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Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World
From a leading computer scientist, a unifying theory that will revolutionize our understanding of how life evolves and learns.
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About the author
Leslie Valiant
9 books15 followersLeslie Valiant FRS is a British computer scientist and computational theorist. He is currently the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University, and was educated at King's College, Cambridge, Imperial College London, and University of Warwick where he received a PhD in computer science in 1974.
Valiant is world-renowned for his work in theoretical computer science. Among his many contributions to complexity theory, he introduced the notion of #P-completeness to explain why enumeration and reliability problems are intractable. He also introduced the "probably approximately correct" (PAC) model of machine learning that has helped the field of computational learning theory grow, and the concept of holographic algorithms. His earlier work in automata theory includes an algorithm for context-free parsing, which is (as of 2010) still the asymptotically fastest known. He also works in computational neuroscience focusing on understanding memory and learning.
Valiant's 2013 book is Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World (Basic Books, ISBN 9780465032716). In it he argues, among other things, that evolutionary biology does not explain the rate at which evolution occurs, writing, for example, "The evidence for Darwin's general schema for evolution being essentially correct is convincing to the great majority of biologists. This author has been to enough natural history museums to be convinced himself. All this, however, does not mean the current theory of evolution is adequately explanatory. At present the theory of evolution can offer no account of the rate at which evolution progresses to develop complex mechanisms or to maintain them in changing environments."
Valiant is world-renowned for his work in theoretical computer science. Among his many contributions to complexity theory, he introduced the notion of #P-completeness to explain why enumeration and reliability problems are intractable. He also introduced the "probably approximately correct" (PAC) model of machine learning that has helped the field of computational learning theory grow, and the concept of holographic algorithms. His earlier work in automata theory includes an algorithm for context-free parsing, which is (as of 2010) still the asymptotically fastest known. He also works in computational neuroscience focusing on understanding memory and learning.
Valiant's 2013 book is Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World (Basic Books, ISBN 9780465032716). In it he argues, among other things, that evolutionary biology does not explain the rate at which evolution occurs, writing, for example, "The evidence for Darwin's general schema for evolution being essentially correct is convincing to the great majority of biologists. This author has been to enough natural history museums to be convinced himself. All this, however, does not mean the current theory of evolution is adequately explanatory. At present the theory of evolution can offer no account of the rate at which evolution progresses to develop complex mechanisms or to maintain them in changing environments."
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