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Computer Science and Intelligent Systems

Computer Science and Intelligent Systems

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The dominant feature of modern technology is not how productive it makes us, or how it has revolutionized the workplace, but how enjoyable it is. We take pleasure in our devices, from smartphones to personal computers to televisions. Whole classes of leisure activities rely on technology. How has technology become such an integral part of enjoyment?

The Politics and Aesthetics of Participation in Experience-Centered Design

In Taking A[part], John McCarthy and Peter Wright consider a series of boundary-pushing research projects in human-computer interaction (HCI) in which the design of digital technology is used to inquire into participative experience. McCarthy and Wright view all of these projects—which range from the public and performative to the private and interpersonal—through the critical lens of participation.

Computing is usually viewed as a technology field that advances at the breakneck speed of Moore’s Law. If we turn away even for a moment, we might miss a game-changing technological breakthrough or an earthshaking theoretical development. This book takes a different perspective, presenting computing as a science governed by fundamental principles that span all technologies. Computer science is a science of information processes. We need a new language to describe the science, and in this book Peter Denning and Craig Martell offer the great principles framework as just such a language.

A Primer

This introduction to quantum algorithms is concise but comprehensive, covering many key algorithms. It is mathematically rigorous but requires minimal background and assumes no knowledge of quantum theory or quantum mechanics. The book explains quantum computation in terms of elementary linear algebra; it assumes the reader will have some familiarity with vectors, matrices, and their basic properties, but offers a review of all the relevant material from linear algebra.

A Primer

This book offers a concise and accessible introduction to the emerging field of artificial cognitive systems. Cognition, both natural and artificial, is about anticipating the need for action and developing the capacity to predict the outcome of those actions. Drawing on artificial intelligence, developmental psychology, and cognitive neuroscience, the field of artificial cognitive systems has as its ultimate goal the creation of computer-based systems that can interact with humans and serve society in a variety of ways.

Portable Parallel Programming with the Message-Passing Interface

This book offers a thoroughly updated guide to the MPI (Message-Passing Interface) standard library for writing programs for parallel computers. Since the publication of the previous edition of Using MPI, parallel computing has become mainstream. Today, applications run on computers with millions of processors; multiple processors sharing memory and multicore processors with multiple hardware threads per core are common.

Technology for Well-Being and Human Potential

On the eve of Google’s IPO in 2004, Larry Page and Sergey Brin vowed not to be evil. Today, a growing number of technologists would go further, trying to ensure that their work actively improves people’s lives. Technology, so pervasive and ubiquitous, has the capacity to increase stress and suffering; but it also has the less-heralded potential to improve the well-being of individuals, society, and the planet.

Mastering Complexity

In this book, Sanjoy Mahajan shows us that the way to master complexity is through insight rather than precision. Precision can overwhelm us with information, whereas insight connects seemingly disparate pieces of information into a simple picture. Unlike computers, humans depend on insight. Based on the author’s fifteen years of teaching at MIT, Cambridge University, and Olin College, The Art of Insight in Science and Engineering shows us how to build insight and find understanding, giving readers tools to help them solve any problem in science and engineering.

Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets. This collection describes key approaches in sparse modeling, focusing on its applications in fields including neuroscience, computational biology, and computer vision.

Category theory was invented in the 1940s to unify and synthesize different areas in mathematics, and it has proven remarkably successful in enabling powerful communication between disparate fields and subfields within mathematics. This book shows that category theory can be useful outside of mathematics as a rigorous, flexible, and coherent modeling language throughout the sciences.

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