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A.I. and Genius Machines

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by Scientific American Editors
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A.I. and Genius Machines by Scientific American Editors

A.I. and Genius Machines by the editors of Scientific American

In science fiction, artificial intelligence takes the shape of computers that can speak like people, think for themselves, and sometimes act against us. Sometimes the machines seem to know everything, and symbolize implacable and unknowable power, as in The Matrix. Such machines can also embody the limits of logic, and by extension our own powers of reason. In Arthur C. Clarke's 2001: A Space Odyssey, HAL was a computer of vast capability driven insane by the demands of his programming – to honestly and completely report information – when those instructions conflicted with orders to keep state secrets. Star Trek has given us the android, Lieutenant Commander Data, who strives to be more human. None of these visions came true in quite the way science fiction writers imagined, even though in many ways computers surpass their fictional counterparts. This eBook reviews work in the field and covers topics from chess-playing to quantum computing. The writers tackle how to make computers more powerful, how we define consciousness, what the hard problems are and even how computers might be built once the limits of silicon chips have been reached. Artificial intelligence also raises some thorny ethical questions, such as whether morality can be programmed. These are kinds of issues that make artificial intelligence and computing fascinating. Building an intelligent machine brings together the human desire to create and the question of what makes us what we are. If anyone ever builds a true thinking machine, that last question becomes much more complicated, not less. Data and HAL would probably agree.

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Ebook Details
Pages: 50
Size: 3.3 MB
Publisher: Scientific American
Date published:   2013
ISBN: 9781466833814 (DRM-EPUB)

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This product is listed in the following categories:

Nonfiction > Computers > Artificial Intelligence
Nonfiction > Computers > Neural Networks

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03/26/2017
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