Books "Probably Approximately Correct," by Leslie Valiant

Published on October 3rd, 2013 | by Travis Korte


“Probably Approximately Correct,” by Leslie Valiant

In Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World, author Leslie Valiant shows how machine learning principles arise in the evolution of life and allow organisms to predict things about their environments without needing to formulate theories first. Valiant, a professor of computer science at Harvard, calls these processes “ecorhithms,” and explains the core computer science principles behind them without burdening nontechnical readers with code. The book has been the topic of considerable discussion in computational biology, due to its argument that Darwin’s theory of evolution is incomplete; Valiant makes the case that Darwin’s theory does not adequately predict the rapid rate at which evolution occurs and ought to be supplemented by additional computational theory.

The book’s title was derived from “probably approximately correct learning,” a machine learning paradigm proposed by Valiant in 1984. Valiant won the Turing Award, computer science’s highest honor, in 2010.

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About the Author

Travis Korte is a research analyst at the Center for Data Innovation specializing in data science applications and open data. He has a background in journalism, computer science and statistics. Prior to joining the Center for Data Innovation, he launched the Science vertical of The Huffington Post and served as its Associate Editor, covering a wide range of science and technology topics. He has worked on data science projects with HuffPost and other organizations. Before this, he graduated with highest honors from the University of California, Berkeley, having studied critical theory and completed coursework in computer science and economics. His research interests are in computational social science and using data to engage with complex social systems. You can follow him on Twitter @traviskorte.

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