To those of you who have an interest in chess or who have been monitoring recent developments in artificial intelligence the name "AlphaZero" will be instantly recognisable; its victory over the then-leading chess engine in the world, Stockfish, had revolutionised the way that chess is played by both computers and, indeed, humans. However, if you aren't a chess aficionado or have missed the news a couple of years ago, you might be wondering what exactly this AlphaZero really is, and what makes it worth writing an entire blog post about. For you, I will explain. In short, AlphaZero is a game-playing program that, through a combination of self-play and neural network reinforcement learning (more on that later), is able to learn to play games such as chess and Go from scratch that is, after being fed nothing more than the rules of said games. In fact, a newer derivative of AlphaZero, called MuZero, isn't limited to only board games such as chess, but can also learn to play a range of simple video games from the Atari collection.

I have come to the personal conclusion that while all artists are not chess players, all chess players are artists. Originally called Chaturanga, the game was set on an 8x8 Ashtāpada board and shared two key fundamental features that still distinguish the game today. Different pieces subject to different rules of movement and the presence of a single king piece whose fate determines the outcome. But it was not until the 15th century, with the introduction of the queen piece and the popularization of various other rules, that we saw the game develop into the form we know today. The emergence of international chess competition in the late 19th century meant that the game took on a new geopolitical importance.