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Computer Science and Intelligent Systems

Computer Science and Intelligent Systems

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Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them.

This mathematically oriented introduction to the theory of logic programming presents a systematic exposition of the resolution method for propositional, first-order, and Horn- clause logics, together with an analysis of the semantic aspects of the method. It is through the inference rule of resolution that both proofs and computations can be manipulated on computers, and this book contains elegant versions and proofs of the fundamental theorems and lemmas in the proof theory of logic programming.

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.

People keep track. In the eighteenth century, Benjamin Franklin kept charts of time spent and virtues lived up to. Today, people use technology to self-track: hours slept, steps taken, calories consumed, medications administered. Ninety million wearable sensors were shipped in 2014 to help us gather data about our lives. This book examines how people record, analyze, and reflect on this data, looking at the tools they use and the communities they become part of.

Making and Remaking the Modern Computer

Conceived in 1943, completed in 1945, and decommissioned in 1955, ENIAC (the Electronic Numerical Integrator and Computer) was the first general-purpose programmable electronic computer. But ENIAC was more than just a milestone on the road to the modern computer.

Programming by Demonstration
Edited by Allen Cypher

Until recently most programming power has been in the hands of the professional programmer rather than the end user. Programming by Demonstration is a method that allows end users to create, customize, and extend programs by demonstrating what the program should do. Programming by Demonstration systems have existed since 1975, yet this is the first time that information on all of the best of these systems has been gathered in one place.

Foundations of Neural Computation

Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computationcollects, by topic, the most significant papers that have appeared in the journal over the past nine years.This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs.

Habitual New Media

New media—we are told—exist at the bleeding edge of obsolescence. We thus forever try to catch up, updating to remain the same. Meanwhile, analytic, creative, and commercial efforts focus exclusively on the next big thing: figuring out what will spread and who will spread it the fastest. But what do we miss in this constant push to the future? In Updating to Remain the Same, Wendy Hui Kyong Chun suggests another approach, arguing that our media matter most when they seem not to matter at all—when they have moved from “new” to habitual.

Principles and Best Practice

This book addresses an often-neglected aspect of the creation of VHDL designs. A VHDL description is also source code, and VHDL designers can use the best practices of software development to write high-quality code and to organize it in a design. This book presents this unique set of skills, teaching VHDL designers of all experience levels how to apply the best design principles and coding practices from the software world to the world of hardware.

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.

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