-different-ways-can-chess-game-unfold http://www.popsci.com/science/article/2010-12/fyi-how-many-d...

IMTDb 9y ago

When Ke Jie says : "I also thought I was very close to winning the game in the middle but maybe that’s not what AlphaGo was thinking". Do we know what AlphaGo was thinking ?

Ajedi32 9y ago
From what I've heard AlphaGo does maintain an estimate of what its odds of winning are at any given point in the game. Not sure if anyone from DeepMind has released any of that data from game 2 though.
- IMTDb 9y ago
  It would be quite interesting to compare Ke Jie or other experts feeling about the game with those data :)
- arcanus 9y ago
  Pretty early on Denis tweeted that AlphaGo believed his opponent had been playing a perfect game, up to that point: https://twitter.com/demishassabis/status/867584056095002624 https://twitter.com/demishassabis/status/867584056095002624

romaniv 9y ago

The official tournament page says it was only the second game. Why does the article suggest it was the third? https://events.google.com/alphago2017/ https://events.google.com/alphago2017/

skybrian 9y ago
If you win two games of a three game match, the last game doesn't matter.

devy 9y ago

If AlphaGo has a dan rating, it would have been rated at 100 dan by now.

partycoder 9y ago
The problem with professional dan ranks is that they're capped at 9p, and each association has different rules for awarding that rank. Because of this there is a lot of variance among 9p. For instance, Fernando Aguilar 6d (aguilar1 and aguilar on KGS), an amateur dan from Argentina, has defeated Hasegawa Sunao 9p as well as Yo Kagen 9p. In theory this should not have happened. If the cap didn't exist, I think Alpha Go may be around 13p, based on the fact that Lee Sedol and Ke Jie may be able to give reverse komi to many top 9p players, and that Alpha Go may be about 3 stones stronger than both. Park Yeong-hun is less often mentioned but he may be playing at a higher level to that of Lee Sedol.
- wapz 9y ago
  I don't know Fernando Aguilar but is the reason he's 6D not that he's capped at it before becoming professional? IIRC when I played in SF bay area (probably all of USA in the AGA), you couldn't get higher than 6D(7D?) without becoming a pro. I think in tournament lineup you would see something like Mr. A 6D (7.2) where the 7.2 was his estimated rating but it was a long time ago.
  - partycoder 9y ago
    Not entirely sure about caps at the amateur level. At least go servers don't have such cap, and there's certain mapping between KGS ranks to offline amateur ranks. The highest attainable rank at Go servers is 9d, aguilar is 6d there. https://www.gokgs.com/graphPage.jsp?user=aguilar1 https://www.gokgs.com/graphPage.jsp?user=aguilar1 An informal mapping among ranking systems can be seen here: http://senseis.xmp.net/?RankWorldwideComparison http://senseis.xmp.net/?RankWorldwideComparison Servers usually manually override ranks for professional players... But going back to my point, a 9p should be consistently stronger than a 6 dan amateur, but these counterexamples challenge that.