Fortunately, the exact opposite has happened. Chess engines have helped us to rediscover the game and have opened up new possibilities. Chess is gaining much popularity in recent times. Much of it is contributed to a recent release of a popular mini-series “The Queen’s Gambit”, watched by 62 million households. Also, many popular streamers such as GM Hikaru Nakamura are promoting chess on modern platforms. Additionally, due to the current stay-at-home situation, chess has gained huge popularity in online chess platforms. The number of chess players on chess.com went up by 500% last year alone. Chessboard purchases went up 250% on eBay. The number of Google queries on “how to play chess” is on the record high in the last 9 years. The original novel “The Queen’s Gambit” became the new New York Times bestseller 37 years after its release.
After the Deep Blue match, chess engines, of which Stockfish is the most popular today, have evolved and allowed us to analyze games with more precision. We have used these chess engines as a learning tool. These computer programs allow anybody to have a few virtual grandmasters (highest title in chess) at home mentoring on what is a good or bad move. Previously this was time-consuming to do manually, especially for new players. Hence, what we see today is a larger number of younger grandmasters that can develop their skills by playing against chess engines. Previously, it was not as easy to get hold of human grandmasters to play against for practice. So indeed, over the years, people have realized, contradictory to the initial fears, that computers, as learning tools, can allow us to become better chess players ourselves.
So this combinatorial explosion in chess is problematic for both humans and computers. So, how do human players sometimes evaluate 10 or move moves ahead, this should be impossible (900^10 possibilities)?! Here is the trade secret: strong players do so by evaluating only the most likely moves and the better a player is the better he/she knows what is worth evaluating. There is no point in evaluating all possibilities, but rather they rely on their experience, common sense, theory, or intuition of which moves make sense to be evaluated. They indeed call it “intuition”, but we can also call it trained pattern recognition.
Previous chess engines, such as Stockfish, Deep Blue, and others were not too smart when it came to this “intuition”, but rather relied on heavy computational capabilities to calculate many moves ahead, some of which could have been skipped in the evaluation. Those engines did employ some strategies to reduce this search space, such as alpha-beta pruning, or the mentioned custom rules on what to look for. However, this was nothing close to the “intuition” that the grandmasters have developed through playing chess. Generally, these chess engines often play moves that are non-intuitive moves compared to human moves, so it was hard to learn from those moves for us as humans as we do not possess that computational capacity to evaluate millions of positions per second. Although these chess engines were by many degrees stronger than any human player, it was lacking the style and beauty of how humans play and understand the game in an open and dynamic way, mostly due to our brain’s limitation to exhaustively compute every single position and, thus, we as humans often just “go with our guts”.
The key to surpass Stockfish and thus any human player by AlphaZero, was for it to play against itself. After each game, the outcome was measured, and the neural network adjusted using reinforcement learning. This allowed AlphaZero to evolve beyond the currently known theory and understanding of chess and yet play games that are more human-like, well maybe, super-human like. Today, chess players can observe fascinating games played by AlphaZero. This has led to a new understanding of chess, new variations in the openings, middle-games, and confirmation that our understanding of chess, in the way grandmasters have always played it, is in line with how AlphaZero plays the game.
To give an understanding of how AlphaZero plays the game, we can closely compare it to how human grandmasters play it. It generally evaluates a position on patterns. Especially on the grandmaster level, you would generally observe a chess position and have an immediate feeling of what is a favorable chess position. Although neither humans nor AlphaZero, can compute why this position is winning in the next 20 or more moves, we just know that it is winning and only need to get ourselves into that position and hope that with good play, we can convert that positional advantage into victory.