Books  |    |  October 15, 2019

Artificial Intelligence: A Guide for Thinking Humans

Book by Melanie Mitchell.
Published by Farrar, Straus and Giroux.
336 pages.

No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it.

In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent―really―are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go.

Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.

Table of Contents

  • Prologue: Terrified
  • Background
    • The Roots of Artificial Intelligence
    • Neural Networks and the Ascent of Machine Learning
    • AI Spring
  • Looking and Seeing
    • Who, What, When, Where, Why
    • ConvNets and ImageNet
    • A Closer Look at Machines That Learn
    • On Trustworthy and Ethical AI
  • Learning to Play
    • Rewards for Robots
    • Game On
    • Beyond Games
  • Artificial Intelligence Meets Natural Language
    • Words, and the Company They Keep
    • Translation as Encoding and Decoding
    • Ask Me Anything
  • The Barrier of Meaning
    • On Understanding
    • Knowledge, Abstraction, and Analogy in Artificial Intelligence
    • Questions, Answers, and Speculations

About the Author

Melanie Mitchell has a PhD in computer science from the University of Michigan, where she studied with the cognitive scientist and writer Douglas Hofstadter; together, they created the Copycat program, which makes creative analogies in an idealized world. The author and editor of six books and numerous scholarly papers, she is currently a professor of computer science at Portland State University and an external professor at the Santa Fe Institute.