#Stockfish chess make ai faster how to#
#Stockfish chess make ai faster software#

In his book, Barrat interviewed the computer science professor Steve Omohundro, who cites chess as a typical application for which human scientists might create a self-improving machine - a machine whose inner workings and ultimate goals would be completely alien to us and potentially uncontrollable. If that’s the future of advanced computing, of course it will be used to play chess. An advanced machine whose genetic origins are not understood by its human programmers would be fundamentally unknowable if it became self-improving or even self-aware, he says. So what does this mean for chess? Barrat wrote a book about the future implications of artificial intelligence, Our Final Invention, where he argues that genetic programming might be one of the foundations for a computer learning to teach itself. Such “black box” systems are already being used to solve various problems where trial-and-error yields good results - computer scientists from Google and Cal-State Sacramento, for instance, created a genetic algorithm computer to play the notoriously difficult sprinting game QWOP. The result, says the computer science author James Barrat, is a computer with accurate and useful output, but whose inner workings are beyond the understanding of its human programmers. Using a programming method called genetic algorithms, a computer can generate optimized solutions by mimicking the process of natural selection in its virtual environment.

#Stockfish chess make ai faster software#

There is, however, computer software that approaches problem-solving much differently. We understand the input (the moves played), we understand the output (the engine’s evaluation), and - most important - we understand the engine’s inner workings, how it arrived at the output. We know how today’s chess computers evaluate positions, and as complicated as an engine’s algorithms may be, it all boils down to simple math in the end. Even though top-level engine games may seem inscrutable and other-worldly, they are the predictable results of each engine’s human-guided programming.

#Stockfish chess make ai faster how to#

This sort of competitive development is unlikely to slow down any time soon, and further advances in computer hardware and software certainly mean that tomorrow’s chess engines will be even stronger than today’s.īut as well as the machines play now, they are still dependent on human programmers and chess instructors to “teach” them how to play. Three months ago, we took a look at the top five computer chess engines, and today the best two programs on that list, Komodo and Stockfish, are significantly better at chess then they were in the spring. Chess-playing computers are almost unfathomably strong, and they are improving faster than even the best human players.