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We are at a crossroads in history. 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. Yet it comes hand in hand with an insidious weakness. While we can readily absorb entire systems of thought about worlds of experience beyond our own, we struggle to judge correctly what information we should trust.
The book 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, it 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 play to our species’ great strength and protect our collective future, we must better understand and prioritize the vital importance of being educable. The book provides a road map and can be found here https://press.princeton.edu/books/hardcover/9780691230566/the-importance-of-being-educable
Prof. Leslie Gabriel Valiant is the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University.
Prof. Valiant is a Turing Award Laureate, awarded in 2010, having been described by the ACM as a heroic figure in theoretical computer science and a role model for his courage and creativity in addressing some of the deepest unsolved problems in science; in particular for his "striking combination of depth and breadth". The citation for his Turing Award is as follows: "For transformative contributions to the theory of computation, including the theory of probably approximately correct (PAC) learning, the complexity of enumeration and of algebraic computation, and the theory of parallel and distributed computing."
Prof. Valiant received the Nevanlinna Prize in 1986, the Knuth Prize in 1997, the EATCS Award in 2008. He was elected a Fellow of the Royal Society (FRS) in 1991, a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 1992, and a member of the United States National Academy of Sciences in 2001.
Among the many contributions of Prof. Valiant to Complexity Theory, he introduced the notion of #P-completeness ("Sharp-P completeness") to explain why enumeration and reliability problems are intractable. He created the Probably Approximately Correct or PAC model of learning that introduced the field of Computational Learning Theory and became a theoretical basis for the development of Machine Learning.
He also introduced the concept of Holographic Algorithms, inspired by the Quantum Computation model. In computer systems, his most well-known research involves the introduction of the Bulk Synchronous Parallel (BSP) processing model. Analogous to the von Neumann model for a single computer architecture, BSP has been an influential model for parallel and distributed computing architectures. Recent examples are Google adopting it for computation at large scale via MapReduce, MillWheel, Pregel and Dataflow, and Facebook (Meta) creating a graph analytics system capable of processing over 1 trillion edges. There have also been active open-source projects to add explicit BSP programming as well as other high-performance parallel programming models derived from BSP. Popular examples are Hadoop, Spark, Giraph, Hama, Beam and Dask. His earlier work in Automata Theory includes an extension of the CYK algorithm for context-free parsing, which is still the asymptotically fastest known. He also works in Computational Neuroscience, focusing on understanding memory and learning.
Prof. Valiant has previously written books like Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World and Circuits of the Mind.
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