ACM named David Silver the recipient of the 2019 ACM Prize in Computing for breakthrough advances in computer game-playing. Silver is a professor at University College London and a Principal Research Scientist at DeepMind, a Google-owned artificial intelligence company based in the U.K. Silver is recognized as a central figure in the growing and impactful area of deep reinforcement learning.
Silver's most highly publicized achievement was leading the team that developed AlphaGo, a computer program that defeated the world champion of the game Go, a popular abstract board game. Silver developed the AlphaGo algorithm by deftly combining ideas from deep-learning, reinforcement-learning, traditional tree-search and large-scale computing. AlphaGo is recognized as a milestone in artificial intelligence (AI) research and was ranked by New Scientist magazine as one of the top 10 discoveries of the last decade.
AlphaGo was initialized by training on expert human games followed by reinforcement learning to improve its performance. Subsequently, Silver sought even more principled methods for achieving greater performance and generality. He developed the AlphaZero algorithm that learned entirely by playing games against itself, starting without any human data or prior knowledge except the game rules. AlphaZero achieved superhuman performance in the games of chess, Shogi, and Go, demonstrating unprecedented generality of the game-playing methods.
Computer Game-Playing and AI
Teaching computer programs to play games, against humans or other computers, has been a central practice in AI research since the 1950s. Game playing, which requires an agent to make a series of decisions toward an objective—winning—is seen as a useful facsimile of human thought processes. Game-playing also affords researchers results that are easily quantifiable—that is, did the computer follow the rules, score points, and/or win the game?
At the dawn of the field, researchers developed programs to compete with humans at checkers, and over the decades, increasingly sophisticated chess programs were introduced. A watershed moment occurred in 1997, when ACM sponsored a tournament in which IBM's DeepBlue became the first computer to defeat a world chess champion, Gary Kasparov. At the same time, the objective of the researchers was not simply to develop programs to win games, but to use game-playing as a touchstone to develop machines with capacities that simulated human intelligence.
"Few other researchers have generated as much excitement in the AI field as David Silver," said ACM President Cherri M. Pancake. "Human vs. machine contests have long been a yardstick for AI. Millions of people around the world watched as AlphaGo defeated the Go world champion, Lee Sedol, on television in March 2016. But that was just the beginning of Silver's impact. His insights into deep reinforcement learning are already being applied in areas such as improving the efficiency of the UK's power grid, reducing power consumption at Google's data centers, and planning the trajectories of space probes for the European Space Agency."
"Infosys congratulates David Silver for his accomplishments in making foundational contributions to deep reinforcement learning and thus rapidly accelerating the state of the art in artificial intelligence," said Pravin Rao, COO of Infosys. "When computers can defeat world champions at complex board games, it captures the public imagination and attracts young researchers to areas like machine learning. Importantly, the frameworks that Silver and his colleagues have developed will inform all areas of AI, as well as practical applications in business and industry for many years to come. Infosys is proud to provide financial support for the ACM Prize in Computing and to join with ACM in recognizing outstanding young computing professionals."
Silver is credited with being one of the foremost proponents of a new machine learning tool called deep reinforcement learning, in which the algorithm learns by trial-and-error in an interactive environment. The algorithm continually adjusts its actions based on the information it accumulates while it is running. In deep reinforcement learning, artificial neural networks—computation models which use different layers of mathematical processing—are effectively combined with the reinforcement learning strategies to evaluate the trial-and-error results. Instead of having to perform calculations of every possible outcome, the algorithm makes predictions leading to a more efficient execution of a given task.
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