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Myth of Artificial Intelligence: Why Computers Cant Think the Way We Do [Kõva köide]

3.95/5 (1460 hinnangut Goodreads-ist)
  • Formaat: Hardback, 320 pages, kõrgus x laius: 210x140 mm
  • Ilmumisaeg: 06-Apr-2021
  • Kirjastus: Harvard University Press
  • ISBN-10: 0674983513
  • ISBN-13: 9780674983519
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  • Formaat: Hardback, 320 pages, kõrgus x laius: 210x140 mm
  • Ilmumisaeg: 06-Apr-2021
  • Kirjastus: Harvard University Press
  • ISBN-10: 0674983513
  • ISBN-13: 9780674983519
Teised raamatud teemal:
"Futurists are certain that humanlike AI is on the horizon, but in fact engineers have no idea how to program human reasoning. AI reasons from statistical correlations across data sets, while common sense is based heavily on conjecture. Erik Larson argues that hyping existing methods will only hold us back from developing truly humanlike AI"--

A cutting-edge AI researcher and tech entrepreneur debunks the fantasy that superintelligence is just a few clicks away—and argues that this myth is not just wrong, it’s actively blocking innovation and distorting our ability to make the crucial next leap.

“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

A cutting-edge AI researcher and tech entrepreneur debunks the fantasy that superintelligence is just a few clicks away—and argues that this myth is not just wrong, it’s actively blocking innovation and distorting our ability to make the crucial next leap.

Futurists insist that AI will soon eclipse the capacities of the most gifted human mind. What hope do we have against superintelligent machines? But we aren’t really on the path to developing intelligent machines. In fact, we don’t even know where that path might be.

A tech entrepreneur and pioneering research scientist working at the forefront of natural language processing, Erik Larson takes us on a tour of the landscape of AI to show how far we are from superintelligence, and what it would take to get there. Ever since Alan Turing, AI enthusiasts have equated artificial intelligence with human intelligence. 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. Human intelligence is a web of best guesses, given what we know about the world. We haven’t a clue how to program this kind of intuitive reasoning, known as abduction. Yet it is the heart of common sense. That’s why Alexa can’t understand what you are asking, and why AI can only take us so far.

Larson argues that AI hype is both bad science and bad for science. A culture of invention thrives on exploring unknowns, not overselling existing methods. Inductive AI will continue to improve at narrow tasks, but if we want to make real progress, we will need to start by more fully appreciating the only true intelligence we know—our own.



Futurists are certain that humanlike AI is on the horizon, but in fact engineers have no idea how to program human reasoning. AI reasons from statistical correlations across data sets, while common sense is based heavily on conjecture. Erik Larson argues that hyping existing methods will only hold us back from developing truly humanlike AI.

Arvustused

If you want to know about AI, read this book. For several reasonsmost 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 Larson worries that were making two mistakes at once, defining human intelligence down while overestimating what AI is likely to achieveAnother 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 * Thoughtfulmakes a convincing case that artificial general intelligencemachine-based intelligence that matches our ownis beyond the capacity of algorithmic machine learning because there is a mismatch between how humans and machines know what they knowAI cant account for the qualitative, nonmeasurable, idiosyncratic, messy stuff of life. -- Sue Halpern * New York Review of Books * 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, author of The End of Science 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. -- Oren Etzioni, CEO of the Allen Institute for AI 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 persons 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, technology executive and former DARPA official There are several books out there addressing the trending topic of AI, but Larsons The Myth of Artificial Intelligence is arguably the best one of them so farShould be taught in every undergraduate level engineering program. -- Gábor István Bíró * Metascience * 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. -- Elizabeth Obee * Towards Data Science * Far and away the best refutation of Kurzweils overpromises, but also of the hype pressed by those who have fallen in love with AIs 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. -- William A. Dembski * Evolution News * Larsons 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. -- Brendan Patrick Purdy * Law & Liberty * 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 scienceComes at an opportune momentwhen 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. -- Viraj Kulkarni * The Wire (India) * A detailed, wide-ranging excavation of AIs history and culture, and the limitations of current machine learning, [ Larson] argues that theres basically no good scientific reason to believe the [ AI] mythA clever, engaging book that looks closely at the machines we fear could one day destroy us all, and at how our current tools wont create this future. -- Ellen Broad * Inside Story * Discusses how widely publicized misconceptions about intelligence and inference have led AI research down narrow paths that are limiting innovation and scientific discoveriesSheds light on the challenges that the field faces today and helps readers to see through the overblown claims about progress toward AGI or singularity. -- Ben Dickson * TechTalks * Lays out a birds eye view of the origins and ideas behind current AI methodsDisentangles 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 *

Introduction 1(6)
Part I THE SIMPLIFIED WORLD
7(80)
1 The Intelligence Error
9(10)
2 Turing at Bletchley
19(14)
3 The Superintelligence Error
33(11)
4 The Singularity, Then and Now
44(6)
5 Natural Language Understanding
50(10)
6 AI as Technological Kitsch
60(8)
7 Simplifications and Mysteries
68(19)
Part II THE PROBLEM OF INFERENCE
87(148)
8 Don't Calculate, Analyze
89(6)
9 The Puzzle of Peirce (and Peirce's Puzzle)
95(11)
10 Problems with Deduction and Induction
106(27)
11 Machine Learning and Dig uata
133(24)
12 Abductive Inference
157(34)
13 Inference and Language I
191(13)
14 Inference and Language II
204(31)
Part III THE FUTURE OF THE MYTH
235(48)
15 Myths and Heroes
237(8)
16 AI Mythology Invades Neuroscience
245(18)
17 Neocortical Theories of Human Intelligence
263(6)
18 The End of Science?
269(14)
Notes 283(18)
Acknowledgments 301(2)
Index 303
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.