The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do

by Erik J. Larson

Overview

“Exposes the vast gap between the actual science underlying AI and the dramatic claims being made for it.”
—John Horgan

“If you want to know about AI, read this book…It shows how a supposedly futuristic reverence for Artificial Intelligence retards progress when it denigrates our most irreplaceable resource for any future progress: our own human intelligence.”
—Peter Thiel

Ever since Alan Turing, AI enthusiasts have equated artificial intelligence with human intelligence. A computer scientist working at the forefront of natural language processing, Erik Larson takes us on a tour of the landscape of AI to reveal why this is a profound mistake.

AI works on inductive reasoning, crunching data sets to predict outcomes. But humans don’t correlate data sets. We make conjectures, informed by context and experience. And we haven’t a clue how to program that kind of intuitive reasoning, which lies at the heart of common sense. Futurists insist AI will soon eclipse the capacities of the most gifted mind, but Larson shows how far we are from superintelligence—and what it would take to get there.

“Larson worries that we’re making two mistakes at once, defining human intelligence down while overestimating what AI is likely to achieve…Another concern is learned passivity: our tendency to assume that AI will solve problems and our failure, as a result, to cultivate human ingenuity.”
—David A. Shaywitz, Wall Street Journal

“A convincing case that artificial general intelligence—machine-based intelligence that matches our own—is beyond the capacity of algorithmic machine learning because there is a mismatch between how humans and machines know what they know.”
—Sue Halpern, New York Review of Books

Product Details

ISBN-13:	9780674278660
Publisher:	Harvard University Press
Publication date:	10/11/2022
Pages:	320
Sales rank:	109,341
Product dimensions:	5.40(w) x 8.20(h) x 1.10(d)

About the Author

Erik J. Larson is a computer scientist and tech entrepreneur. The founder of two DARPA-funded AI startups, he is currently working on core issues in natural language processing and machine learning. He has written for The Atlantic and for professional journals and has tested the technical boundaries of artificial intelligence through his work with the IC2 tech incubator at the University of Texas at Austin.

Table of Contents

Introduction 1

Part I The Simplified World 7

1 The Intelligence Error 9

2 Turing at Bletchley 19

3 The Superintelligence Error 33

4 The Singularity, Then and Now 44

5 Natural Language Understanding 50

6 AI as Technological Kitsch 60

7 Simplifications and Mysteries 68

Part II The Problem of Inference 87

8 Don't Calculate, Analyze 89

9 The Puzzle of Peirce (and Peirce's Puzzle) 95

10 Problems with Deduction and Induction 106

11 Machine Learning and Big Data 133

12 Abductive Inference 157

13 Inference and Language I 191

14 Inference and Language II 204

Part III The Future of the Myth 235

15 Myths and Heroes 237

16 AI Mythology Invades Neuroscience 245

17 Neocortical Theories of Human Intelligence 263

18 The End of Science? 269

Notes 283

Acknowledgments 301

Index 303

Editorial Reviews

A discussion of general human intelligence versus the current state of artificial intelligence, and how progress in a narrowly defined, specialized area (how to play chess) does not necessarily mean we are getting closer to human-like thinking machines. So, take a rain-check on the impending arrival of the robot overlords, that is going to have to wait a while.

Towards Data Science - Elizabeth Obee

Discusses how widely publicized misconceptions about intelligence and inference have led AI research down narrow paths that are limiting innovation and scientific discoveries…Sheds light on the challenges that the field faces today and helps readers to see through the overblown claims about progress toward AGI or singularity.

TechTalks - Ben Dickson

There are several books out there addressing the trending topic of AI, but Larson’s The Myth of Artificial Intelligence is arguably the best one of them so far…Should be taught in every undergraduate level engineering program.

Metascience - Gábor István Bíró

A detailed, wide-ranging excavation of AI’s history and culture, and the limitations of current machine learning, [Larson] argues that there’s basically ‘no good scientific reason’ to believe the [AI] myth…A clever, engaging book that looks closely at the machines we fear could one day destroy us all, and at how our current tools won’t create this future.

Inside Story - Ellen Broad

Lays out a bird’s eye view of the origins and ideas behind current AI methods…Disentangles the hype of AI from what is actually possible with current technology. Even as he sheds light on the gap between the singularity prediction and what machine learning is truly capable of, he emphasizes the significance of the myth.

Perspectives on Science and Christian Faith

If you want to know about AI, read this book. For several reasons—most of all because it shows how a supposedly futuristic reverence for Artificial Intelligence retards progress when it denigrates our most irreplaceable resource for any future progress: our own human intelligence.

Peter Thiel

Thoughtful…makes a convincing case that artificial general intelligence—machine-based intelligence that matches our own—is beyond the capacity of algorithmic machine learning because there is a mismatch between how humans and machines know what they know…AI can’t account for the qualitative, nonmeasurable, idiosyncratic, messy stuff of life.

New York Review of Books - Sue Halpern

A fantastic tour of AI, at once deeply enlightening and eminently readable, that challenges the overwrought vision of a technology that revolutionizes everything and also threatens our existence. Larson, the thinking person’s tech entrepreneur, explores the philosophical and practical implications of AI as never before and reminds us that wishing for something is not the same as building it.

Todd C. Hughes

Larson’s book is excellent, and tells the story of how successful narrow AI has been in comparison to the failures of strong AI. It also shows us why we have no reason to believe that these failures will turn into successes anytime soon. The Myth of Artificial Intelligence also serves as a warning to be skeptical of the predictions of experts and expresses the importance of having a sound theory to properly practice science.

Law & Liberty - Brendan Patrick Purdy

Far and away the best refutation of Kurzweil’s overpromises, but also of the hype pressed by those who have fallen in love with AI’s latest incarnation, which is the combination of big data with machine learning. Just to be clear, Larson is not a contrarian. He does not have a death wish for AI. He is not trying to sabotage research in the area (if anything, he is trying to extricate AI research from the fantasy land it currently inhabits)…Insightful and timely.

Evolution News - William A. Dembski

Artificial intelligence has always inspired outlandish visions, but now Elon Musk and other authorities assure us that those sci-fi visions are about to become reality. Artificial intelligence is going to destroy us, save us, or at the very least radically transform us. In The Myth of Artificial Intelligence, Erik Larson exposes the vast gap between the actual science underlying AI and the dramatic claims being made for it. This is a timely, important, and even essential book.

John Horgan

Believing in the myth of AI has more serious consequences for our society beyond merely losing sleep over the prospects of a robot uprising. The myth, Larson argues, is negatively affecting research in many fields of science…Comes at an opportune moment—when AI has breached the peak of expectations and is now inching downwards, into the trough of disillusionment. It deflates the hype surrounding the subject and offers coherent arguments against the inevitability and imminence of true machine intelligence.

The Wire (India) - Viraj Kulkarni

Larson worries that we’re making two mistakes at once, defining human intelligence down while overestimating what AI is likely to achieve…Another concern is learned passivity: our tendency to assume that AI will solve problems and our failure, as a result, to cultivate human ingenuity.

Wall Street Journal

Erik Larson offers an expansive look at the field of AI, from its early history to recent prophecies about the advent of superintelligent machines. Engaging, clear, and highly informed, The Myth of Artificial Intelligence is a terrific book.

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