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The Importance of Being Educable: A New Theory of Human Uniqueness
If we hope to share our planet successfully with one another and the AI systems we are creating, we must reflect on who we are, how we got here, and where we are heading. The Importance of Being Educable puts forward a provocative new exploration of the extraordinary facility of humans to absorb and apply knowledge. The remarkable "educability" of the human brain can be understood as an information processing ability. It sets our species apart, enables the civilization we have, and gives us the power and potential to set our planet on a steady course. While we can readily absorb systems of thought about worlds of experience beyond our own, we struggle to judge correctly what information we should trust.
In this visionary book, Leslie Valiant argues that understanding the nature of our own educability is crucial to safeguarding our future. After breaking down how we process information to learn and apply knowledge, and drawing comparisons with other animals and AI systems, he explains why education should be humankind's central preoccupation.
Will the unique capability that has been so foundational to our achievements and civilization continue to drive our progress, or will we fall victim to our vulnerabilities? If we want to protect our collective future, we must better understand and prioritize the importance of being educable.
In this visionary book, Leslie Valiant argues that understanding the nature of our own educability is crucial to safeguarding our future. After breaking down how we process information to learn and apply knowledge, and drawing comparisons with other animals and AI systems, he explains why education should be humankind's central preoccupation.
Will the unique capability that has been so foundational to our achievements and civilization continue to drive our progress, or will we fall victim to our vulnerabilities? If we want to protect our collective future, we must better understand and prioritize the importance of being educable.
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Published April 16, 2024
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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