Collaborating Authors

Implementing a Chess engine from scratch


As a humble Chess amateur, I gave myself this challenge: to develop a simple, good-looking Chess game with AI that can beat me, without Machine-Learning. This article is about my journey to achieve it and is composed of 4 parts: rules, computation, strategy, and playing. And just to make things clear, I also decided not to read theoretical or algorithmic explanations on Chess engines, I wanted to build my own algorithm, based on my common sense and personal experience. I named it Bobby, as a tribute to Robert "Bobby" James Fischer, who has been World Chess Champion and one of my heroes as a young player.

How the AI Revolution Impacted Chess (2/2)


In 2019, Dubov introduced many new ideas in a rare variation of the Tarrasch Defense, which quickly attracted attention at the top level. Several of the world's best players have tried it, including Carlsen who employed it successfully in the 2019 World Rapid and Blitz Championships. Dubov's double-edged opening system is based around concepts that are suggested by the newer engines, including early h-pawn advances and pawn sacrifices for the initiative. Note that both game annotations are based on work I did for my book, The AI Revolution in Chess. At the top level these days, everyone uses neural network (or hybrid) engines.

10 Positions Chess Engines Just Don't Understand


Since IBM's Deep Blue defeated World Chess Champion Garry Kasparov in their 1997 match, chess engines have only increased dramatically in strength and understanding. Today, the best chess engines are an almost incomprehensible 1,000 Elo points stronger than Deep Blue was at that time. A quick Google search for terms such as "Magnus Carlsen versus Stockfish" turns up numerous threads asking if humans can compete against today's top chess engines. The broad consensus seems to be that the very best humans might secure a few draws with the white pieces, but in general, they would lose the vast majority of games and would have no hope of winning any games. I see no reason to disagree with this consensus. Despite the clear superiority of engines, there ARE positions which chess engines don't (and possibly can't) understand that are quite comprehensible for human players.

Kasparov on Deep Learning in chess


Many years ago I was with Garry Kasparov for an event in London's Home House, and there we had dinner with a young lad, a former child prodigy in chess, one who had reached master level (Elo 2300) at the age of 13 and captained a number English junior chess teams. It was an interesting encounter with the boy enthusiastically describing a computer game he was developing. After he left I said to Garry: "That's a cocky young fellow!" "But very smart," Garry replied. And we left it at that.

Untold History of AI: Charles Babbage and the Turk

IEEE Spectrum Robotics

The history of AI is often told as the story of machines getting smarter over time. What's lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies. In this six-part series, we explore that human history of AI--how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While it can be exciting to be swept up by the idea of super-intelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are. In the year 1770, at the court of the Austrian Empress Maria Theresa, an inventor named Wolfgang von Kempelen presented a chess-playing machine.