AlphaGo wins fifth game after not knowing a known tesuji

[NL Versie]

In the fifth and exciting last game of the Google DeepMind challenging match, deep learning AlphaGo played an impressive and very balanced moyo-building game. Even though Lee Sedol had substantial (secure) territory already early in the game and although he was able to thwart AlphaGo's huge moyo plans, the program succeeded in getting enough compensation to stay ahead by a small margin of just a few points. 

Both Lee Sedol and AlphaGo played a very solid opening and after move 40 (circle, white is AlphaGo) the outcome of the game appears completely open from their opposing forces of moyo and territory.

Then AlphaGo miscomputes the effectiveness of a tesuji and looses some points in the bottom right corner. Fortunately, it played those moves in territory already realized by black and it got some sente moves at the outside which would be quite handy if AlphaGo would want to turn it's moyo potential into real territory later in the game.

After Lee Sedol's move (triangle) to keep AlphaGo under pressure in the upper left while at the same time reducing AlphaGo's moyo potential around the center, AlphaGo came up with a great response (move 70, circle) turning around the flow of the game by putting high pressure on black.

Lee Sedol needs to create a living group but in return, AlphaGo adds up strength to gradually help it's moyo building strategy. In a complicated middle game, Lee Sedol is forced to find efficient manners to prevent AlphaGo from realizing it's entire moyo by playing smart reduction moves. He succeeds but it comes at a price after AlphaGo plays a beautiful counter attack move (circle, move 136), putting again ultra-high pressure on Lee Sedol.  

In the final phase of the game, Lee Sedol wonderfully manages to reduce most of the entire moyo built up by AlphaGo (triangle, after move 183). But, despite Lee Sedol's great moyo reduction skills, it is still deep learning AlphaGo that is ahead due to the sufficient compensation it got along the way. 

For the top Go professionals commenting on this fifth game, it is difficult to say where Lee Sedol perhaps made a mistake. But one way or another AlphaGo managed to catch up again after falling behind when misreading a tesuji earlier in the game. Overall, the flow of the game and the way of playing, both of Lee Sedol and AlphaGo, seemed to be very balanced.

In the remaining 100 moves in the endgame, Lee Sedol was unable to overcome his arrears of just a few points. Even though he was ahead on the board, he would lose by about 2.5 points when including white's komi (7.5 points). This was the first time in the match a game developed so close in counting. 

Lee Sedol resigned after move 280 (circle) while less than a handful of small endgame moves were left. This was another stunning and incredible exciting historic game where the distinctions in playing strength between the world's top Go-professional Lee Sedol and deep learning program AlphaGo during the match were most of the time very hard to discover. 

So the final outcome of the match is: AlphaGo defeats Lee Sedol by 4 - 1. A result that only a small minority ( < 10-15%)  of the more than 100 million people worldwide who watched this match online, would have predicted in advance. AlphaGo has impressed all Go players worldwide with rock-solid, deep reading, sometimes unexpected and really wonderful, effective moves in these games.

During the post-match press conference Lee Sedol said: "I am sorry because the match comes to an end". Answering a question about whether the five games might have changed his understanding of the game of Go, Lee Sedol responded: "Basically, I don't necessarily think that AlphaGo is superior to me. I believe there is still more that a human being can do to fight against the AI program. That's why I felt a little bit regrettable because there is more that a human could have shown during this match."

Lee Sedol concluded: "Enjoyment is the essence of Go. I do wonder whether I've always been enjoying the game but I do want to admit that yes, I did enjoy the games against AlphaGo. Creativity of human beings and also all the traditional and classical beliefs that we have had, well I've come to question them a little bit based on my experience with AlphaGo. So I've more studying to do down the road". 

Commentator Chris Garlock remarked: "this match has triggered unprecedented global attention to the game of Go. We could not have asked for a more wonderful or generous gift to this game. The five historic and beautiful games of this once-in-a-lifetime challenging match will be studied over and over again in the years to come, launching what I'm sure is going to be a new era in the most ancient of games. I'm really looking forward to that". 

Latest Predictions AlphaGo vs. Lee Sedol (9p)

               

[NL Versie] 

Nearly all polls worldwide show the same result:   ~75 - 85 % of all voters is convinced that Lee Sedol will win the coming match against AlphaGo. This is also the prediction of the majority of the participants of a price contest among dutch Go players (over  ~105 participants, organised in cooperation with chess and go shop  'het Paard ' and an ICT company). 

There are, however, many reasons to believe that AlphaGo, since the match about half a year ago, in which European Go Champion Fan Hui (2p) was beaten with 5 – 0, at least will play a few dan grades stronger against Lee Sedol. The estimated actual playing strength of AlphaGo is  ≥ 8th dan prof. 

• AlphaGo has learned from it's (small) mistakes during the match against Fan Hui
• improvements to AlphaGo's playing algorithms (for example for move selection and performance evaluation)
• AlphaGo may be can built now on top of extended joseki and shape libraries 
• finetuning and extension of AlphaGo's neural network training sessions
• prevention and circumvention of specific problem situations (e.g. complex ko situations about many points)
• selection of specific groups of professional Go games, not only from the KGS Go Server but as well (selectively) from other Go servers worldwide
• improvement of the balance between on one hand AlphGo's neural networks for move selection and position evaluation and on the other hand precise computation through Monte Carlo Tree Search
• extension of the number of conventional ( > 1202 CPUs) and graphical coprocessors (> 176 GPUs) that the distributed version of AlphaGo can use simultaneously during it's games against Lee Sedol
• increase of the thinking time (computation time) per person: this match 2 hour p.p. (was 1 hour during the match against Fan Hui) which will be strongly beneficial for AlphaGo (especially towards the endgame)
• implementation of new ideas and concepts to increase severely the performance of AlphaGo and/or make use of perhaps weaker elements in the way Lee Sedol plays (if these exist at all  since Lee Sedol has won over 68% of his games during ~the last years)
• extension of the number of studied Go-positions (>60 million) and/or games played (e.g. against itself, ≥ 1.3 million) to increase the accuracy of AlphaGo in reproducing Go-profs moves. It has been shown by the DeepMind group that small improvements in this accuracy do lead immediately to big leaps forward in playing strength 
• improvement and extension of position filters which determine whether a (subpart of a) position during a game against Lee Sedol is sufficiently being recognized by AlphaGo
• improvements in reinforcement learning the value of Go moves by more detailed and accurate backpropagation of the final game result to each move and/or position

Despite all possible improvements of AlphaGo during the match against Lee Sedol, I expect that: 

• Lee Sedol wins at least one game against AlphaGo 
• the winner of the first game will also be the final winner of the match
• Lee Sedol will win the match with 3-2 
• AlphaGo also will win at least one game 
• Lee Sedol demonstrable (*) will forget to play at least one move that he really had to play immediately, and this will happen at least once EACH game of the match
• in the endgame, Lee Sedol demonstrable (*) plays less well than AlphaGo and, consequently, in EACH game of the match Lee Sedol will loose points in the endgame
• at least one game Lee Sedol will reach a position of total resignation (*). Whether he indeed resigns or ultimately wins / loses is irrelevant
• Lee Sedol has to put in all effort to realize and keep his eventually built profit until the end of the game
• within one year after the match with Lee Sedol, AlphaGo will play a similar match against a strong 9p prof (possibly Lee Sedol again) and this time AlphaGo will win this match (of the five formal games, AlphaGo will win then at least 3 games).

(*) as for example reviewed by a majority of 10 independent top Go profs (9p) from South-Korea, China and Japan.

And if you do want to make bets about this:  Deep Learning models are as good as the data you feed them. 

Lee Sedol played brilliant and won the fourth game against deep learning AlphaGo

[NL Versie]

For the first time during this five-game match, Lee Sedol was able to take a clear lead after a brilliant move in the second half of the middle game, fighting hard to prevent AlphaGo from making (potential) territory in the center.

Even though Lee Sedol played under ultra-high pressure for more than an hour, using his last byo-yomi period up to the max each move (maximum of 1 minute per move), he was able to maintain his built up advantage until far in the endgame of this fourth game of the match. Finally, AlphaGo resigned after playing a handful of doubtful moves and several mistakes that even lost additional points. 

Top Go-prof Gu Li (9p) described Lee Sedol's move 78 (triangle, Lee Sedol is white) in the fourth game of the match against AlphaGo as a "God's move". Another Go pro commented after Lee Sedol winning this fourth game: "Lee Sedol just fought the 1000 years history of Baduk". 

The way AlphaGo chooses it's next moves on maximizing it's probability of winning (instead of maximizing the difference by which it may win) forces AlphaGo apparently to play suboptimal and perhaps even faulty moves when it's probability of winning the game is falling below a specific threshold (e.g. 50 %). 

This is the final position of game 4 after Lee Sedol's move 180 (circle), the point in the game  at which AlphaGo resigned after it's notion that the probabilities of winning this game were falling below it's critical threshold for resignation. At this point, AlphaGo is behind at least 5 points (komi included) and therefore needs to make more than ~20 points in the bottom center area (without Lee Sedol getting any compensation for that) in order to catch up.

When AlphaGo's awareness of being defeated by Lee Sedol was passed to Google DeepMind's team member  and operator Aja Huang (6d amateur), he placed two  stones on the board to let Lee Sedol and the world know that AlphaGo resigned and for the first time lost a game (in the official match games without handicap) against a top professional Go player.

This time, Lee Sedol was able to exploit a sequence mistake fighting AlphaGo while the program was misjudging an important middle game fight and lost a heavy group of stones. In return, AlphaGo just got a pover and rather modest sente move. Commentator Michael Redmond (9p) showed several sequences that AlphaGo could have played instead, which would probably have resulted in a considerable better outcome of this fight for AlphaGo.

Lee Sedol's first win against AlphaGo shows --and does prove for the first time-- that deep learning AlphaGo is not playing perfect all the time and really experienced some severe problems in judging correctly the outcome of a rather complex middle-game fight. Even though the fighting in this game seemed not to be as tough as for instance during the second game of the match where ultra-strong AlphaGo defeated Lee Sedol at his own game of fighting play.

After the game Lee Sedol answered a question about his mental condition: "after loosing the first three games and thus the match against AlphaGo, I could not say that there was no psychological shock ... but it was not to the extent that I would have to stop playing the ongoing match because at any moment of the game, I really enjoyed the game. I can tell you that I've not retained any severe damage and I'm very happy to say that I won this single game."