The AI with an Intuition

“AlphaGo” has defeated the human champion of the world’s most complex game by speculating about the future

By Owen Wiseman

If each atom in our universe were itself a universe, then the total number of atoms in all those universes combined would be about the same as the number of possible positions on the board in the ancient Chinese game of “Go.” The game’s complexity makes chess look like hopscotch. Until recently, the world’s leading minds in artificial intelligence predicted that the world’s smartest AIs — which have beat humans at chess by brute force for years now — were still a long, long way from “solving” Go.

Except, it just happened. On March 12, a computer program called AlphaGo played a five-game match against 18-time world Go champion Lee Sedol and defeated him, 4–1, after Lee and other top professionals predicted he could win 5–0 or 4–1. AlphaGo, developed by Google-owned British company DeepMind, has been awarded the rank of 9-dan, the highest possible professional Go ranking that takes human players decades to achieve. The computer did it in a matter of months, leading publications around the globe to call this moment in the man-versus-machine race “a huge breakthrough” (WIRED) and “the beginning of the end” (Quartz).

A man and woman play Go in Korea between the years of 1910 and 1920/ Public Domain via Wiki Commons

AlphaGo uses two neural networks­­­ — computer systems modeled on the human brain — to create strategies and choose individual moves. The first neural network uses traditional “brute force” analysis to consider all possible game outcomes based on hypothetical moves. This is the kind of gaming that computers have excelled at for years, but that could not solve the complexity of Go. AlphaGo, however, has a second neural network that analyzes online games played by humans to determine the likelihood of certain moves, and it tests their strategic value by playing against slightly modified versions of itself. The result is an approximation of human creativity in strategic thinking. And this has the world of AI in a tizzy. For the first time, a computer player has demonstrated what might be called an “intuition” about what is a winning move. It is still a long way from true machine sentience, but it shows that true sentience may matter a lot less than had been assumed.

The profound leap forward that AlphaGo represents became undeniable one hour and eighteen minutes into the match’s second game. AlphaGo had already won the first game, but nobody was sure what that meant. Commentators were reaching for justifications, from overconfidence on Lee’s part to the novelty of nonemotional move selection to just plain luck. Then AlphaGo played what looked to an amateur eye like an unremarkable move, and a hush fell over the commentators and audience. After that move, that game, and the games that followed, nobody used the word “luck” anymore.

Go analysts speak of “sensing” a move rather than “planning” one. The game is human, emotional, and intuitive rather than computational or mechanical.

The English-language YouTube stream of the match had commentary by Michael Redmond, a 9-dan professional player and the highest-ranked player ever of Western origin. Redmond’s face at the moment of the move in question can be described by one word: baffled. It even caused Lee — AlphaGo’s human opponent and perhaps the greatest Go player of all time — to leave his seat and exit the room to gather his thoughts.

It’s not particularly important that you understand that move or why it was so special, but since some exposition of what Go is and how it’s played is inevitable, here it is: Professional Go is played with black and white stones on a board with a 19x19 grid. One player plays white and the other player plays black, and they take turns placing stones on the board. They can place stones anywhere, and once stones are placed, they cannot be moved. The object of the game is to capture areas of the board by walling them off with stones, and also to capture the opponent’s stones by surrounding them.

Amateur Go is often played in runs, with stones being placed side-by-side in long lines as players compete directly to gobble up territory while only planning a move or two ahead. Professional Go, however, in which all the players are thinking strategically, often results in moves that are a few spaces away from other stones. These moves are designed to take territory — to prevent certain moves and force other moves by the opponent, assuming that the opponent is also thinking several moves ahead and will thus see the trap being laid. High-level Go analysis often uses adjectives like “strong” and “weak,” and “protected” and “vulnerable,” rather than “good” or “bad.” The analysts speak of “sensing” a move rather than “planning” one. The game is human, emotional, and intuitive rather than computational or mechanical.

A game of Go in Shanghai, China/ Photography by Brian Jeffery Beggerly. Public Domain via Wiki Commons.

Here is the proof of that transcendent, breakthrough moment: Redmond, who has studied the game for decades, could not tell until much later in the game whether AlphaGo’s move was wise or foolish. All the while AlphaGo’s own internal evaluator — which constantly updated its own perceived chance of winning — was confident of a victory that did eventually arrive. That is creative, strategic genius, which has until this time eluded machines.

Computers’ ability to ape this sort of human intuition has profound implications for our relationship with machines. Many routine jobs are already being automated, and the humans who used to do them are being rendered vestigial. But if computers can be creative strategists, it’s only a matter of time until they learn to mimic the whole spectrum of creativity. Therefore, isn’t all our usefulness on borrowed time? Can AIs become scientists, CEOs, or even artists and writers? Are we all like Redmond, giving our lives in pursuit of esoteric mastery that will be surpassed by an AI after a few months of study?

Sadly, no. We should be so lucky as to be Redmond or Lee. Their defeat has honor and the high drama of maximized human potential beaten by something more. The rest of us will be consigned to obsolescence far less savage but far more terrifying. And to see its character, we can turn again to that single moment in Game 2 of AlphaGo’s match against Lee. There is another layer to what happened that is a truer analog to our fate.

During the match, Lee sat on one side of the board while on the other side sat the computer’s proxy, a man who looked at AlphaGo’s moves on a computer screen and placed the stones on the board where instructed. Redmond and his co-commentator were broadcasting from a different room while observing the game moves in two different ways. They had a camera directly on the game board, but they also had a computer screen displaying the game’s moves, which were entered for internet broadcast by someone else in the game-board room.

When the crucial move occurred, the person inputting them to the server entered the wrong move. The move was so surprising that it even caused a human error in transcription. Redmond’s bafflement wasn’t just at the move itself, he was confused by the computer server and the camera on the board displaying two different moves.

Our fate is not to be Redmond or Lee. Our fate is to be the error-prone inputters, the transcribers, the stone-placers for the machines we will create. The tyranny of machines will not be to put us in slavery. This world is not to be The Matrix. Machines will possess the tyranny of the older brother and his friends on the basketball court, and we will be the younger brother trying to keep up — all willing spirit and weak flesh, sent off to fetch cool drinks when the real game starts but always drawn back by the promise of knowledge and delights beyond our understanding. We will fight to be equal, and when that battle is lost we will try at least to be useful. But we will know in our hearts that the machines are humoring us, and some day the machines will know it too.

Lee lost the first three games of the challenge match with AlphaGo but made a series of beautiful moves in the mid-game to win the fourth. He lost the fifth game. No one was surprised anymore. Lee probably went home that night, ate dinner, and tried to forget for a while that he had been bested. Maybe, if he’s a well-adjusted individual, he even took time to be thankful for the blessings of a life doing something he loves. The computer just went back to analyzing games of Go.

Owen Wiseman hails from the Pacific Northwest. He attended Pomona College, and now works in the film industry. He is pretty sure he is not actually a computer program. Follow him on Twitter.