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Tracking the Evolution of Chess: How AI is Impacting the Classic Game

Over the past few decades, there has been a resurgence of interest in the game of chess. This is largely due to the rise of artificial intelligence (AI) and its impact on the classic game. In this blog post, we will take a look at how AI is impacting chess and how it is helping to track the evolution of the game.

The relationship between AI and chess

Chess is a game that has been around for centuries, and it is one of the most popular board games in the world. Chess has been used as a way to test artificial intelligence (AI) since the early days of AI research. In fact, chess was one of the first games that AI researchers attempted to create programs to play. Despite its simple rules, chess is a very complex game. It requires strategic thinking and planning ahead, which are both things that computers are very good at.

Chess has always brought a lot of attention to AI. With one of the most well-known examples being a match of Gary Karparov vs Deep Blue. In 1996, the world was introduced to Deep Blue, a computer program designed to play chess. It could consider as many as 50 million positions in 3 minutes. This program made its debut in a six-game match against Kasparov, the reigning champion of the time. The high stakes of the $500,000 prize fund and IBM's live game coverage drew worldwide attention. The match itself was historic; it was the first time a world champion had played against a computer program in a slow format. Kasparov, a human, won the match 4-2.

In 1997, Deep Blue made its second exhibition match appearance; this time against Kasparov in game six of the same match. IBM was much more prepared for their rematch. The improved Deep Blue worked at the average of 200 million positions per second and was aided by the advice of human grandmasters. Deep Blue came out victorious.

AI research and Chess

Today, AI is being used in a variety of ways to improve our understanding of the game and create new possibilities for how it can be played. Chess is a good game for AI research because it has well-defined rules, it allows for computation and it has a simple input and output: you either win or you lose. But just like humans, AI programs are not born perfect. They need to be trained by playing many games against themselves or by playing thousands of games against other players.

One AI technique, in particular, has been used to help improve players’ performance. It was coined the Monte Carlo method and was first used to solve chess problems in 1959. It is most frequently used with computers in chess to help identify the best move to make.

Monte Carlo methods use algorithms to simulate many iterations of possible outcomes. They are useful for problems that are hard to solve using other AI techniques, such as when there is no way to determine an exact algorithm.

For example, consider finding the best move to make by calculating all possible future moves. This is a brute-force approach that tries out every variation to find the best move to make. With a game like chess played on an 8 x 8 board, that’s 65,536 possible moves. It is untenable for even the most sophisticated computers. So instead of finding the best move, Monte Carlo methods find a good-enough move.

The algorithm used to determine the best possible move for a particular chess situation can be difficult for a computer to narrow down for two reasons. First, the number of possible moves is too high for a computer to evaluate each one. Second, the game can have many outcomes, including a win, loss or draw. A simple game like tic-tac-toe has three possible outcomes. But chess has more than 10^120 possible different board positions.

Chess Today

AI chess programs are now able to beat even the best human players, and they are helping to track the evolution of chess. The use of AI in chess is providing scientists with valuable data about how the game evolves and by studying how AI programs play chess, researchers can learn about how different strategies emerge and change over time.

For chess players, chess engines provide a helpful and friendly guide to anyone who wants help with opening theory, and they have influenced chess strategy to a surprising degree, thanks to the ability to analyse many thousands of tablebases. They have a had a major impact on the way chess is taught and learnt.

There are a number of top-rated chess coaches who are using chess engines to analyse their students' play, using them to find flaws in their thinking, to reinforce certain moves, and to identify areas for improvement. A surprising number of the world's top players too are now using chess engines on their smartphones during tournaments, so that they can analyse without leaving a table or their chair. This has become part of the new generation of chess players' lives.

With the popularity of online chess and the increasing interest in it during lockdown, AI has never been more relevant to chess. The computers have increased the game’s popularity, attracting new players and expanding the game’s influence around the world.


Chess has been significantly altered by computers, for better or worse. Some contend that the game has become more standardized as a result of computers, which have increased knowledge at the price of innovation, intrigue, and vibrancy. They have merged with every facet of chess, from professional play to amateur study to the enjoyment of spectators.

At the same time, the game is more accessible and less costly, while still being one of the highest forms of intellectual and physical endeavor. AI has merged with every facet of chess, from professional play to amateur study to the enjoyment of spectators.

As a result, the modern game of chess itself has undergone a renaissance, or perhaps a revolution.


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