AlphaGo vs. Lee Se-dol: Game Five

Lee Se-dol resigned before the last handful of “yose” or end-game moves had been played in this morning’s game, bringing the Google DeepMind challenge match between AlphaGo, their Go-playing programme, and the 9 dan professional human player to a close. The artificial intelligence won the match by defeating Lee Se-dol in four of five games.

Lee Se-dol took the black stones and began with a solid, dignified opening that emphasised territory. He resisted white’s attempts to disrupt his plan and denied white the time to reinforce the boundaries of the areas of influence that white acquired in exchange. When a large group of white stones was cut off by a sequence that involved the infamous “stone tower” shape, it seemed like black was in the lead – he had gained thickness and resolved several weaknesses in exchange for a tiny number of points.

Slowly but surely, with calm and sensible late middle-game and end-game fighting, AlphaGo turned the game around once more and by the time the temperature of the end-game moves had fallen to nothing more than one point a move, it was apparent that black would be behind.

Lee Se-dol’s resignation was clear proof of his finely honed ability to count the score in his head because his deficit was barely measurable.

During the pre-match discussion, Google DeepMind talked about the fourth game of the match, the game that AlphaGo lost. They explained that move 78, the tesuji or skilful play that precipitated the demise of AlphaGo in that game, had “surprised” the engine and forced it to build a new plan for a branch in the game tree that it either hadn’t seen or had only explored perfunctorily. A human player in a similar situation would take a lot of time to re-evaluate and recalculate but AlphaGo’s time-control strategy is apparently very simple and primitive and it neglected to invest much of the abundance of time available on its clock.

Today, the post-match press conference was handled ineptly, the English translation coming and going and generally chaotic. A few interesting points could be gleaned from the chaos, including the fact that an ethics committee was set up inside DeepMind as a condition of their sale to Google.

Lee Se-dol also took ownership of his performance once again, saying that he believes that humans can do more against A.I. Go engines and admitting regret that he was unable to show us how. He reminded the audience of the importance of human creativity, said he began to question some classical beliefs about the game “a little bit”, and indicated that he had more studying to do.

AlphaGo’s skill with the stones is not superior to that of top human professionals, according to Lee Se-dol, its advantages are concentration and the psychological facets of the game.

During the ceremonies that followed the game, AlphaGo was awarded an honorary rank of 9 dan by the Korean Baduk Association.

This match is over but its culmination should be considered a beginning rather than an ending. Google DeepMind have executed a historic début, winning five-nil against Fan Hui (2 dan professional) towards the end of 2015 and four-one against Lee Se-dol, but AMD and Facebook and other contenders are yet on their way to the party. AlphaGo developed its strength through self-play – what will happen when these learning algorithms start to train against each other, like human insei, and new blood-lines are introduced to this gene pool?

In the coming months, we will learn more details about what happened in these five games. We will learn what Google DeepMind plan to do with their creation and how it will impact the worlds of Go, artificial intelligence and machine learning. We will see how Lee Se-dol moves forward in his own, human career. We live in exciting times.