Language : English
Published : 2018-02-09
Pages : 192
Common Sense, the Turing Test, and the Quest for Real AI
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.
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The best business guide for design professionals just got even better! This revised and expanded third edition includes everything designers need–besides talent–to turn their artistic success into business success. You’ll find information on key issues facing designers from freelancing to managing established design firms. A strong visual focus and to-the-point text take the fear factor out of learning about thorny business realities like staffing, marketing, bookkeeping, intellectual property, and more. These smart business practices are essential to success in graphic, Web, and industrial design. Here are just a few of the things you’ll learn:
• How to get on the right career path
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Talent Is Not Enough provides a big-picture context for these and other challenges and shares practical, real-world advice. Since its first publication, the book has become an essential resource for both students and working professionals in these areas and more:
• Design planning and strategy
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Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, substantial additions to the chapter on recurrence (now called “Divide-and-Conquer”), and an appendix on matrices. It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many new exercises and problems have been added for this edition. As of the third edition, this textbook is published exclusively by the MIT Press. The hardcover edition does not include a dust jacket.
About the Author
Thomas H. Cormen is Professor of Computer Science and former Director of the Institute for Writing and Rhetoric at Dartmouth College. He is the coauthor (with Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein) of the leading textbook on computer algorithms, Introduction to Algorithms (third edition, MIT Press, 2009). Charles E. Leiserson is Professor of Computer Science and Engineering at the Massachusetts Institute of Technology. Ronald L. Rivest is Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. Clifford Stein is Professor of Industrial Engineering and Operations Research at Columbia University.
Based on the Association for Computing Imagery model curriculum guidelines, Foundations of Computer Science gives students a bird’s eye view of Computer Science. This easy-to-read and easy-to-navigate text covers all the fundamentals of computer science required for first year undergraduates embarking on a computing degree.
For undergraduate or graduate courses in IT Strategy or Management. Using IT to deliver business value. IT Strategy: Issues and Practices provides a critical issues perspective that shows students how to use and manage IT to deliver business value. This edition has been overhauled in order to reflect the most important issues facing IT managers today.