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Author: Hector J. Levesque
Publisher: MIT Press
ISBN: 0262036045
Category : Computers
Languages : en
Pages : 190
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Book Description
What 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: Hector J. Levesque
Publisher: MIT Press
ISBN: 0262036045
Category : Computers
Languages : en
Pages : 190
Get Book Here
Book Description
What 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: Hector J. Levesque
Publisher: MIT Press
ISBN: 0262535203
Category : Computers
Languages : en
Pages : 190
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Book Description
What 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: Ronald J. Brachman
Publisher: MIT Press
ISBN: 0262369222
Category : Computers
Languages : en
Pages : 320
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Book Description
How 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
ISBN: 0262300648
Category : Computers
Languages : en
Pages : 323
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Book Description
Students explore the idea that thinking is a form of computation by learning to write simple computer programs for tasks that require thought. This book guides students through an exploration of the idea that thinking might be understood as a form of computation. Students make the connection between thinking and computing by learning to write computer programs for a variety of tasks that require thought, including solving puzzles, understanding natural language, recognizing objects in visual scenes, planning courses of action, and playing strategic games. The material is presented with minimal technicalities and is accessible to undergraduate students with no specialized knowledge or technical background beyond high school mathematics. Students use Prolog (without having to learn algorithms: “Prolog without tears!”), learning to express what they need as a Prolog program and letting Prolog search for answers. After an introduction to the basic concepts, Thinking as Computation offers three chapters on Prolog, covering back-chaining, programs and queries, and how to write the sorts of Prolog programs used in the book. The book follows this with case studies of tasks that appear to require thought, then looks beyond Prolog to consider learning, explaining, and propositional reasoning. Most of the chapters conclude with short bibliographic notes and exercises. The book is based on a popular course at the University of Toronto and can be used in a variety of classroom contexts, by students ranging from first-year liberal arts undergraduates to more technically advanced computer science students.
Author: Sean Gerrish
Publisher: MIT Press
ISBN: 0262038404
Category : Computers
Languages : en
Pages : 313
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Book Description
Everything you've always wanted to know about self-driving cars, Netflix recommendations, IBM's Watson, and video game-playing computer programs. The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now. Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people.
Author: Hector J. Levesque
Publisher: MIT Press
ISBN: 9780262263498
Category : Computers
Languages : en
Pages : 316
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Book Description
This book describes in detail the relationship between symbolic representations of knowledge and abstract states of knowledge, exploring along the way the foundations of knowledge, knowledge bases, knowledge-based systems, and knowledge representation and reasoning. The idea of knowledge bases lies at the heart of symbolic, or "traditional," artificial intelligence. A knowledge-based system decides how to act by running formal reasoning procedures over a body of explicitly represented knowledge—a knowledge base. The system is not programmed for specific tasks; rather, it is told what it needs to know and expected to infer the rest. This book is about the logic of such knowledge bases. It describes in detail the relationship between symbolic representations of knowledge and abstract states of knowledge, exploring along the way the foundations of knowledge, knowledge bases, knowledge-based systems, and knowledge representation and reasoning. Assuming some familiarity with first-order predicate logic, the book offers a new mathematical model of knowledge that is general and expressive yet more workable in practice than previous models. The book presents a style of semantic argument and formal analysis that would be cumbersome or completely impractical with other approaches. It also shows how to treat a knowledge base as an abstract data type, completely specified in an abstract way by the knowledge-level operations defined over it.
Author: Robert Epstein
Publisher: Springer Science & Business Media
ISBN: 1402096240
Category : Computers
Languages : en
Pages : 520
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Book Description
An exhaustive work that represents a landmark exploration of both the philosophical and methodological issues surrounding the search for true artificial intelligence. Distinguished psychologists, computer scientists, philosophers, and programmers from around the world debate weighty issues such as whether a self-conscious computer would create an internet ‘world mind’. This hugely important volume explores nothing less than the future of the human race itself.
Author: Nicholas Agar
Publisher: MIT Press
ISBN: 0262349167
Category : Technology & Engineering
Languages : en
Pages : 231
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Book Description
An argument in favor of finding a place for humans (and humanness) in the future digital economy. In the digital economy, accountants, baristas, and cashiers can be automated out of employment; so can surgeons, airline pilots, and cab drivers. Machines will be able to do these jobs more efficiently, accurately, and inexpensively. But, Nicholas Agar warns in this provocative book, these developments could result in a radically disempowered humanity. The digital revolution has brought us new gadgets and new things to do with them. The digital revolution also brings the digital economy, with machines capable of doing humans' jobs. Agar explains that developments in artificial intelligence enable computers to take over not just routine tasks but also the kind of “mind work” that previously relied on human intellect, and that this threatens human agency. The solution, Agar argues, is a hybrid social-digital economy. The key value of the digital economy is efficiency. The key value of the social economy is humanness. A social economy would be centered on connections between human minds. We should reject some digital automation because machines will always be poor substitutes for humans in roles that involve direct contact with other humans. A machine can count out pills and pour out coffee, but we want our nurses and baristas to have minds like ours. In a hybrid social-digital economy, people do the jobs for which feelings matter and machines take on data-intensive work. But humans will have to insist on their relevance in a digital age.
Author: Herbert L. Roitblat
Publisher: MIT Press
ISBN: 0262044129
Category : Computers
Languages : en
Pages : 340
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Book Description
Why a new approach is needed in the quest for general artificial intelligence. Since the inception of artificial intelligence, we have been warned about the imminent arrival of computational systems that can replicate human thought processes. Before we know it, computers will become so intelligent that humans will be lucky to kept as pets. And yet, although artificial intelligence has become increasingly sophisticated—with such achievements as driverless cars and humanless chess-playing—computer science has not yet created general artificial intelligence. In Algorithms Are Not Enough, Herbert Roitblat explains how artificial general intelligence may be possible and why a robopocalypse is neither imminent, nor likely. Existing artificial intelligence, Roitblat shows, has been limited to solving path problems, in which the entire problem consists of navigating a path of choices—finding specific solutions to well-structured problems. Human problem-solving, on the other hand, includes problems that consist of ill-structured situations, including the design of problem-solving paths themselves. These are insight problems, and insight is an essential part of intelligence that has not been addressed by computer science. Roitblat draws on cognitive science, including psychology, philosophy, and history, to identify the essential features of intelligence needed to achieve general artificial intelligence. Roitblat describes current computational approaches to intelligence, including the Turing Test, machine learning, and neural networks. He identifies building blocks of natural intelligence, including perception, analogy, ambiguity, common sense, and creativity. General intelligence can create new representations to solve new problems, but current computational intelligence cannot. The human brain, like the computer, uses algorithms; but general intelligence, he argues, is more than algorithmic processes.
Author: Dean Buonomano
Publisher: W. W. Norton & Company
ISBN: 0393247953
Category : Science
Languages : en
Pages : 357
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Book Description
"Beautifully written, eloquently reasoned…Mr. Buonomano takes us off and running on an edifying scientific journey." —Carol Tavris, Wall Street Journal In Your Brain Is a Time Machine, leading neuroscientist Dean Buonomano embarks on an "immensely engaging" exploration of how time works inside the brain (Barbara Kiser, Nature). The human brain, he argues, is a complex system that not only tells time, but creates it; it constructs our sense of chronological movement and enables "mental time travel"—simulations of future and past events. These functions are essential not only to our daily lives but to the evolution of the human race: without the ability to anticipate the future, mankind would never have crafted tools or invented agriculture. This virtuosic work of popular science will lead you to a revelation as strange as it is true: your brain is, at its core, a time machine.
