Half a century of research has resulted in machines capable of beating the best human chess players and humanoid robots that can interact. But can machines really think? Is the mind just a complicated computer program? "Introducing Artificial Intelligence" focuses on the issues behind one of science's most difficult problems.
Can computers think? Can they use reason to develop their own concepts, solve complex problems, understand our languages? This updated edition of a comprehensive survey includes extensive new text on "Artificial Intelligence in the 21st Century," introducing deep neural networks, conceptual graphs, languages of thought, mental models, metacognition, economic prospects, and research toward human-level AI. Ideal for both lay readers and students of computer science, the original text features abundant illustrations, diagrams, and photographs as well as challenging exercises. Lucid, easy-to-read discussions examine problem-solving methods and representations, game playing, automated understanding of natural languages, heuristic search theory, robot systems, heuristic scene analysis, predicate-calculus theorem proving, automatic programming, and many other topics.
Could a computer have a mind? What kind of machine would this be? Exactly what do we mean by 'mind' anyway?The notion of the 'intelligent' machine, whilst continuing to feature in numerous entertaining and frightening fictions, has also been the focus of a serious and dedicated research tradition. Reflecting on these fictions, and on the research tradition that pursues 'Artificial Intelligence', raises a number of vexing philosophical issues. Minds and Computers introduces readers to these issues by offering an engaging, coherent, and highly approachable interdisciplinary introduction to the Philosophy of Artificial Intelligence.Readers are presented with introductory material from each of the disciplines which constitute Cognitive Science: Philosophy, Neuroscience, Psychology, Computer Science, and Linguistics. Throughout, readers are encouraged to consider the implications of this disparate and wide-ranging material for the possibility of developing machines with minds. And they can expect to de
Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
Step into the future with AI The term "Artificial Intelligence" has been around since the 1950s, but a lot has changed since then. Today, AI is referenced in the news, books, movies, and TV shows, and the exact definition is often misinterpreted. Artificial Intelligence For Dummies provides a clear introduction to AI and how it’s being used today. Inside, you’ll get a clear overview of the technology, the common misconceptions surrounding it, and a fascinating look at its applications in everything from self-driving cars and drones to its contributions in the medical field. Learn about what AI has contributed to society Explore uses for AI in computer applications Discover the limits of what AI can do Find out about the history of AI The world of AI is fascinating—and this hands-on guide makes it more accessible than ever!
Advances in artificial intelligence (AI) highlight the potential of this technology to affect productivity, growth, inequality, market power, innovation, and employment. This volume seeks to set the agenda for economic research on the impact of AI. It covers four broad themes: AI as a general purpose technology; the relationships between AI, growth, jobs, and inequality; regulatory responses to changes brought on by AI; and the effects of AI on the way economic research is conducted. It explores the economic influence of machine learning, the branch of computational statistics that has driven much of the recent excitement around AI, as well as the economic impact of robotics and automation and the potential economic consequences of a still-hypothetical artificial general intelligence. The volume provides frameworks for understanding the economic impact of AI and identifies a number of open research questions. Contributors: Daron Acemoglu, Massachusetts Institute of Technology Philippe Aghion, Collège de France Ajay Agrawal, University of Toronto Susan Athey, Stanford University James Bessen, Boston University School of Law Erik Brynjolfsson, MIT Sloan School of Management Colin F. Camerer, California Institute of Technology Judith Chevalier, Yale School of Management Iain M. Cockburn, Boston University Tyler Cowen, George Mason University Jason Furman, Harvard Kennedy School Patrick Francois, University of British Columbia Alberto Galasso, University of Toronto Joshua Gans, University of Toronto Avi Goldfarb, University of Toronto Austan Goolsbee, University of Chicago Booth School of Business Rebecca Henderson, Harvard Business School Ginger Zhe Jin, University of Maryland Benjamin F. Jones, Northwestern University Charles I. Jones, Stanford University Daniel Kahneman, Princeton University Anton Korinek, Johns Hopkins University Mara Lederman, University of Toronto Hong Luo, Harvard Business School John McHale, National University of Ireland Paul R. Milgrom, Stanford University Matthew Mitchell, University of Toronto Alexander Oettl, Georgia Institute of Technology Andrea Prat, Columbia Business School Manav Raj, New York University Pascual Restrepo, Boston University Daniel Rock, MIT Sloan School of Management Jeffrey D. Sachs, Columbia University Robert Seamans, New York University Scott Stern, MIT Sloan School of Management Betsey Stevenson, University of Michigan Joseph E. Stiglitz. Columbia University Chad Syverson, University of Chicago Booth School of Business Matt Taddy, University of Chicago Booth School of Business Steven Tadelis, University of California, Berkeley Manuel Trajtenberg, Tel Aviv University Daniel Trefler, University of Toronto Catherine Tucker, MIT Sloan School of Management Hal Varian, University of California, Berkeley
Computers by Portuguese Conference on Artificial Intelligence (7 : 1995 : Funchal)
This book presents the refereed proceedings of the 7th Portuguese Conference on Artificial Intelligence, EPIA'95, held in Funchal, Madeira Island, Portugal, in October 1995. The 30 revised full papers and the 15 poster presentations included were selected during a highly competitive selection process from a total of 167 submissions from all over the world. Among the topics covered are automated reasoning and theorem proving, belief revision, constraint-based reasoning, distributed artificial intelligence, genetic algorithms, machine learning, neural networks, non-monotonic reasoning, planning and case-based reasoning, qualitative reasoning, robotics and control, and theory of computation.
Build smart cybersecurity systems with the power of machine learning and deep learning to protect your corporate assets Key Features Identify and predict security threats using artificial intelligence Develop intelligent systems that can detect unusual and suspicious patterns and attacks Learn how to test the effectiveness of your AI cybersecurity algorithms and tools Book Description Today's organizations spend billions of dollars globally on cybersecurity. Artificial intelligence has emerged as a great solution for building smarter and safer security systems that allow you to predict and detect suspicious network activity, such as phishing or unauthorized intrusions. This cybersecurity book presents and demonstrates popular and successful AI approaches and models that you can adapt to detect potential attacks and protect your corporate systems. You'll learn about the role of machine learning and neural networks, as well as deep learning in cybersecurity, and you'll also learn how you can infuse AI capabilities into building smart defensive mechanisms. As you advance, you'll be able to apply these strategies across a variety of applications, including spam filters, network intrusion detection, botnet detection, and secure authentication. By the end of this book, you'll be ready to develop intelligent systems that can detect unusual and suspicious patterns and attacks, thereby developing strong network security defenses using AI. What you will learn Detect email threats such as spamming and phishing using AI Categorize APT, zero-days, and polymorphic malware samples Overcome antivirus limits in threat detection Predict network intrusions and detect anomalies with machine learning Verify the strength of biometric authentication procedures with deep learning Evaluate cybersecurity strategies and learn how you can improve them Who this book is for If you’re a cybersecurity professional or ethical hacker who wants to build intelligent systems using the power of machine learning and AI, you’ll find this book useful. Familiarity with cybersecurity concepts and knowledge of Python programming is essential to get the most out of this book.
This book constitutes the refereed proceedings of the 11th Portuguese Conference on Artificial Intelligence, EPIA 2003, held in Beja, Portugal in December 2003. The 29 revised full papers and 20 revised short papers presented were carefully reviewed and selected from a total of 119 submissions. In accordance with the five constituting workshops, the papers are organized in topical sections on artificial life and evolutionary algorithms, constraint and logic programming systems, extraction of knowledge from databases, multi-agent systems and AI for the Internet, and natural language processing and text retrieval.
In clear, readable language, consultant and researcher Kevin Desouza accomplishes an unlikely feat: explaining artificial intelligence to nonspecialists, in a way that experts will recognize and accept as correct and immediately applicable. Workers in knowledge management are relatively isolated from each other, businesspeople are still unconvinced that artificial intelligence has much to offer, and engineers creating the latest algorithm or device seldom consider its value for businesspeople--Desouza seeks to change all that. He maintains that knowledge will be traded like physical goods, and that businesses must leverage knowledge resources within its organizations to survive in a highly competitive marketplace. Introducing us the concepts and significance of knowledge management, he shows that incorporating artificial intelligence computer-based techniques into business settings can provide truly significant gains in productivity. This book is among the first of its kind to provide a comprehensive one-stop guide to the basics of knowledge management, plus a lucid explanation of A.I., and how to use it in almost all types of organizational settings.