{"product_id":"machines-like-us-isbn-9780262046794","title":"Machines like Us","description":"\u003cb\u003eHow we can create artificial intelligence with broad, robust common sense rather than narrow, specialized expertise.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eIt’s sometime in the not-so-distant future, and you send your fully autonomous self-driving car to the store to pick up your grocery order. The car is endowed with as much capability as an artificial intelligence agent can have, programmed to drive better than you do. But when the car encounters a traffic light stuck on red, it just sits there—indefinitely. Its obstacle-avoidance, lane-following, and route-calculation capacities are all irrelevant; it fails to act because it lacks the common sense of a human driver, who would quickly figure out what’s happening and find a workaround. In \u003ci\u003eMachines like Us\u003c\/i\u003e, Ron Brachman and Hector Levesque—both leading experts in AI—consider what it would take to create machines with common sense rather than just the specialized expertise of today’s AI systems. \u003cbr\u003e \u003cbr\u003eUsing the stuck traffic light and other relatable examples, Brachman and Levesque offer an accessible account of how common sense might be built into a machine. They analyze common sense in humans, explain how AI over the years has focused mainly on expertise, and suggest ways to endow an AI system with both common sense and effective reasoning. Finally, they consider the critical issue of how we can trust an autonomous machine to make decisions, identifying two fundamental requirements for trustworthy autonomous AI systems: having reasons for doing what they do, and being able to accept advice. Both in the end are dependent on having common sense.Preview ix\u003cbr\u003e1 The Road to Common Sense 1\u003cbr\u003e2 Common Sense in Humans 9\u003cbr\u003e3 Expertise in AI Systems 25\u003cbr\u003e4 Knowledge and Its Representation 51\u003cbr\u003e5 A Commonsense Understanding of the World 67\u003cbr\u003e6 Commonsense Knowledge 83\u003cbr\u003e7 Representation and Reasoning, Part I 113\u003cbr\u003e8 Representation and Reasoning, Part II 137\u003cbr\u003e9 Common Sense in Action 159\u003cbr\u003e10 Steps toward Implementation 183\u003cbr\u003e11 Building Trust 197\u003cbr\u003eEpilogue 215\u003cbr\u003eAppendix 219\u003cbr\u003eBonus Chapter: The Logic of Common Sense 227\u003cbr\u003eAcknowledgments 247\u003cbr\u003eNotes 249\u003cbr\u003eBibliography 279\u003cbr\u003eIndex 297\u003cbr\u003eAbout the Authors 305\u003ci\u003e\"Machines Like Us,\u003c\/i\u003e for those who want to better understand a key part of intelligence we still haven’t been able to clarify and build for artificial intelligence, is an excellent overview.”\u003cbr\u003e\u003cb\u003e\u003cb\u003e\u003ci\u003e—Forbes\u003c\/i\u003e\u003c\/b\u003e\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\"\u003ci\u003eMachines Like Us\u003c\/i\u003e provides a fresh perspective on potential areas of research, courtesy of two scientists who have been deeply involved in artificial intelligence since the 1970s.\"\u003cbr\u003e\u003cb\u003e\u003ci\u003e—\u003c\/i\u003eBen Dickson, TechTalks\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e“In \u003ci\u003eMachines like Us\u003c\/i\u003e, Brachman and Levesque imagine a world where proactive explainability and trust are at the forefront of any AI system, arguing that programming commonsense knowledge and reasoning into AI is imperative. Without the ability to react to the unexpected, AI systems can not only prove futile in critical moments but also present major risks to human safety.”\u003cbr\u003e\u003cb\u003e—\u003ci\u003eTechTarget\u003c\/i\u003e\u003c\/b\u003e“\u003ci\u003eMachines like Us\u003c\/i\u003e explains, lucidly and insightfully, why current AI systems hopelessly lack common sense, why they desperately need it, and how they can get it.”\u003cbr\u003e \u003cb\u003e—Ernest Davis, Professor of Computer Science, New York University\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e“Brachman and Levesque explain in accessible and entertaining detail what common sense entails and what approaches might work for giving it to machines. Essential reading for anyone interested in AI or in intelligence in general.”\u003cbr\u003e \u003cb\u003e—Melanie Mitchell, Davis Professor of Complexity, Santa Fe Institute; author of \u003ci\u003eArtificial Intelligence: A Guide for Thinking Humans\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003eRonald J. Brachman\u003c\/b\u003e is Director of the Jacobs Technion-Cornell Institute at Cornell Tech in New York City and Professor of Computer Science at Cornell University. During a long career in industry, he held leadership positions at Bell Labs, Yahoo, and DARPA. \u003cb\u003eHector J. Levesque\u003c\/b\u003e is Professor Emeritus in the Department of Computer Science at the University of Toronto. He is the author of \u003ci\u003eCommon Sense, the Turing Test, and the Quest for Real AI\u003c\/i\u003e (MIT Press), and other books.\u003cbr\u003e ","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":48233360097509,"sku":"NP9780262046794","price":29.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262046794_e4e3df95-a2c0-4ee2-a818-087984636792.jpg?v=1767732015","url":"https:\/\/k12savings.com\/es\/products\/machines-like-us-isbn-9780262046794","provider":"K12savings","version":"1.0","type":"link"}