DeepMind, AlphaGo and the future of computing

Not that long ago, a computer besting a human in a complex game of strategy and skill was nigh unthinkable. The best chess players in the world were far better at the ancient game than their silicon brethren. While chess is a beautiful and fluid game, and the best players employ creativity, unpredictability and gambles, it is ultimately quantifiable. By that nature, it was only a matter of time before computers got better than us at it.

Every piece in chess has an approximate point value assigned to it given its importance to the overall game, as well as the capabilities of that piece (in the classic mode of point assignments, a pawn is worth 1 point, knights and bishops are worth 3, rooks 5, and the all powerful queen nets you 9 points). Now, the true strength and importance of any piece is massively dependent on the style of game, position of that game, stage of the game, etc. But, it gives you a natural way to score a player’s advantage in any given situation.

On top of that, there are strategic advantages a player can achieve based on the board and the position of pieces therein relative to one’s opponents. While chess provides a massive amount of possible game configurations from start to finish of unlimited games, on any given turn there are only so many moves you as a player can make. The very best players can assess their relative position and then simulate many, many moves in advance to achieve a positional or piece advantage in the future. It requires otherworldly focus and mental horsepower to run through so many decision trees in one’s head — each piece has a host of moves it can make, your opponent has a host of moves s/he can make in response, and on and on for a great many number of combinations.

One of the great advantages a human player had over computers in the beginning was twofold — one, a human brain is just a much more powerful computational tool than the earliest supercomputers. Two, humans are much better at prioritizing information than a computer is. One of the things that allows the best players to look 12 or more moves in advance is that grandmasters know certain moves or certain decision trees are dumb ideas, or that they’d never happen, so the grandmaster can skip the computation required to run out those decision trees while instead focusing on the decision trees far more likely to a) occur, and b) result in a victory. Because of these, the earliest games against computers went in favor of the top human chess players. But that wouldn’t always hold.

Because of the computational nature of chess, there would come a time when computing power would rival that of the human brain. Eventually, you could throw enough CPUs and computational power at one specific task, and the computer would be able to out-compute humans. It was only a matter of time. If you’re requiring a computer to make calculations based on data input, the more processors you assign to that task, the faster it can make the computation. You keep shrinking the size of the processor and adding more and more and more to the computer architecture, you’re eventually going to have enough horsepower to rival or surpass a human.

Hardware architects passed that point a while ago. Nowadays, the top computer programs running on not even cutting-edge supercomputers can reliably beat the best of the best humans. There are only so many decision trees, so many historical games to study, so many combinations of moves from a given position, that a powerful enough computer can simply throw enough brute computing force at the “equation” and get an answer. It didn’t require creativity or transformational innovation to win this battle — the computer isn’t thinking in the traditional sense; it’s simply so powerful, it can compute every move in every decision tree possible from the current board and make a mathematical conclusion of what move gives it the greatest chance of achieving victory in the future.

The same cannot be said of Go.

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