Behind Deep Blue

By Feng-Hsiung Hsu. 2022

259 citations recorded by the source corpus.

From the Publisher: On May 11, 1997, as millions worldwide watched a stunning victory unfold on television, a machine shocked the chess world by defeating the defending world champion, Garry Kasparov. Written by the man who started the adventure, Behind Deep Blue reveals the inside story of what happened behind the scenes at the two historic Deep Blue vs. Kasparov matches. This is also the story behind the quest to create the mother of all chess machines. The book unveils how a modest student project eventually produced a multimillion dollar supercomputer, from the development of the scientific ideas through technical setbacks, rivalry in the race to develop the ultimate chess machine, and wild controversies to the final triumph over the world's greatest human player. In nontechnical, conversational prose, Fenghsiung Hsu, the system architect of Deep Blue, tells us how he and a small team of fellow researchers forged ahead at IBM with a project they'd begun as students at Carnegie Mellon in the mid-1980s: the search for one of the oldest holy grails in artificial intelligence -- a machine that could beat any human chess player in a bona fide match. Back in 1949 science had conceived the foundations of modern chess computers but not until almost fifty years later -- until Deep Blue -- would the quest be realized. Hsu refutes Kasparov's controversial claim that only human intervention could have allowed Deep Blue to make its decisive, "uncomputerlike" moves. In riveting detail he describes the heighteing tension in this war of brains and nerves, the "smoldering fire" in Kasparov's eyes. Behind Deep Blue is not just another tale of man versus machine. This fascinating book tells us how man as genius was given an ultimate, unforgettable run for his mind, no, not by the genius of a computer, but of man as toolmaker.

Subfields: Computer Vision and Pattern Recognition, Education, Sociology and Political Science, Transportation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Education

In Sociology and Political Science

In Transportation

Citations in the corpus

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (2017, background): “Chess subsequently became the grand challenge task for a generation of artificial intelligence researchers, culminating in high-performance computer chess programs that perform at superhuman level (9, 14).”
Giraffe: Using Deep Reinforcement Learning to Play Chess (2015, background): “…could not have been searching more than 3-5 positions per second, while Deep Blue, a supercomputer with 480 custom ”chess processors”, searched about 200 million positions per second [1][2] to play at approximately equal strength (Deep Blue won the 6-game match with 2 wins, 3 draws, and 1 loss).”
Google Research Football: A Novel Reinforcement Learning Environment (2019, methodology): “Reinforcement learning algorithms have been successfully applied to board games such as Backgammon (Tesauro 1995), Chess (Hsu 2004), or Go (Silver et al. 2016).”
Agents, Bodies, Constraints, Dynamics, and Evolution (2009, background): “Incidentally, there was an amazing, successful effort to get chess programs to the point where they beat the world champion (Hsu 2002).”
Large Language Models (2023, background): “Symbolic methods have had considerable success at many tasks requiring intelligence, famously including chess playing [60] and symbolic algebra 8 as well as more prosaic but very central tasks such as translating high level computer languages to machine code (compiling).”