The 1997 match took place not on a standard stage, but rather in a small television studio. The match’s outcome made headlines worldwide, and helped a broad audience better understand high-powered computing. Game 6 ended the match with a crushing defeat of the champion by Deep Blue. The chess grandmaster won the first game, Deep Blue took the next one, and the two players drew the three following games. The IBMers knew their machine could explore up to 200 million possible chess positions per second. The odds of Deep Blue winning were not certain, but the science was solid. The champion and computer met at the Equitable Center in New York, with cameras running, press in attendance and millions watching the outcome. The human chess champion won in 1996 against an earlier version of Deep Blue the 1997 match was billed as a “rematch.” There, they continued their work with the help of other computer scientists, including Joe Hoane, Jerry Brody and C. A classmate of his, Murray Campbell, worked on the project, too, and in 1989, both were hired to work at IBM Research. In 1985, a graduate student at Carnegie Mellon University, Feng-hsiung Hsu, began working on his dissertation project: a chess playing machine he called ChipTest. IBM computer scientists had been interested in chess computing since the early 1950s.
Over the years, many computers took on many chess masters, and the computers lost. The game is a collection of challenging problems for minds and machines, but has simple rules, and so is perfect for such experiments. Taken together, our results suggest that there is substantial promise in designing artificial intelligence systems with human collaboration in mind by first accurately modeling granular human decision-making.Since the emergence of artificial intelligence and the first computers in the late 1940s, computer scientists compared the performance of these “giant brains” with human minds, and gravitated to chess as a way of testing the calculating abilities of computers. For a dual task of predicting whether a human will make a large mistake on the next move, we develop a deep neural network that significantly outperforms competitive baselines. We develop and introduce Maia, a customized version of Alpha-Zero trained on human chess games, that predicts human moves at a much higher accuracy than existing engines, and can achieve maximum accuracy when predicting decisions made by players at a specific skill level in a tuneable way. Applying existing chess engines to this data, including an open-source implementation of AlphaZero, we find that they do not predict human moves well.
The hundreds of millions of games played online by players at every skill level form a rich source of data in which these decisions, and their exact context, are recorded in minute detail. The aggregate performance of a chess player unfolds as they make decisions over the course of a game. We pursue this goal in a model system with a long history in artificial intelligence: chess. A crucial step in bridging this gap between human and artificial intelligence is modeling the granular actions that constitute human behavior, rather than simply matching aggregate human performance. However, the ways in which AI systems approach problems are often different from the ways people do, and thus may be uninterpretable and hard to learn from.
CHESS AGAINST COMPUTER CODE
The code for training Maia can be found on our Github Repo.Īs artificial intelligence becomes increasingly intelligent-in some cases, achieving superhuman performance-there is growing potential for humans to learn from and collaborate with algorithms. If you want to see some more examples of Maia's predictions we have a tool here to see where the different models disagree. If you want to be the first to know, you can sign up for our email list here. We are going to be releasing beta versions of learning tools, teaching aids, and experiments based on Maia (analyses of your games, personalized puzzles, Turing tests, etc.). You can read a blog post about Maia from the Computational Social Science Lab or Microsoft Research.
CHESS AGAINST COMPUTER FULL
Read the full research paper on Maia, which was published in the 2020 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2020).
0 kommentar(er)
