Complexity A Guided Tour Melanie Mitchell
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Complexity
Author: Melanie Mitchell
language: en
Publisher: Oxford University Press
Release Date: 2009-04-01
What enables individually simple insects like ants to act with such precision and purpose as a group? How do trillions of neurons produce something as extraordinarily complex as consciousness? In this remarkably clear and companionable book, leading complex systems scientist Melanie Mitchell provides an intimate tour of the sciences of complexity, a broad set of efforts that seek to explain how large-scale complex, organized, and adaptive behavior can emerge from simple interactions among myriad individuals. Based on her work at the Santa Fe Institute and drawing on its interdisciplinary strategies, Mitchell brings clarity to the workings of complexity across a broad range of biological, technological, and social phenomena, seeking out the general principles or laws that apply to all of them. Richly illustrated, Complexity: A Guided Tour--winner of the 2010 Phi Beta Kappa Book Award in Science--offers a wide-ranging overview of the ideas underlying complex systems science, the current research at the forefront of this field, and the prospects for its contribution to solving some of the most important scientific questions of our time.
Artificial Intelligence
'After reading Mitchell’s guide, you’ll know what you don’t know and what other people don’t know, even though they claim to know it. And that’s invaluable'' The New York Times A leading computer scientist brings human sense to the AI bubble No recent scientific enterprise has been so alluring, terrifying and filled with extravagant promise and frustrating setbacks as artificial intelligence. Writing with clarity and passion, leading AI researcher Melanie Mitchell offers a captivating account of modern-day artificial intelligence. Flavoured with personal stories and a twist of humour, Artificial Intelligence illuminates the workings of machines that mimic human learning, perception, language, creativity and common sense. Weaving together advances in AI with cognitive science and philosophy, Mitchell probes the extent to which today's 'smart' machines can actually think or understand, and whether AI even requires such elusive human qualities at all. Artificial Intelligence: A Guide for Thinking Humans provides readers with an accessible and clear-eyed view of the AI landscape, what the field has actually accomplished, how much further it has to go and what it means for all of our futures.
An Introduction to Genetic Algorithms
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics—particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.