Chennai Chess Olympiad and IA

In 2021, Zerodha founder Nikhil Kamath beat five-time world chess champion Vishwanathan Anand with the help of computers (he later confessed) at a celebrity fundraiser. The controversy has sparked discussions around the use of AI in chess.

As India is set to host the 44th Chess Olympiad in Mahabalipuram from July 28, let’s take a look at how AI has impacted the game of chess.

Chess AI

The first mention of chess technology dates back to the 18th century when Austrian Empress Maria Theresa commissioned a chess playing machine. Many gamers have faced the “Mechanical Turk”, believing it to be an automated machine. However, it turned out to be a scam. A human hidden inside the machine operated it.

In the mid-1940s, British mathematician Alan Turing began theorizing how a computer could play chess against a human. In 1949 Claude Shannon published a seminal paper outlining a potential program to do just that. In 1950, Alan Turing created a program capable of playing chess. Soon after, the chess program of Dietrich Prinz and Bernstein burst onto the scene.

Computer chess first appeared in the 1970s. MicroChess, the first commercial chess program for microcomputers, in 1976; Chess Challenger in 1977; and Sargon, who won the world’s first computer chess tournament for microcomputers, in 1978.

Robotic chess computers appeared in the 1980s. Boris Handroid, Novag Robot Adversary and Milton Bradley Grandmaster are some examples. The most popular was Chessmaster 2000, which ruled the chess video and computer game industry for the next two decades.

As chess computers grew in popularity in the 1980s, Gary Kasparov, then world chess champion, claimed that AI-driven chess engines could not defeat the chess grandmasters of high level. However, in 1989 and 1996 Kasparov beat IBM’s powerful chess engines Deep Thought and Deep Blue.

Things started to change in the late 1990s. In 1997, Deep Blue defeated Kasparov. A year later, Kasparov came up with the idea of ​​Cyborg Chess or Centaur Chess, in which human and computer skills are combined to raise the level of the game. The first game of Cyborg Chess took place in 1998.

In 2017, AlphaZero, a computer program developed by DeepMind, defeated Stockfish, the most powerful chess engine in the world. AlphaZero used the reinforcement learning technique in which the algorithm mimics the human learning process to train its neural networks.

In 2018 TalkChess.com released Leela Chess Zero, developed by Gary Linscott (who also developed Stockfish). Without having any specific knowledge of chess, Leela Chess Zero learned the game based on deep reinforcement learning using an open source implementation of AlphaZero.

In 2019, DeepMind came up with another reinforcement learning based algorithm called MuZero.

Use case

Chess players use AI-driven chess engines to analyze their games and those of their competitors. As a result, AI has helped improve the quality of games.

After the pandemic, many chess competitions went online. At the European Online Chess Championship, up to 80 participants were disqualified for cheating. FIDE, the international chess body, has approved an artificial intelligence-based behavior tracking module for FIDE Online Arena games. Chess.com, an Internet chess server, uses a cheat detection system to gauge the likelihood of a human player matching the moves of a chess engine or outperforming the games of some of the greatest players in the game. chess using a statistical model. DeepMind is also working on developing new cheat detection software.

AI has also reduced training cost and effort and helped develop new chess strategies.

AI challenges

AI has indeed changed the dynamics of the game. However, the use of AI in chess has raised some issues. Computer chess engines have greatly improved gameplay. However, people have also raised concerns that gamers at this age rely too much on machine-driven analysis.

Even when it comes to detecting cheating, the AI ​​poses some problems. First, it is possible for a player to be flagged incorrectly by the AI. For example, a Chess.com player and grandmaster, Akshat Chandra, was banned after a win over Hikaru because his moves supposedly matched Komodo, a powerful positional chess engine. Although Chandra was cleared, his reputation took a hit.

Chess engines and neural networks based on deep learning offer enormous possibilities. Additionally, the complex nature and strategic focus of the game provided a basis for evaluating any progress in the field of artificial intelligence. “They (games) are the perfect platform to develop and test AI algorithm ideas. It’s very efficient to use games for AI development, because you can run thousands of experiments in parallel on computers in the cloud and often faster than real-time, and generate as much data from training your systems need to learn. Conveniently, games also normally have a clear goal or score, so it’s easy to measure the progress of the algorithms to see if they’re gradually improving over time, and therefore if research is going in the right direction said Demis, co-founder of DeepMind. Hasabis.

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