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Computer Science and Intelligent Systems

Computer Science and Intelligent Systems

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A Computer-Based Approach

Proof is the primary vehicle for knowledge generation in mathematics.
In computer science, proof has found an additional use: verifying that
a particular system (or component, or algorithm) has certain desirable
properties. This book teaches students how to read and write proofs
using Athena, a freely downloadable computer language. Athena proofs
are machine-checkable and written in an intuitive natural-deduction style.
The book contains more than 300 exercises, most with full solutions.

An Essay on the Materialities of Information

Virtual entities that populate our digital experience, like e-books, virtual worlds, and online stores, are backed by the large-scale physical infrastructures of server farms, fiber optic cables, power plants, and microwave links. But another domain of material constraints also shapes digital living: the digital representations sketched on whiteboards, encoded into software, stored in databases, loaded into computer memory, and transmitted on networks. These digital representations encode aspects of our everyday world and make them available for digital processing.

The Birth of Computer Science

In 1936, when he was just twenty-four years old, Alan Turing wrote a remarkable paper in which he outlined the theory of computation, laying out the ideas that underlie all modern computers. This groundbreaking and powerful theory now forms the basis of computer science. In Turing’s Vision, Chris Bernhardt explains the theory, Turing’s most important contribution, for the general reader. Bernhardt argues that the strength of Turing’s theory is its simplicity, and that, explained in a straightforward manner, it is eminently understandable by the nonspecialist.

In the 1950s, the United States and the Soviet Union raced to develop space-based intelligence gathering capability. The Soviets succeeded first, with SPUTNIK I in 1957. The United States began to monitor the growing Soviet space presence by developing technology for the detection and tracking of man-made resident space objects (RSOs) in near-Earth orbit. In 1972, the Soviet Union launched a satellite into deep space orbit, and the U.S. government called on MIT Lincoln Laboratory to develop deep space surveillance technology.

In order to deal with uncertainty intelligently, we need to be able to represent it and reason about it. In this book, Joseph Halpern examines formal ways of representing uncertainty and considers various logics for reasoning about it. While the ideas presented are formalized in terms of definitions and theorems, the emphasis is on the philosophy of representing and reasoning about uncertainty.

A Beginner's Guide

Algorithms are what we do in order not to have to do something. Algorithms consist of instructions to carry out tasks—usually dull, repetitive ones. Starting from simple building blocks, computer algorithms enable machines to recognize and produce speech, translate texts, categorize and summarize documents, describe images, and predict the weather. A task that would take hours can be completed in virtually no time by using a few lines of code in a modern scripting program. This book offers an introduction to algorithms through the real-world problems they solve.

Imagination in the Age of Computing

We depend on—we believe in—algorithms to help us get a ride, choose which book to buy, execute a mathematical proof. It’s as if we think of code as a magic spell, an incantation to reveal what we need to know and even what we want. Humans have always believed that certain invocations—the marriage vow, the shaman’s curse—do not merely describe the world but make it. Computation casts a cultural shadow that is shaped by this long tradition of magical thinking.

What can artificial intelligence teach us about the mind? If AI’s underlying concept is that thinking is a computational process, then how can computation illuminate thinking? It’s a timely question. AI is all the rage, and the buzziest AI buzz surrounds adaptive machine learning: computer systems that learn intelligent behavior from massive amounts of data. This is what powers a driverless car, for example.

Technology for Wellbeing 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.

From Biological Inspiration to Implementation and Control

Autonomous robots are intelligent machines capable of performing tasks in the world by themselves, without explicit human control. Examples range from autonomous helicopters to Roomba, the robot vacuum cleaner. In this book, George Bekey offers an introduction to the science and practice of autonomous robots that can be used both in the classroom and as a reference for industry professionals.

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