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Author: Hector J. Levesque
Publisher: MIT Press
Published: 2017
Total Pages: 190
ISBN-13: 0262036045
DOWNLOAD EBOOKWhat kind of AI? -- The big puzzle -- Knowledge and behavior -- Making it and faking it -- Learning with and without experience -- Book smarts and street smarts -- The long tail and the limits to training -- Symbols and symbol processing -- Knowledge-based systems -- AI technology
Author: Ronald J. Brachman
Publisher: MIT Press
Published: 2022-05-17
Total Pages: 320
ISBN-13: 0262369222
DOWNLOAD EBOOKHow we can create artificial intelligence with broad, robust common sense rather than narrow, specialized expertise. It’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 Machines like Us, 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. Using 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.
Author: Ronald J. Brachman
Publisher: MIT Press
Published: 2023-10-17
Total Pages: 319
ISBN-13: 0262547325
DOWNLOAD EBOOKHow we can create artificial intelligence with broad, robust common sense rather than narrow, specialized expertise. It’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 Machines like Us, 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. Using 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.
Author: Hector J. Levesque
Publisher: MIT Press
Published: 2018-03-09
Total Pages: 190
ISBN-13: 0262535203
DOWNLOAD EBOOKWhat artificial intelligence can tell us about the mind and intelligent behavior. What can artificial intelligence teach us about the mind? If AI's underlying concept is that thinking is a computational process, then how can computation illuminate thinking? It's a timely question. AI is all the rage, and the buzziest AI buzz surrounds adaptive machine learning: computer systems that learn intelligent behavior from massive amounts of data. This is what powers a driverless car, for example. In this book, Hector Levesque shifts the conversation to “good old fashioned artificial intelligence,” which is based not on heaps of data but on understanding commonsense intelligence. This kind of artificial intelligence is equipped to handle situations that depart from previous patterns—as we do in real life, when, for example, we encounter a washed-out bridge or when the barista informs us there's no more soy milk. Levesque considers the role of language in learning. He argues that a computer program that passes the famous Turing Test could be a mindless zombie, and he proposes another way to test for intelligence—the Winograd Schema Test, developed by Levesque and his colleagues. “If our goal is to understand intelligent behavior, we had better understand the difference between making it and faking it,” he observes. He identifies a possible mechanism behind common sense and the capacity to call on background knowledge: the ability to represent objects of thought symbolically. As AI migrates more and more into everyday life, we should worry if systems without common sense are making decisions where common sense is needed.
Author: John McCarthy
Publisher: Intellect L & D E F A E
Published: 1998
Total Pages: 256
ISBN-13: 9781871516494
DOWNLOAD EBOOKExtending over a period of 30 years, this is a collection of papers written by John McCarthy on artificial intelligence. They range from informal surveys written for a general audience to technical discussions of challenging research problems that should be of interest to specialists.
Author: Martin W. Bauer
Publisher: Taylor & Francis
Published: 2024-06-28
Total Pages: 286
ISBN-13: 1040086527
DOWNLOAD EBOOKCommon sense is the endless frontier in the development of artificial intelligence, but what exactly is common sense, can we replicate it in algorithmic form, and if we can – should we? Bauer, Schiele and their contributors from a range of disciplines analyse the nature of common sense, and the consequent challenges of incorporating into artificial intelligence models. They look at different ways we might understand common sense and which of these ways are simulated within computer algorithms. These include sensory integration, self-evident truths, rhetorical common places, and mutuality and intentionality of actors within a moral community. How far are these possible features within and of machines? Approaching from a range of perspectives including Sociology, Political Science, Media and Culture, Psychology and Computer Science, the contributors lay out key questions, practical challenges and "common sense" concerns underlying the incorporation of common sense within machine learning algorithms for simulating intelligence, socialising robots, self-driving vehicles, personnel selection, reading, automatic text analysis, and text production. A valuable resource for students and scholars of Science–Technology–Society Studies, Sociologists, Psychologists, Media and Culture Studies, human–computer interaction with an interest in the post-human, and programmers tackling the contextual questions of machine learning.
Author: Vikram Mansharamani
Publisher: Harvard Business Press
Published: 2020-06-16
Total Pages: 176
ISBN-13: 1633699226
DOWNLOAD EBOOKWe've outsourced too much of our thinking. How do we get it back? Have you ever followed your GPS device to a deserted parking lot? Or unquestioningly followed the advice of an expert—perhaps a doctor or financial adviser—only to learn later that your own thoughts and doubts were correct? And what about the stories we've all heard over the years about sick patients—whether infected with Ebola or COVID-19—who were sent home or allowed to travel because busy staff people were following a protocol to the letter rather than using common sense? Why and how do these kinds of things happen? As Harvard lecturer and global trend watcher Vikram Mansharamani shows in this eye-opening and perspective-shifting book, our complex, data-flooded world has made us ever more reliant on experts, protocols, and technology. Too often, we've stopped thinking for ourselves. With stark and compelling examples drawn from business, sports, and everyday life, Mansharamani illustrates how in a very real sense we have outsourced our thinking to a troubling degree, relinquishing our autonomy. Of course, experts, protocols, and computer-based systems are essential to helping us make informed decisions. What we need is a new approach for integrating these information sources more effectively, harnessing the value they provide without undermining our ability to think for ourselves. The author provides principles and techniques for doing just that, empowering readers with a more critical and nuanced approach to making decisions. Think for Yourself is an indispensable guide for those looking to restore self-reliant thinking in a data-driven and technology-dependent yet overwhelmingly uncertain world.
Author: Melanie Mitchell
Publisher: Farrar, Straus and Giroux
Published: 2019-10-15
Total Pages: 336
ISBN-13: 0374715238
DOWNLOAD EBOOKMelanie Mitchell separates science fact from science fiction in this sweeping examination of the current state of AI and how it is remaking our world 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.
Author: Erik T. Mueller
Publisher: Elsevier
Published: 2010-07-26
Total Pages: 431
ISBN-13: 0080476619
DOWNLOAD EBOOKTo endow computers with common sense is one of the major long-term goals of Artificial Intelligence research. One approach to this problem is to formalize commonsense reasoning using mathematical logic. Commonsense Reasoning is a detailed, high-level reference on logic-based commonsense reasoning. It uses the event calculus, a highly powerful and usable tool for commonsense reasoning, which Erik T. Mueller demonstrates as the most effective tool for the broadest range of applications. He provides an up-to-date work promoting the use of the event calculus for commonsense reasoning, and bringing into one place information scattered across many books and papers. Mueller shares the knowledge gained in using the event calculus and extends the literature with detailed event calculus solutions to problems that span many areas of the commonsense world. Covers key areas of commonsense reasoning including action, change, defaults, space, and mental states. The first full book on commonsense reasoning to use the event calculus. Contextualizes the event calculus within the framework of commonsense reasoning, introducing the event calculus as the best method overall. Focuses on how to use the event calculus formalism to perform commonsense reasoning, while existing papers and books examine the formalisms themselves. Includes fully worked out proofs and circumscriptions for every example.
Author: Richmond H. Thomason
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 230
ISBN-13: 9400924488
DOWNLOAD EBOOKcians concerned with using logical tools in philosophy have been keenly aware of the limitations that arise from the original con centration of symbolic logic on the idiom of mathematics, and many of them have worked to create extensions of the received logical theories that would make them more generally applicable in philosophy. Carnap's Testability and Meaning, published in 1936 and 1937, was a good early example of this sort of research, motivated by the inadequacy of first-order formalizations of dis 'This sugar cube is soluble in water'. positional sentences like And in fact there is a continuous history of work on this topic, extending from Carnap's paper to Shoham's contribution to the present volume . . Much of the work in philosophical logic, and much of what has appeared in The Journal of Philosophical Logic, was mo tivated by similar considerations: work in modal logic (includ ing tense, deontic, and epistemic logic), intensional logics, non declaratives, presuppositions, and many other topics. In this sort of research, sin.ce the main point is to devise new formalisms, the technical development tends to be rather shallow in comparison with mathematical logic, though it is sel dom absent: theorems need to be proved in order to justify the formalisms, and sometimes these are nontrivial. On the other hand, much effort has to go into motivating a logical innovation.
