When it comes to games that exercise the mind, a lot of people think of chess as their go-to
answer.
Others, however, especially those in east Asia, might give a different answer: Go.
Go has a number of similarities with chess - both are abstract strategy games with long
histories, both are played by millions of people around the world, and both games have
seen strong interest from the artificial intelligence community in the development of computer agents
that can play at a master human level.
In terms of AI, Go presents a much more difficult game for computers' AIs to handle than chess.
A chess-playing computer program beat the top chess professional in 1997, when IBM's
Deep Blue took on Gary Kasperov.
It took nearly a further two decades, an eternity in technology terms, for a computer to defeat
a top human professional in Go, when Google Deep Ming's Alpha Go took on Lee Sedol in
2016.
In terms of rules, Go is a far simpler game than chess.
There's only one kind of piece, and only four basic rules.
So what is it about Go that makes it so difficult for computers to play?
There are two major reasons.
First, the search space for Go is much larger than it is for chess.
A large component of traditional strategies for chess AIs includes digging down in game
trees and exploring as many combinatorial possibilities for moves as possible.
This is much less viable for Go.
A single turn in Go consists of the placement of a stone on one intersection in a 19-by-19
grid.
In an average turn in Go, a player might have over 200 unique possible moves.
Compare this to chess, where you might have an average of about 35-moves per player.
In addition, a typical game of chess might be about 40-moves per player; compare that
to Go, where a game can be over 100-moves per player.
Because of this, the exponential explosion from exhaustive searches are far more taxing
for Go than for chess.
Second, the objective of the game and the heuristics for evaluating which player is
in a better position are much clearer for chess than they are for Go.
In chess, the objective is clear - capture the king, and there are some basic heuristics
that can be learned to evaluate which player is performing better.
For example, you might've heard the heuristic of counting captured pawns as one point, knights
and bishops as three points, and so on.
Heuristics like this are used for chess AIs to focus on parts of the game tree to search
down.
In Go, there isn't an easy heuristic to figure out who is doing better.
The objective of the game is to surround the most territory, but it can be difficult to
count territory as complete secure until the borders are complete.
One player might make some solid corner territory early in the game, but the opponent might
be compensated with a strong wall on the outside, which has the potential to worth more.
In order to succeed in Go, a computer program cannot use brute force alone.
An AI needs to emulate the pattern matching and intuition that humans perform in order
to short-cut the search-base and explore only the most promising moves.
The addition of deep neural networks to emulate this human-like thinking is a major contributor
to Alpha Go's success.
Not just its ability to play at the top level, but also its ability to generate moves that
defy conventional wisdom.
Ever since Alpha Go's games about Lee Sedol, Go players at all levels have been trying
to learn from its style of play.
In January of 2017, Alpha Go played 60 online matches against some of the world's top players,
including the top professionals of China, Korea and Japan without losing a single game.
Now, in just a few short days, Alpha Go will show its continued learning in a series of
games in China, including a three-game match against the world's top Go player, Ke Jie.
Go is a game that's easy to learn, but it's also a game that you'll learn from for an
entire lifetime.
I've only been playing for about a year, so I'm still a fairly poor player, but because
of how little I know, I'm constantly seeing and learning new things.
The main appeal of Go to me is the beauty of how the game flows from its simple rules.
Hopefully this video has given an insight into what makes Go such a complex game for
computers, and what makes it tricky for humans as well.
But the best way of understanding is to try it for yourself, and in only a few minutes,
you'll be ready to play your first game.