DeepMind’s groundbreaking artificial intelligence, AlphaGo, defeated Lee Sedol 9p in the final game of the Google DeepMind Challenge Match on March 15, 2016, winning the five game match with a 4–1 score.
After Lee pulled off a surprise win in the fourth game, hopes of a repeat performance in the fifth were high amongst Lee’s fans, but it was not to be.
Although Lee got off to a good start in game five, and AlphaGo even made a miscalculation around move 50, the computer’s superior judgment and efficiency of play eventually won the day.
AlphaGo now goes down in history as the first computer Go program to defeat a top professional player, and was awarded an honorary professional 9 dan rank by the Korean Baduk Association (baduk = Go in Korean).
When you’re onto a good thing…
As in game one, Lee (Black) began with territorial moves on the 3-4 points, with Black 1 and 3.
These were met by AlphaGo’s more flexible and center oriented moves on the star points, with White 2 and 4.
Lee enclosed his top right corner with Black 5, claiming the corner territory for himself, and took the bottom right corner territory too (from 7 to 11) after AlphaGo approached at White 6.
It seemed that Lee was following a similar game plan to the one that had worked for him in game four — namely, to take territory early on and confront AlphaGo’s influence later in the game.
No territory for you!
When AlphaGo played to develop potential territory on the right side, with White 12 to 16, Lee resisted with Black 17 instead of extending to P15 and allowing AlphaGo to play around P10.
This move once again focused on taking territory and denying AlphaGo territory on the right side, at the expense of ceding center influence to White with 18.
Lee pressed his advantage in the bottom right with Black 19 and Black 21, attacking White’s three stones, but AlphaGo played flexibly and sacrificed them with 22 and 24.
Though the game was still very close, Lee’s plan seemed to be going well so far.
As negotiations shifted to the top left corner, Black changed tack slightly with Black 31.
The standard local tactic after White 30 would have been to continue moving into the corner at C17, but then White would have played at D14 and practically connected its pincer stone (White 30) to its top left corner.
In the process of doing so, White would have built a wall which greatly expanded its influence over the center.
Black 31 made contact with White’s pincer stone instead, which indicated that Lee wanted to prevent White from closing off the center.
Up to Black 39, Black stabilized his group in the top left and established a beachhead in the center, but allowed White to play on both sides and retain the initiative.
The overall position was still well balanced, and generally speaking this was a sensible strategy for Black.
Nevertheless, Lee appeared to be taking the game in a new direction, instead of repeating his all or nothing strategy from game four.
AlphaGo continued to develop the center from 40 to 46, and then embarked on a complicated tactic to resurrect its bottom right corner stones, from 48 to 58.
Though this sequence appeared to be very sharp, it encountered the crushing resistance of the tombstone squeeze — a powerful tesuji which involves sacrificing two stones, and then one more, in order to collapse the opponent’s group upon itself and finally capture it.
This was a strange and revealing moment in the game.
Like riding a bike
Even intermediate level Go players would recognize the tombstone squeeze, partly because it appears often in Go problems (these are puzzles which Go players like to solve for fun and improvement).
AlphaGo, however, appeared to be seeing it for the first time and working everything out from first principles (though surely it was in its training data).
No matter where AlphaGo played in the corner, Lee was always one move ahead, and would win any race to capture. And he barely had to think about it.
It’s a bit like when you learn to drive a car, ride a bike, or tie your shoelaces. To begin with, you really need to focus on what you’re doing and it’s all very hard.
Remember to look at the road, oops, I’m going to fast, check the mirror, remember to look at the road, change gears… oops, too fast again.
But given time, it gradually becomes second nature, to the point that you barely need to think about what you’re doing at all. You just do it.
For Lee, and all other experienced human players, the moves from Black 49 to 59 were like that. Strangely, and perhaps inexplicably, they were not for AlphaGo.
Demis Hassabis tweeted about AlphaGo’s error during the game:
#AlphaGo made a bad mistake early in the game (it didnt know a known tesuji) but now it is trying hard to claw it back… nail-biting.
— Demis Hassabis (@demishassabis) March 15, 2016
Lee Sedol takes the lead
Because of AlphaGo’s mistake, Lee Sedol took the lead.
However, it wasn’t a commanding lead. Winning a game of Go is like running a marathon, and Lee was only a few points ahead.
Fortunately for AlphaGo, the damage from its mistake was mostly confined to the bottom right corner, involving stones which it had already decided to sacrifice.
Though the machine had stumbled, it still had plenty of chances to catch up and Lee needed to stay focused.
Don’t celebrate too early
There’s a saying we Go players have, about not celebrating too early.
It’s not uncommon for one player to win a small battle early in the game and take the lead, only to have their focus waver and lose the war because they were too pleased about their earlier success.
This has happened to most players at some point, including me…
For a moment I recalled the wonderfully entertaining and varied conspiracy theories we have seen about this match and joked about a new one with a friend.
Maybe AlphaGo deliberately allowed its stones to be captured, to soften Lee’s resolve?
No, I don’t actually believe that’s what happened at all, but despite Lee Sedol’s incredible mental toughness he’s still susceptible to the vicissitudes of the human psyche, just like everyone else.
The bears ambush Goldilocks
White 60 to 68 flowed smoothly and developed the top. It was time for Black to do something about White’s sphere of influence at the top and in the center.
Lee chose the shoulder hit of Black 69, which was a kind of ‘Goldilocks’ move — not too shallow, but not too deep.
Such moves are difficult to deal with, because they stretch just far enough to be annoying, but not so far that a counter-attack is easy.
In other words, they are just right.
Regardless, AlphaGo counter-attacked with White 70, rather than defending at G17 and allowing Black to skip lightly to J16.
An experienced human player would think twice about this attack. It feels good to play, but there’s also a fear that the strategy will backfire if Black can withstand the initial attack.
That’s because trying to capture Black’s stone at G16 could leave White’s potential territory in the center in ruins if White fails to capture after raising the stakes.
In fact, this kind of situation is usually scary for both players. Except, in this case, one of the players felt no fear.
The position was still wide open, so there were all sorts of flexible tactics which Black could have tried.
For example, the elephant’s step to J14 is one idea for contesting the center.
Lee, however, chose the safe and simple strategy of making a living group at the top, from 71 to 79. He clearly believed that this would be good enough.
The broader situation in the match was important here.
After losing the first three games, Lee had lost the match and had nothing to lose. Most observers believed at that point that Lee would lose 5–0.
Given this freedom from expectations, Lee was free to pursue the aggressive and somewhat reckless strategy of gambling the whole game on a single battle in game four.
But now the situation was different. He had won a game, proving that the machine was fallible, and scores of people wanted him to win again.
The difference between 4–1 and 3–2 is significant, because the latter result would have given ‘team human’ room to argue about the result and begged the question: what if it had been a 10 game match?
Lee had something to lose, and this was where the Achilles’ heel of humanity was exposed. Creeping fear set in.
The danger of being cautious
Lee had already played cautiously up to 79, and when White played at 80 he ensured that he was safe with 81.
But this was too careful.
White harassed Black’s stones at the top starting with 82, and although Black managed to make two eyes (thus ensuring the life of his stones), his efforts at the top only earned him three points in the end.
Meanwhile, far from White’s potential territory being ruined, the computer was able to wall off a large area around the right center, up to White 90.
A moment of caution had allowed AlphaGo bully Black and catch up. The game was even again.
God mode enabled
As the game continued, AlphaGo played a series of unusual (even boring looking) but subtly impressive moves which did not bode well for Lee.
White 100 initially looked too tight, but interestingly avoided approaching Black’s lower right power too closely and exposed a weakness at K7.
For Black’s part, 101 to 105 comprised an impressive combination which reduced White’s center potential and indirectly protected the cutting point at K7.
White 106 didn’t completely secure the lower left corner for White (C5 or C4 instead would), but it helped three stones on the left side after Black 105.
And when Lee invaded White’s bottom left area from 107 to 111, White 112 was an unintuitive but effective way to attack Black (Black 113 was also very nice).
For the rest of the game, there was an Agent Smith-esque feeling of “inevitability” to White’s play, despite Lee’s valiant efforts to catch up.
Once Lee lost his advantage at the top, the pressure on AlphaGo was relieved and it was able to do what it does best — maintain a very small lead, minimize risk and maximize its probability of winning.
To this imprecise human author, it simply felt like god mode had been enabled.
Brief analysis of game five
Here is An Younggil 8p’s preliminary analysis of game five. Further game commentary will be posted over the coming week.
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A territorial opening
Black started with a territorial opening, and White 12 was the start of a cutting edge opening strategy.
Lee had planned his opening with other pros, and his strategy in this game was to take territory first and invade later, which was similar to game four.
Black 17 was a sharp attacking move, and White took influence while sacrificing her three stones from White 20 to 24. The result was well balanced.
Black 31 was the right choice to reduce White’s influence over the center, and the position was still even up to Black 39.
White 40 and 42 were powerful, but Black 43 and 45 were firm responses.
White’s moves from 48 to 59 were unnecessary, and White lost ko threats and aji by playing this way. Black took the lead.
Black 69 was an excellent move to reduce White’s influence, but White’s cap at 70 and the continuation up to 80 were flexible and gentle, maintaining the balance.
White reverses the game
Black 81 was too cautious. Black should have played at White 84 to make a better shape with larger eye potential.
White 82 to 84 were sharp, and the game was reversed in White’s favor.
White 90 and 100 were calm and solid, and it appeared that AlphaGo thought it was ahead.
Black 101 was ingenious, and Black caught up by erasing White’s thickness in the center up to 105.
Still favorable for White
Black 107 and 109 formed a good combination for invading, and Black 113 was a good tesuji, but White’s responses were still patient and solid.
The result up to Black 127 was satisfactory for Black, but White took sente, and the game was still favorable for White.
However, White 136 was questionable, and Black managed to catch up a little up to 147.
The game appeared to be even again, but actually White 154 was big, and White was still slightly ahead.
Lee had been aiming for the Hail Mary pass of 167 and 169, but White’s responses were precise and Black lost a few points up to White 180.
White 186 and 194 were big endgame moves, and the game was practically decided.
Black tried to catch up, but White’s endgame from here on was perfect, and Black didn’t get any chances.
AlphaGo wins the game
In the end, White was winning by 2.5 points, so Lee Sedol resigned.
AlphaGo showed us another impressive game, and eventually it seemed that there wasn’t any clear way for Black to win.
I think that AlphaGo calculated that it was ahead by at least half a point, so it played calmly and solidly in the second half of the game.
Lee Sedol vs AlphaGo – Game 5
Regardless of how you feel about it, the world of Go is going to be very different after this historic match.
As a result of this spectacular contest, we welcome a contingent of new players who have only just heard about the game (hello there!), and public awareness of Go is higher than it has ever been.
My hope is that a reasonable number of new players will stick around and we’ll have a stronger base for promoting Go in the West from now on.
Perhaps we can organize Learn Go Week again later this year?
The future of Go
Already some are concerned that people won’t want to play Go anymore once computers surpass humans, but others point to the continuing popularity of chess after Deep Blue defeated Garry Kasparov, arguing that the level of play is higher than ever.
Personally, I will continue to play and enjoy Go. How about you?
I play Go because it’s beautiful, and fun. It’s a game which never ceases to surprise and fascinate me. There’s nothing that compares to that feeling of being ‘in the zone’.
I’ve made so many friends through Go, and learned so much about myself. I’m really looking forward to playing against something like AlphaGo.
For most of us, the fact that there’s always someone out there who’s better at the game than us has never really mattered before, so why would it now?
The majority of professional players seem to be excited about the arrival of AlphaGo too.
The future of AI
And in the longer term, human society will have to become accustomed to this new technology as it is gradually applied to fields beyond Go. Maybe now is a good time to start talking about that?
As DeepMind CEO Demis Hassabis freely admits, there are difficult conversations which continued advances in artificial intelligence will bring to the fore.
As funny (or scary) as they might be, jokes about Skynet do not do this issue justice.
In the press conference after the match, Hassabis said, “As with all powerful technologies, they bring opportunities and challenges.”
“We think of AI at DeepMind as a powerful tool. A tool to help human experts to achieve more.”
“In general, we believe in open and collaborative research, and we believe in the power of AI to benefit the many, not just the few.”
What’s your take on this? Join the conversation below.