One of the last bastions of human mastery over computers is the game of Go—the best human players beat the best Go engines with ease.
That’s largely because of the way Go engines work. These machines search through all possible moves to find the strongest.
While this brute force approach works well in draughts and chess, it does not work well in Go because of the sheer number of possible positions on a board. In draughts, the number of board positions is around 10^20; in chess it is 10^60.
But in Go it is 10^100—that’s significantly more than the number of particles in the universe. Searching through all these is unfeasible even for the most powerful computers.
So in recent years, computer scientists have begun to explore a different approach. Their idea is to find the most powerful next move using a neural network to evaluate the board. That gets around the problem of searching. However, neural networks have yet to match the level of good amateur players or even the best search-based Go engines.