Review of Game 5: AlphaGo unfamiliar with common tesuji in ultimate moyo game
Dia. 1: Game 5, after white 12 (circle, Lee Sedol is black)
Dia. 2: Game 5, after black 17 (circle, Lee Sedol is black)
Dia. 3: Game 5, after black 25 (circle, Lee Sedol is black)
Both Lee Sedol and AlphaGo play a very solid opening and after move 40 (circle in Dia. 4) the outcome of the game appears completely open from their opposing moyo and territory strategies.
Dia. 4: Game 5, after white 40 (circle, Lee Sedol is black)
This tesuji frequently occurs in the games AlphaGo originally has been trained on, so it is quite incredible that the program didn't learn to handle this tesuji correctly. AlphaGo looses points here (as well as ko threats), apart from the unnecessary waste of options to utilize the potential aji of it's three white stones (Dia. 5).
At this point in the game, Demis Hassabis tweeted: "AlphaGo made a bad mistake early in the game (it didn't know a known tesuji) but now it is trying hard to claw it back... nail-biting". He added later: "The tesuji itself appears quite often in pro game records, thus it is most likely learned through the 'policy' network. I personally feel AlphaGo should be aware of the tesuji itself, but Lee Sedol's follow-up move was probably better than expected by AlphaGo". He bites his nails and hopes that AlphaGo very soon gets a chance to make things right again (Dia. 5).
Dia. 6: Game 5, after white 70 (circle, Lee Sedol is black)
Lee Sedol invades to pressurize AlphaGo further in the upper left corner and to reduce AlphaGo's potential center moyo at the same time (triangle in Dia. 6). However, AlphaGo plays a beautiful and effective response which turns around the flow of the game immediately. Now, Lee Sedol is put himself under very high pressure (circle in Dia. 6).
AlphaGo's move prevents black's invading stone to connect easily with black's group to the left, prevents an escape of the invading stone to the center to make eventually a base there, forces the invading stone towards white's strength top right, and firmly contributes to it's built up sphere of influence in the center. Moreover, the idea is that if Lee Sedol tries to live, AlphaGo can exert huge pressure on him to become strong on the outside (to reinforce it's moyo even further, see Dia. 6).
Dia. 7: Game 5, after black 81 (circle, Lee Sedol is black)
Dia. 8: Game 5, after black 91 (circle, Lee Sedol is black)
Black has about 70 points secured territory, white has about 30 points together in the upper left corner and it's center moyo. Taking 7.5 komi into account this means that white can only win this game if the program gets at least 35 points in the bottom left corner and edge (without giving black any extra compensation for this). Alternatively, white has to find ways to collect additional points in the center. This crude estimate suggests that the game in this position is still open and undecided.
Dia. 9: Game 5, after white 122 (circle, Lee Sedol is black)
Dia. 10: Game 5, after white 136 (circle, Lee Sedol is black)
Dia. 11: Game 5, after black 183 (circle, Lee Sedol is black)
What complicates this position is that black's territory is almost defined while white has opportunities to score some additional points at different locations on the board. Lee Sedol's only chance is to restrict AlphaGo's potential as far as possible with smart and effective reduction moves.
Dia. 12: Game 5, end position after white 280 (Lee Sedol is black)
When he returns, Lee Sedol plays another couple of moves and then resigns after move 280 (final position, Dia. 12) with just less than a handful of endgame moves left. It is the first time in this match that a game has been played until so late in the endgame. And has ended with such a small difference in points.
So this has been another amazing, inspiring, and historic game in which differences in playing strength between the world's top Go-prof Lee Sedol and deep learning program AlphaGo were hard to detect.
With this result, 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 280 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.
It took Lee Sedol about four games to slightly figure out AlphaGo's way of play: the first two games Lee Sedol probably lost by making a bad strategy decision, the third game Lee Sedol lost due to a fatal mistake already early in the opening, and with the knowledge of the fourth game, it is highly probable that Lee Sedol would have followed other tactics. And would have had a significantly greater chance of winning this match.
In this 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 though he was able to thwart most of AlphaGo's moyo plans, the program succeeded in getting enough compensation along the way to stay ahead by a margin of just a few points. And to maintain this small advantage during the second half of the game.
[Part 6: Review of Game 1: Lee Sedol underestimates AlphaGo's incredible fighting power]
[Part 8: Review of Game 3: Lee Sedol's opening mistakes due to enormous mental pressure]