Machine learning goes beyond theory to beat human poker champs

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How to deal with a breakdown in theoretical support in machine learning? Researchers from Carnegie Mellon and Facebook describe winning many hands against the world’s top poker players by inventing smart search strategies to counter a lack of theoretical math used in most game-playing AI.

Among the many achievements of machine learning in recent years, some of the most striking are the victories of the machine against human players in games, such as Google’s DeepMind group’s conquest of Go in 2016. In such milestones, researchers are often guided by theoretical math that says there can be an optimal strategy to be found, given a good algorithm and enough compute.

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