Artificial intelligence (AI) Books

Book SynopsisTable of Contents CHAPTER 1 Describe Artificial Intelligence workloads and considerations CHAPTER 2 Describe fundamental principles of machine learning on Azure CHAPTER 3 Describe features of computer vision workloads on Azure CHAPTER 4 Describe features of Natural Language Processing (NLP) workloads on Azure CHAPTER 5 Describe features of conversational AI workloads on Azure

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Book SynopsisComputing power has revolutionized the theory and practice of statistical inference. This book delivers a concentrated course in modern statistical thinking by tracking the revolution from classical theories to the large-scale prediction algorithms of today. Anyone who applies statistical methods to data will benefit from this landmark text.Trade Review'How and why is computational statistics taking over the world? In this serious work of synthesis that is also fun to read, Efron and Hastie, two pioneers in the integration of parametric and nonparametric statistical ideas, give their take on the unreasonable effectiveness of statistics and machine learning in the context of a series of clear, historically informed examples.' Andrew Gelman, Columbia University, New York'This unusual book describes the nature of statistics by displaying multiple examples of the way the field has evolved over the past sixty years, as it has adapted to the rapid increase in available computing power. The authors' perspective is summarized nicely when they say, 'very roughly speaking, algorithms are what statisticians do, while inference says why they do them'. The book explains this 'why'; that is, it explains the purpose and progress of statistical research through a close look at many major methods, methods the authors themselves have advanced and studied at great length. Both enjoyable and enlightening, Computer Age Statistical Inference is written especially for those who want to hear the big ideas, and see them instantiated through the essential mathematics that defines statistical analysis. It makes a great supplement to the traditional curricula for beginning graduate students.' Rob Kass, Carnegie Mellon University, Pennsylvania'This is a terrific book. It gives a clear, accessible, and entertaining account of the interplay between theory and methodological development that has driven statistics in the computer age. The authors succeed brilliantly in locating contemporary algorithmic methodologies for analysis of 'big data' within the framework of established statistical theory.' Alastair Young, Imperial College London'This is a guided tour of modern statistics that emphasizes the conceptual and computational advances of the last century. Authored by two masters of the field, it offers just the right mix of mathematical analysis and insightful commentary.' Hal Varian, Google'Efron and Hastie guide us through the maze of breakthrough statistical methodologies following the computing evolution: why they were developed, their properties, and how they are used. Highlighting their origins, the book helps us understand each method's roles in inference and/or prediction. The inference-prediction distinction maintained throughout the book is a welcome and important novelty in the landscape of statistics books.' Galit Shmueli, National Tsing Hua University'A masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twenty-first century.' Stephen Stigler, University of Chicago, and author of Seven Pillars of Statistical Wisdom'Computer Age Statistical Inference offers a refreshing view of modern statistics. Algorithmics are put on equal footing with intuition, properties, and the abstract arguments behind them. The methods covered are indispensable to practicing statistical analysts in today's big data and big computing landscape.' Robert Gramacy, University of Chicago Booth School of Business'Every aspiring data scientist should carefully study this book, use it as a reference, and carry it with them everywhere. The presentation through the two-and-a-half-century history of statistical inference provides insight into the development of the discipline, putting data science in its historical place.' Mark Girolami, Imperial College London'Efron and Hastie are two immensely talented and accomplished scholars who have managed to brilliantly weave the fiber of 250 years of statistical inference into the more recent historical mechanization of computing. This book provides the reader with a mid-level overview of the last 60-some years by detailing the nuances of a statistical community that, historically, has been self-segregated into camps of Bayes, frequentist, and Fisher yet in more recent years has been unified by advances in computing. What is left to be explored is the emergence of, and role that, big data theory will have in bridging the gap between data science and statistical methodology. Whatever the outcome, the authors provide a vision of high-speed computing having tremendous potential to enable the contributions of statistical inference toward methodologies that address both global and societal issues.' Rebecca Doerge, Carnegie Mellon University, Pennsylvania'In this book, two masters of modern statistics give an insightful tour of the intertwined worlds of statistics and computation. Through a series of important topics, Efron and Hastie illuminate how modern methods for predicting and understanding data are rooted in both statistical and computational thinking. They show how the rise of computational power has transformed traditional methods and questions, and how it has pointed us to new ways of thinking about statistics.' David Blei, Columbia University, New York'Absolutely brilliant. This beautifully written compendium reviews many big statistical ideas, including the authors' own. A must for anyone engaged creatively in statistics and the data sciences, for repeated use. Efron and Hastie demonstrate the ever-growing power of statistical reasoning, past, present, and future.' Carl Morris, Harvard University, Massachusetts'Computer Age Statistical Inference gives a lucid guide to modern statistical inference for estimation, hypothesis testing, and prediction. The book seamlessly integrates statistical thinking with computational thinking, while covering a broad range of powerful algorithms for learning from data. It is extraordinarily rare and valuable to have such a unified treatment of classical (and classic) statistical ideas and recent 'big data' and machine learning ideas. Accessible real-world examples and insightful remarks can be found throughout the book.' Joseph K. Blitzstein, Harvard University, Massachusetts'Among other things, it is an attempt to characterize the current state of statistics by identifying important tools in the context of their historical development. It also offers an enlightening series of illustrations of the interplay between computation and inference … This is an attractive book that invites browsing by anyone interested in statistics and its future directions.' Bill Satzer, Mathematical Association of America Reviews'My take on Computer Age Statistical Inference is that experienced statisticians will find it helpful to have such a compact summary of twentieth-century statistics, even if they occasionally disagree with the book's emphasis; students beginning the study of statistics will value the book as a guide to statistical inference that may offset the dangerously mind-numbing experience offered by most introductory statistics textbooks; and the rest of us non-experts interested in the details will enjoy hundreds of hours of pleasurable reading.' Joseph Rickert, RStudio (www.rstudio.com)'Efron and Hastie (both, Stanford Univ.) have superbly crafted a central text/reference book that presents a broad overview of modern statistics. The work examines major developments in computation from the late-20th and early-21st centuries, ranging from electronic computations to 'big data' analysis. Focusing primarily on the last six decades, the text thoroughly documents the progression within the discipline of statistics … This text is highly recommended for graduate libraries.' D. J. Gougeon, ChoiceTable of ContentsPart I. Classic Statistical Inference: 1. Algorithms and inference; 2. Frequentist inference; 3. Bayesian inference; 4. Fisherian inference and maximum likelihood estimation; 5. Parametric models and exponential families; Part II. Early Computer-Age Methods: 6. Empirical Bayes; 7. James–Stein estimation and ridge regression; 8. Generalized linear models and regression trees; 9. Survival analysis and the EM algorithm; 10. The jackknife and the bootstrap; 11. Bootstrap confidence intervals; 12. Cross-validation and Cp estimates of prediction error; 13. Objective Bayes inference and Markov chain Monte Carlo; 14. Statistical inference and methodology in the postwar era; Part III. Twenty-First Century Topics: 15. Large-scale hypothesis testing and false discovery rates; 16. Sparse modeling and the lasso; 17. Random forests and boosting; 18. Neural networks and deep learning; 19. Support-vector machines and kernel methods; 20. Inference after model selection; 21. Empirical Bayes estimation strategies; Epilogue; References; Index.

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Book SynopsisWhat is AI? A dominant view describes it as the quest "to solve intelligence" - a solution supposedly to be found in the secret logic of the mind or in the deep physiology of the brain, such as in its complex neural networks. The Eye of the Master argues, to the contrary, that the inner code of AI is shaped not by the imitation of biological intelligence, but the intelligence of labour and social relations, as it is found in Babbage's "calculating engines" of the industrial age as well as in the recent algorithms for image recognition and surveillance. The idea that AI may one day become autonomous (or "sentient", as someone thought of Google's LaMDA) is pure fantasy. Computer algorithms have always imitated the form of social relations and the organisation of labour in their own inner structure and their purpose remains blind automation. The Eye of the Master urges a new literacy on AI for scientists, journalists and new generations of activists, who should recognise that the "mystery" of AI is just the automation of labour at the highest degree, not intelligence per se.Trade ReviewWe are surrounded by stories about AI threatening jobs, as if it were a power haunting labor from outside and above. The Eye of the Master radically challenges such a view. What Matteo Pasquinelli demonstrates is that labor is at root of the historical development of AI. Tales of expropriation and resistance, automation and struggle crisscross the pages of this passionate book, which is at same time an amazing academic achievement and a political weapon to rethink the politics of AI. -- Sandro Mezzadra, co-author of The Politics of OperationsIn this original and extremely timely book, Matteo Pasquinelli offers nothing less than a long-range history and critical analysis of a labour theory of automation and knowledge. He uses detailed studies both of the remarkable accounts of general intellect and the extractive and exploitative organisation of the industrial workplace produced in nineteenth-century British political economy and of the challenging developments of models of machine intelligence and computational systems developed in the mid-twentieth century United States to unlock the sources and meanings of the politics of artificial intelligence. The work shows how Marx's depiction of the development of the social individual under industrial capitalism provides indispensable resources for making sense now of what artificial intelligence means, and the forms of economic and political order that its embodiment of knowledge and control express. At a moment when apostles and prophets of machine intelligence proclaim both a utopian world of effortless control and a catastrophe of extinction, Pasquinelli's patient and clever work provides a crucial insight into the past and future of AI monopolies and their consequences. -- Simon Schaffer, author of Babbage’s Intelligence (1994) and OK computer (2001)Artificial Intelligence and its impact on society is on everyone's lips, but how was AI shaped by society in the first place? This amazing account of its emergence, starting with the evolution of labor division and automatization, is a must-read. Pasquinelli's book not only shows us where we came from but also how we might escape the problematic consequences of this evolution. -- Jürgen Renn, Director at the Max Planck Institute for the History of Science and Founding Director of the Max Planck Institute for Geoanthropology.Table of ContentsIntroduction: AI as Division of Labour1 The Material Tools of Algorithmic ThinkingPart ITHE INDUSTRIAL AGE2 Babbage and the Mechanisation of Mental Labour3 The Machinery Question4 The Origins of Marx's General Intellect5 The Abstraction of LabourPart IITHE INFORMATION AGE6 The Self-Organisation of the Cybernetic Mind7 The Automation of Pattern Recognition8 Hayek and the Epistemology of Connectionism9 Th e Invention of the PerceptronConclusion: The Automation of General Intelligence

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Book SynopsisHow smart machines are transforming us all — and what we should do about it. The smart-machines revolution is re-shaping our lives and our societies. Here, Nigel Shadbolt, one of Britain’s leading authorities on artificial intelligence, and Roger Hampson dispel terror, confusion, and misconception. They argue that it is human stupidity, not artificial intelligence, that should concern us. Lucid, well-informed, and deeply human, The Digital Ape offers a unique approach to some of the biggest questions about our future.Trade Review‘[W]e should be grateful to Sir Nigel Shadbolt and Roger Hampson for pausing for breath and helping us to think through the true significance of our latest technological developments.’ * Financial Times *‘Numbed by dire warnings of technological Armageddon? Computer scientist Nigel Shadbolt and economist Roger Hampson dispel the miasma with this superb survey of the landscape we “digital apes” have wrought.’ -- Barbara Kiser * Nature *'Nigel Shadbolt is one of the most fascinating and important scientists alive today.' -- Professor Jim Al-Khalili'There has never been a more important time to discuss what it means to be human, in the past, now, and in the future. This is a book for anyone interested in getting behind the headlines and understanding how technology is impacting our world. The writers are two masters in their field who are not only erudite but immensely humane and compassionate.' -- Martha Lane Fox'This is a brilliantly readable, genuinely cutting-edge book that is also often very entertaining. Of all the recent studies of automation and AI, The Digital Ape stands head and shoulders above the rest. Shadbolt and Hampson have written a landmark book.' * Andrew Keen, author of How to Fix the Future and The Internet is Not the Answer *‘Rich in ideas and insights, the book is especially strong on our growing personal relationships with Alexa and other robots … An upbeat — even reassuring — take on what will be an AI-saturated future.’ STARRED REVIEW * Kirkus Reviews *‘All explore the relationship between the human animal and what might be its most momentous creation yet: artificial intelligence … In a series of wide-ranging chapters, the authors argue that human beings are not just distinguished by their ability to use tools but also largely shaped by it.’ * Weekend Australian *‘[An] interdisciplinary approach comes over in The Digital Ape, which has arresting sentences.’ * Computer Weekly *

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Book SynopsisDisruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines can help you prepare.Artificial intelligence (AI) has impacted many industries around the world—banking and finance, pharmaceuticals, automotive, medical technology, manufacturing, and retail. But it has only just begun its odyssey toward cheaper, better, and faster predictions that drive strategic business decisions. When prediction is taken to the max, industries transform, and with such transformation comes disruption.What is at the root of this? In their bestselling first book, Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction, they go deeper, examining the most basic unit of analysis: the decision. The authors explain that the two key decision-making ingredients are prediction and judgment, and we perform both together in our minds, often without realizing it. The rise of AI is shifting prediction from humans to machines, relieving people from this cognitive load while increasing the speed and accuracy of decisions.This sets the stage for a flourishing of new decisions and has profound implications for system-level innovation. Redesigning systems of interdependent decisions takes time—many industries are in the quiet before the storm—but when these new systems emerge, they can be disruptive on a global scale. Decision-making confers power. In industry, power confers profits; in society, power confers control. This process will have winners and losers, and the authors show how businesses can leverage opportunities, as well as protect their positions.Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policymaker on how to make the coming AI disruptions work for you rather than against you.Trade Review"Highly accessible, cleverly written [with] great ideas for practically implementing AI across a system." — Dialogue"A must for anyone with an interest in how the world may look in future." — Institute of Leadership and Management Edge magazineNamed one of the 10 Best Business Books of 2022 by ForbesA Toronto Star Bestseller"This jauntily written and thought-provoking book sketches out how this new economic revolution might unfold." — Financial Times"…a timely and insightful follow up to Prediction Machines." — Engineering and Technology Magazine, The Institution of Engineering and Technology"It's an interesting argument, and the book that Gans and his co-authors have published makes a strong case for developing system-level AI applications in organizations and institutions…" — ForbesAdvance Praise for Power and Prediction:"This is a book that leaders of all types of organizations should read. It explains the enormous size of the AI opportunity and the challenges in getting there." — Dominic Barton, Chair, Rio Tinto; former Global Managing Partner, McKinsey & Company"AI may be to the twenty-first century what electricity was to the twentieth. This is the best book yet that considers what it will mean for all who participate in our economy." — Lawrence H. Summers, Charles W. Eliot University Professor and former president, Harvard University; former secretary, US Treasury; and former chief economist, World Bank"AI will surely displace jobs and disrupt industries in the decades to come. The system-level changes that are on the horizon are excitingly discussed in this book." — Vinod Khosla, founder, Khosla Ventures; cofounder, Sun Microsystems"Power and Prediction is a hugely thought-provoking and inspiring primer on how to shape strategy and design organizations in the age of AI." — Heather Reisman, founder and CEO, Indigo Books and Music"We're told AI will be the most important thing humanity ever works on, yet it feels abstract and niche in its current impact on the world. This book is a must-read for anyone who wants to peek around the corner into AI's future." — Shivon Zilis, Director of Operations and Special Projects, Neuralink; former project director, Tesla"Nobody provides more insight into the fundamental economics of AI and what AI truly enables than Agrawal, Gans, and Goldfarb." — Tiff Macklem, governor, Bank of Canada"Agrawal, Gans, and Goldfarb have done it again! Their new book, Power and Prediction, is destined to become the definitive guide to understanding how and why AI is transforming the economy." — Erik Brynjolfsson, Jerry Yang and Akiko Yamazaki Professor, Stanford University; Director, Stanford Digital Economy Lab; coauthor, The Second Machine Age"Whether we like it or not, artificial intelligence is set to influence every aspect of our lives. How can we make sure that individuals, companies, and organizations benefit from it rather than waste time and resources dealing with unintended consequences? This readable book provides an excellent introduction, emphasizing how AI can improve what we do by providing better predictions and helping reorganize systems." — Daron Acemoglu, Elizabeth and James Killian Professor of Economics, MIT; author, When Nations Fail"Power and Prediction is an important book not only for economists who model the impact of artificial intelligence and entrepreneurs who want to maximize its benefits but also for social scientists and public policy analysts. The authors put prediction problems squarely within the systems and the rules in which they operate to help us understand what will work and why. Along the way, they shine a new light on the importance of systems and rules. A must read for everyone in the public as well as the private sector." — Janice Gross Stein, Professor of Political Science, Munk School, University of Toronto

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Book SynopsisElgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences and law, expertly written by the world's leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas. Woodrow Barfield and Ugo Pagallo present a succinct introduction to the legal issues related to the design and use of artificial intelligence (AI). Exploring human rights, constitutional law, data protection, criminal law, tort law, and intellectual property law, they consider the laws of a number of jurisdictions including the US, the European Union, Japan, and China, making reference to case law and statutes. Key features include: a critical insight into human rights and constitutional law issues which may be affected by the use of AI discussion of the concept of legal personhood and how the law might respond as AI evolves in intelligence an introduction to current laws and statutes which apply to AI and an identification of the areas where future challenges to the law may arise. This Advanced Introduction is ideal for law and social science students with an interest in how the law applies to AI. It also provides a useful entry point for legal practitioners seeking an understanding of this emerging field.Trade Review‘Barfield and Pagallo’s book offers a great overview on the most discussed and practically relevant legal discussions about AI. The authors portray the currently applicable laws and the relevant decisions comprehensibly for law students and non-lawyers. The references throughout the book as well as a list of additional topics will assist readers who would like to expand their knowledge. They present an overview and offer law students several carefully chosen gateways through which readers may explore the vast and steadily growing literature in the field. If you are looking for a concise book on the manifold issues of artificial intelligence and law, Barfield and Pagallo’s Advanced Introduction to Law and Artificial Intelligence is a great starting point.’ -- Carolin Kemper, Prometheus‘Edward Elgar has hit the nail on the head by choosing this particular topic to publish in its Edward Elgar Advanced Introduction Series. It is a much need book at this time when the hype about Artificial Intelligence (AI) is at a crescendo level.’ -- Sally Ramage, Criminal Lawyer‘This book provides an authoritative introduction into the specific legal topics covered, and a springboard into further research, and will prove a useful resource for its intended audience.’ -- Stephanie Falconer, Law in Context'A much needed comprehensive and up-to-date introduction to the law of AI, a must read for all ICT lawyers!' --Giovanni Sartor, University of Bologna and European University Institute, ItalyTable of ContentsContents: Introduction to Law and Artificial Intelligence 1. Definitions, Actors, Concepts 2. Human Rights Considerations 3. Constitutional Law Issues 4. Legal Personality and Artificial Intelligence 5. Issues of Data Protection 6. Tort Law Approaches 7. Criminal Law 8. Copyright Law 9. Patent Law 10. Business Law, Antitrust, and Trade Secrets 11. Looking Ahead: Towards a Law of Artificial Intelligence Index

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Book SynopsisIntelligent machines are revolutionizing business.Machine learning and data analytics are powering a wave of groundbreaking technologies. Is your company ready?If you read nothing else on how intelligent machines are revolutionizing business, read these 10 articles. We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you understand how these technologies work together, how to adopt them, and why your strategy can't ignore them.In this book you'll learn how: Data science, driven by artificial intelligence and machine learning, is yielding unprecedented business insights Blockchain has the potential to restructure the economy Drones and driverless vehicles are becoming essential tools 3-D printing is making new business models possible Augmented reality is transforming retail and manufacturing Smart speakers are redefining the rules of marketing Humans and machines are working together to reach new levels of productivity This collection of articles includes "Artificial Intelligence for the Real World," by Thomas H. Davenport and Rajeev Ronanki; "Stitch Fix's CEO on Selling Personal Style to the Mass Market," by Katrina Lake; "Algorithms Need Managers, Too," by Michael Luca, Jon Kleinberg, and Sendhil Mullainathan; "Marketing in the Age of Alexa," by Niraj Dawar; "Why Every Organization Needs an Augmented Reality Strategy," by Michael E. Porter and James E. Heppelmann; "Drones Go to Work," by Chris Anderson; "The Truth About Blockchain," by Marco Iansiti and Karim R. Lakhani; "The 3-D Printing Playbook," by Richard A. D’Aveni; "Collaborative Intelligence: Humans and AI Are Joining Forces," by H. James Wilson and Paul R. Daugherty; "When Your Boss Wears Metal Pants," by Walter Frick; and "Managing Our Hub Economy," by Marco Iansiti and Karim R. Lakhani.Trade Review"a timely, relevant compendium of HBR articles. The authors' insights…have value for CIOs and business people trying to use analytics in the running their businesses." -- CIO Magazine

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Book SynopsisComputational Intelligence: An Introduction, Second Edition offers an in-depth exploration into the adaptive mechanisms that enable intelligent behaviour in complex and changing environments. The main focus of this text is centred on the computational modelling of biological and natural intelligent systems, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems and evolutionary computation. Engelbrecht provides readers with a wide knowledge of Computational Intelligence (CI) paradigms and algorithms; inviting readers to implement and problem solve real-world, complex problems within the CI development framework. This implementation framework will enable readers to tackle new problems without any difficulty through a single Java class as part of the CI library. Key features of this second edition include: A tutorial, hands-on based presentation of the material. State-of-the-art coverage of the most rTable of ContentsFigures. Tables. Algorithms. Preface. Part I INTRODUCTION. 1 Introduction to Computational Intelligence. 1.1 Computational Intelligence Paradigms. 1.2 Short History. 1.3 Assignments. Part II ARTIFICIAL NEURAL NETWORKS. 2 The Artificial Neuron. 2.1 Calculating the Net Input Signal. 2.2 Activation Functions. 2.3 Artificial Neuron Geometry. 2.4 Artificial Neuron Learning. 2.5 Assignments. 3 Supervised Learning Neural Networks. 3.1 Neural Network Types. 3.2 Supervised Learning Rules. 3.3 Functioning of Hidden Units. 3.4 Ensemble Neural Networks. 3.5 Assignments. 4 Unsupervised Learning Neural Networks. 4.1 Background. 4.2 Hebbian Learning Rule. 4.3 Principal Component Learning Rule. 4.4 Learning Vector Quantizer-I. 4.5 Self-Organizing Feature Maps. 4.6 Assignments. 5 Radial Basis Function Networks. 5.1 Learning Vector Quantizer-II. 5.2 Radial Basis Function Neural Networks. 5.3 Assignments. 6 Reinforcement Learning. 6.1 Learning through Awards. 6.2 Model-Free Reinforcement LearningModel. 6.3 Neural Networks and Reinforcement Learning. 6.4 Assignments. 7 Performance Issues (Supervised Learning). 7.1 PerformanceMeasures. 7.2 Analysis of Performance. 7.3 Performance Factors. 7.4 Assignments. Part III EVOLUTIONARY COMPUTATION. 8 Introduction to Evolutionary Computation. 8.1 Generic Evolutionary Algorithm. 8.2 Representation – The Chromosome. 8.3 Initial Population. 8.4 Fitness Function. 8.5 Selection. 8.6 Reproduction Operators. 8.7 Stopping Conditions. 8.8 Evolutionary Computation versus Classical Optimization. 8.9 Assignments. 9 Genetic Algorithms. 9.1 Canonical Genetic Algorithm. 9.2 Crossover. 9.3 Mutation. 9.4 Control Parameters. 9.5 Genetic Algorithm Variants. 9.6 Advanced Topics. 9.7 Applications. 9.8 Assignments. 10 Genetic Programming. 10.1 Tree-Based Representation. 10.2 Initial Population. 10.3 Fitness Function. 10.4 Crossover Operators. 10.5 Mutation Operators. 10.6 Building Block Genetic Programming. 10.7 Applications. 10.8 Assignments. 11 Evolutionary Programming. 11.1 Basic Evolutionary Programming. 11.2 Evolutionary Programming Operators. 11.3 Strategy Parameters. 11.4 Evolutionary Programming Implementations. 11.5 Advanced Topics. 11.6 Applications. 11.7 Assignments. 12 Evolution Strategies. 12.1 (1+1)-ES. 12.2 Generic Evolution Strategy Algorithm. 12.3 Strategy Parameters and Self-Adaptation. 12.4 Evolution Strategy Operators. 12.5 Evolution Strategy Variants. 12.6 Advanced Topics. 12.7 Applications of Evolution Strategies. 12.8 Assignments. 13 Differential Evolution. 13.1 Basic Differential Evolution. 13.2 DE/x/y/z. 13.3 Variations to Basic Differential Evolution. 13.4 Differential Evolution for Discrete-Valued Problems. 13.5 Advanced Topics. 13.6 Applications. 13.7 Assignments. 14 Cultural Algorithms. 14.1 Culture and Artificial Culture. 14.2 Basic Cultural Algorithm. 14.3 Belief Space. 14.4 Fuzzy Cultural Algorithm. 14.5 Advanced Topics. 14.6 Applications. 14.7 Assignments. 15 Coevolution. 15.1 Coevolution Types. 15.2 Competitive Coevolution. 15.3 Cooperative Coevolution. 15.4 Assignments. Part IV COMPUTATIONAL SWARM INTELLIGENCE. 16 Particle Swarm Optimization. 16.1 Basic Particle Swarm Optimization. 16.2 Social Network Structures. 16.3 Basic Variations. 16.4 Basic PSO Parameters. 16.5 Single-Solution Particle SwarmOptimization. 16.6 Advanced Topics. 16.7 Applications. 16.8 Assignments. 17 Ant Algorithms. 17.1 Ant Colony OptimizationMeta-Heuristic. 17.2 Cemetery Organization and Brood Care. 17.3 Division of Labor. 17.4 Advanced Topics. 17.5 Applications. 17.6 Assignments. Part V ARTIFICIAL IMMUNE SYSTEMS. 18 Natural Immune System. 18.1 Classical View. 18.2 Antibodies and Antigens. 18.3 TheWhite Cells. 18.4 Immunity Types. 18.5 Learning the Antigen Structure. 18.6 The Network Theory. 18.7 The Danger Theory. 18.8 Assignments. 19 Artificial Immune Models. 19.1 Artificial Immune System Algorithm. 19.2 Classical ViewModels. 19.3 Clonal Selection TheoryModels. 19.4 Network TheoryModels. 19.5 Danger TheoryModels. 19.6 Applications and Other AIS models. 19.7 Assignments. Part VI FUZZY SYSTEMS. 20 Fuzzy Sets. 20.1 Formal Definitions. 20.2 Membership Functions. 20.3 Fuzzy Operators. 20.4 Fuzzy Set Characteristics. 20.5 Fuzziness and Probability. 20.6 Assignments. 21 Fuzzy Logic and Reasoning. 21.1 Fuzzy Logic. 21.2 Fuzzy Inferencing. 21.3 Assignments. 22 Fuzzy Controllers. 22.1 Components of Fuzzy Controllers. 22.2 Fuzzy Controller Types. 22.3 Assignments. 23 Rough Sets. 23.1 Concept of Discernibility. 23.2 Vagueness in Rough Sets. 23.3 Uncertainty in Rough Sets. 23.4 Assignments. References. A Optimization Theory. A.1 Basic Ingredients of Optimization Problems. A.2 Optimization ProblemClassifications. A.3 Optima Types. A.4 OptimizationMethod Classes. A.5 Unconstrained Optimization. A.6 Constrained Optimization. A.7 Multi-Solution Problems. A.8 Multi-Objective Optimization. A.9 Dynamic Optimization Problems. Index.

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Book SynopsisCould 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 deTrade ReviewThis book is an excellent introduction to some of the most important problems within the philosophy of artificial intelligence... Carter's book is in fact highly interdisciplinary, but he has clearly succeeded in integrating some very crucial topics regarding artificial intelligence in a clever and thought-provoking manner... The book will be an excellent choice as a textbook to be used for a university course introducing important and interesting problems within the philosophy of artificial intelligence. History and Philosophy of Logic Like good science fiction, Matt Carter's Minds and Computers essentially constitutes an exploration into what makes human beings what they are... [It] is a teaching tool par excellence and should find its way into every classroom where the philosophy of mind is being studied. Heythrop Journal

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Book SynopsisEssential exploration of artificial intelligence and its key roles, components, uses, benefits and challenges within a wide range of real-world applications. Dr. Kyle Allison, senior executive, professor, speaker and author who is focused on all things digital uses his multi-faceted knowledge and experience from teaching and consulting to offer valuable and succinct need-to-know facts in 6 laminated pages. Working on digital strategies of all sizes including retailers like Best Buy, Dick's Sporting Goods, VF Corporation and more, Dr. Allison focuses on the most important functions of AI for business strategy in our famous QuickStudy format that gives more answers per page than any other source. AI has the power to improve performance, ensure quality standards and boost the output of resources. At this price do not miss this eye-opening tool that can offer you direction for the use of AI for everything from data management and finance to customer service, human resources, and more.

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Book SynopsisProgramming Game AI by Example provides a comprehensive and practical introduction to the “bread and butter” AI techniques used by the game development industry, leading the reader through the process of designing, programming, and implementing intelligent agents for action games using the C++ programming language. Techniques covered include state- and goal-based behavior, inter-agent communication, individual and group steering behaviors, team AI, graph theory, search, path planning and optimization, triggers, scripting, scripted finite state machines, perceptual modeling, goal evaluation, goal arbitration, and fuzzy logic.

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Book SynopsisLearn how to model and train advanced neural networks to implement a variety of Computer Vision tasksKey Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraints Book DescriptionDeep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a model for image and video classification Use features from a pre-trained Convolutional Neural Network model for image retrieval Understand and implement object detection using the real-world Pedestrian Detection scenario Learn about various problems in image captioning and how to overcome them by training images and text together Implement similarity matching and train a model for face recognition Understand the concept of generative models and use them for image generation Deploy your deep learning models and optimize them for high performance Who this book is forThis book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.Table of ContentsTable of Contents Getting Started Image Classification Image Retrieval Object Detection Semantic Segmentation Similarity Learning Image Captioning Generative models Video Classification Deployment

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Book SynopsisNEW YORK TIMES BESTSELLER • How will Artificial Intelligence affect crime, war, justice, jobs, society and our very sense of being human? The rise of AI has the potential to transform our future more than any other technology—and there’s nobody better qualified or situated to explore that future than Max Tegmark, an MIT professor who’s helped mainstream research on how to keep AI beneficial.   How can we grow our prosperity through automation without leaving people lacking income or purpose? What career advice should we give today’s kids? How can we make future AI systems more robust, so that they do what we want without crashing, malfunctioning or getting hacked? Should we fear an arms race in lethal autonomous weapons? Will machines eventually outsmart us at all tasks, replacing humans on the job market and perhaps altogether? Will AI help life flourish like never before or give us more power than we can handle?

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Book SynopsisFinancial Times Best Books of the Year 2018TechRepublic Top Books Every Techie Should ReadBook DescriptionHow will AI evolve and what major innovations are on the horizon? What will its impact be on the job market, economy, and society? What is the path toward human-level machine intelligence? What should we be concerned about as artificial intelligence advances?Architects of Intelligence contains a series of in-depth, one-to-one interviews where New York Times bestselling author, Martin Ford, uncovers the truth behind these questions from some of the brightest minds in the Artificial Intelligence community.Martin has wide-ranging conversations with twenty-three of the world's foremost researchers and entrepreneurs working in AI and robotics: Demis Hassabis (DeepMind), Ray Kurzweil (Google), Geoffrey Hinton (Univ. of Toronto and Google), Rodney Brooks (Rethink Robotics), Yann LeCun (Facebook) , Fei-Fei Li (Stanford and Google), Yoshua Bengio (Univ. of Montreal), Andrew Ng (AI Fund), Daphne Koller (Stanford), Stuart Russell (UC Berkeley), Nick Bostrom (Univ. of Oxford), Barbara Grosz (Harvard), David Ferrucci (Elemental Cognition), James Manyika (McKinsey), Judea Pearl (UCLA), Josh Tenenbaum (MIT), Rana el Kaliouby (Affectiva), Daniela Rus (MIT), Jeff Dean (Google), Cynthia Breazeal (MIT), Oren Etzioni (Allen Institute for AI), Gary Marcus (NYU), and Bryan Johnson (Kernel).Martin Ford is a prominent futurist, and author of Financial Times Business Book of the Year, Rise of the Robots. He speaks at conferences and companies around the world on what AI and automation might mean for the future.Meet the minds behind the AI superpowers as they discuss the science, business and ethics of modern artificial intelligence. Read James Manyika’s thoughts on AI analytics, Geoffrey Hinton’s breakthroughs in AI programming and development, and Rana el Kaliouby’s insights into AI marketing. This AI book collects the opinions of the luminaries of the AI business, such as Stuart Russell (coauthor of the leading AI textbook), Rodney Brooks (a leader in AI robotics), Demis Hassabis (chess prodigy and mind behind AlphaGo), and Yoshua Bengio (leader in deep learning) to complete your AI education and give you an AI advantage in 2019 and the future.

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Book SynopsisWith this practical book, author Lomit Patel shows you how to use AI and automation to provide an operational layer atop those acquisition solutions to deliver amazing results for your company.

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Book SynopsisThis pioneering Research Handbook on Public Management and Artificial Intelligence provides a comprehensive overview of the potentials, challenges, and governance principles of AI in a public management context. Multidisciplinary in approach, it draws on a variety of jurisdictional perspectives and expertly analyses key topics relating to this socio-technical phenomenon.Showcasing contributions by a collection of eminent scholars from across the globe, this Research Handbook presents cutting-edge research on AI in public management. Organised into three parts corresponding with distinct foci of research, it explores the adoption and implementation of AI in public management settings, presents specific case studies and examples of AI in the public sector, and outlines future trends and directions in the evolution of AI adoption and use in public management.Based on empirical research from a global perspective, this Research Handbook will prove invaluable to practitioners, policymakers, and public managers both as users and co-creators of AI-enabled services. Researchers and academics in the fields of organisational innovation, public management, technology, public administration, and public policy will also find this to be an essential read.Trade Review‘As AI makes an unprecedented leap forward, there are fundamental questions about the role it will and should play in government. This must-read volume brings together contributions from leaders in digital governance research from around the globe to answer these questions. With its truly international perspective and breadth, this is an essential reference for the AI era.’ -- Karen Mossberger, Arizona State University, USTable of ContentsContents: Foreword xvi Introduction to the Research Handbook on Public Management and Artificial Intelligence 1 Yannis Charalabidis, Rony Medaglia and Colin van Noordt PART I ADOPTION AND IMPLEMENTATION OF AI IN PUBLIC MANAGEMENT 1 Artificial intelligence algorithms and applications in the public sector: a systematic literature review based on the PRISMA approach 8 David Valle-Cruz J., Ramon Gil-Garcia and Rodrigo Sandoval-Almazan 2 A trifold research synthesis on AI-induced service automation 27 Matthias Döring and Lisa Hohensinn 3 AI in the public sector: fundamental operational questions and how to address them 45 Muiris MacCarthaigh, Stanley Simoes and Deepak P. 4 Towards a systematic understanding on the challenges of public procurement of artificial intelligence in the public sector 62 Keegan McBride. Colin van Noordt, Gianluca Misuraca and Gerhard Hammerschmid 5 Enhancing citizen service management through AI-enabled systems – a proposed AI readiness framework for the public sector 79 Alvina Lee, Venky Shankararaman and Ouh Eng Lieh 6 Measuring user-centricity in AI-enabled European public services: a proposal for enabling maturity models 97 Francesco Niglia and Luca Tangi PART II EXAMPLES AND CASE STUDIES OF AI IN PUBLIC MANAGEMENT 7 Application of artificial intelligence by Poland’s public administration 118 Bartosz Rzycki, David Duenas-Cid and Aleksandra Przegalińska 8 The effect of algorithmic tools on public value considerations in participatory processes: the case of regulations.gov 136 Sarah Giest, Alex Ingrams and Bram Klievink 9 Artificial intelligence and its regulation in representative institutions 149 Fotios Fitsilis and Patricia Gomes Rêgo de Almeida 10 Personalised public services powered by AI: the citizen digital twin approach 168 Aleksi Kopponen, Antti Hahto, Tero Villman, Petri Kettunen, Tommi Mikkonen and Matti Rossi 11 Enterprise data governance for artificial intelligence: implications from algorithmic jobseeker profiling applications in government 185 Luis Felipe Luna-Reyes and Teresa M. Harrison PART III FORWARD-LOOKING RESEARCH ON AI IN PUBLIC MANAGEMENT 12 Taking stock and looking ahead – developing a science for policy research agenda on the use and uptake of AI in public sector organisations in the EU 206 Luca Tangi, Peter Ulrich, Sven Schade and Marina Manzoni 13 Analysis of driving public values of AI initiatives in government in Europe 224 Colin van Noordt, Gianluca Misuraca and Ines Mergel 14 Challenges and design principles for the evaluation of productive AI systems in the public sector 243 Per Rådberg Nagbøl, Oliver Krancher and Oliver Müller 15 Trustworthy public sector AI: research progress and future agendas 260 Naomi Aoki, Melvin Tay and Masaru Yarime

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Book SynopsisWhat makes human cognition distinct from animal and artificial forms of intelligence?and how analogies play a crucial role in our unique abilities.In The Human Edge, cognitive scientist, poet, and translator Keith Holyoak takes a fresh look at what makes human intelligence special. His focus is analogy?the ability to see relational similarities between things that on the surface seem unalike. The book brings together a half century of research in cognitive, comparative, and developmental psychology, coupled with work in philosophy, law, education, linguistics, neuroscience, and artificial intelligence. Rather than simply examining analogy as an isolated human ability, Holyoak places it in the broader context of a trinity of special human capacities?analogy, language, and understanding the minds of other people. Each of these capacities relies on distinct neural circuitry in the human brain.Holyoak analyzes the similarities?and critical differences?between cognition in humans and in other intelligent animals, ranging from crows to chimpanzees. He also traces how relational thinking develops in children, emphasizing the distinctive advances that begin at about age three. Along the way, Holyoak paints a broad picture of how people use analogy in everyday life?to make jokes, to argue, to teach, to make moral judgments. He considers when an analogy counts as rational evidence?for or against a scientific hypothesis, or the judgment in a legal case. He also evaluates the most recent advances in artificial intelligence that have started to achieve complex tasks previously limited to humans while highlighting the distinctive aspects of human creativity.In a time of rapid technological change, with ominous portents for society, this book provides a timely reexamination of what really counts as the human edge.

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Book Synopsis'Compelling and innovative... Barry takes a story that has been done countless times before and makes it seem original' - Daily MailShe is the ultimate weapon. She once served us. Now she's got her own plans. Once we approached the aliens in peace... and they annihilated us. Now mankind has developed the ultimate killing machine, the Providence class of spaceship. With the ships' frightening speed, frightening intelligence and frightening weaponry, it's now the salamanders' turn to be annihilated... in their millions. The mismatched quartet of Talia, Gilly, Jolene and Anders are the crew on one of these destroyers. But with the ship's computers designed to outperform human decision-making in practically all areas, they are virtual bystanders. The Providence will take them to where the enemy are and she will dictate the strategy in any battle.The crew's only job role is to publicise their glorious war to a sceptical Earth. Social media and video clips are THEIR weapons in an endless charm offensive. THEIR chief enemy is not the space reptiles but each other, and boredom. But then everything changes. A message comes from base: the Providence is going into the Violet Zone, where there are no beacons and no communications with Earth. It is the heart of the enemy empire - and now the crew are left to wonder whether this is a mission of ultimate destruction or, more sinisterly, of ultimate self-destruction...PROVIDENCE is a dazzling, inventive, and thought-provoking new novel from the author of Lexicon.Trade ReviewPraise for Providence * : *Providence is such a blast you almost overlook how clever it is. Like Starship Troopers and 2001: A Sapce Odyssey, it's about the limit of human intelligence, the nature of humanity and the price we're prepared to pay for the survival of the species * Daily Mail *Providence is a light-hearted thriller with a superbly dark, existential sting in its tail * The Times *A quirky, character-driven commentary on the mechanisation of conflict and the sheer perversity of human nature * Financial Times *Compelling and innovative... Barry takes a story that has been done countless times before and makes it seem original * Daily Mail *[A] terrific sci-fi thriller * Publishers Weekly *PROVIDENCE is an absolute treat. Pulls the trick of being both irrepressibly old-school sci-fi and creepily relevant to the data-driven future * Austin Grossman, author of Soon I Will Be Invincible *An astonishing novel! PROVIDENCE is Philip K. Dick and William Gibson fueled by pure adrenaline (with a bit of Spielberg and Ridley Scott thrown in). The brilliant, unstoppable imagination of Max Barry glows on every page of this action-filled, yet emotionally resonant, tale. It will keep you riveted from first page till last. I read in one sitting and I guarantee you'll do the same * Jeffery Deaver, author of The Never Game *Action-packed and unsettling, PROVIDENCE is a sleekly-written parable about the absurdity of war--and the deeply human urge to destroy everything we don't understand, whether it comes from a distant planet or right here on Earth * Annalee Newitz, author of Autonomous and The Future of Another Timeline *I could not put PROVIDENCE down until I'd finished it in one thrilling sitting. This is science fiction at its best-a ship so believably alive and characters so determined, flawed, and compelling that you'll forget you're not also part of the crew * Peng Shepherd, author of The Book of M *PROVIDENCE'S greatest strength - apart from being terrifically entertaining - is its characters. (...) It's Barry's empathy for his characters, his willingness to dig deep into their foibles, that distinguishes PROVIDENCE from most of the golden-age science fiction that inspired it. * Locus *The meat of this is in its human interactions * SciFi Bulletin *Max Barry's excellent novel Providence * New Scientist *Providence is an extremely smart take on an entirely different genre ... page-turning anticipation which pays off beautifully in the final third * SFX *Providence is a meditation ... on the difference between programmed intelligence and the human version. Barry tells a fun and funny story that finds the perfect moments to break your heart in the best ways * Locus *Like Starship Troopers with added brain, this is such a blast you can overlook just how clever and thought-provoking it is * Daily Mail *

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Book Synopsis____________________What is AIQ? How does it work? Most importantly, how can it help us? Two leading data scientists offer an up-close and user-friendly look at artificial intelligence and how to harness its power for a better world. 'A positive and entertaining look at the great potential unlocked by marrying human creativity with powerful machines.' Steven D. Levitt, co-author of Freakonomics____________________Dozens of times per day, we all interact with intelligent machines that are constantly learning from the wealth of data now available to them. These machines, from smart phones to talking robots to self-driving cars, are remaking the world in the twenty first century in the same way that the Industrial Revolution remade the world in the nineteenth.AIQ is based on a simple premise: if you want to understand the modern world, then you have to know a little bit of the mathematical language spoken by intelligent machines. AIQ will teach you that language but in an unconventional way, anchored in stories rather than equations.Trade ReviewThere comes a time in the life of a subject when someone steps up and writes the book about it. AIQ explores the fascinating history of the ideas that drive this technology of the future and demystifies the core concepts behind it; the result is a positive and entertaining look at the great potential unlocked by marrying human creativity with powerful machines. -- Steven Levitt, bestselling co-author of FreakonomicsEntertaining and persuasive. The book’s goal is to explain how artificial intelligence delivers its incredible results, and Polson and Scott are like a pair of excitable mechanics lifting up the bonnet of a sports car. This is a passionate book, and it is a model of how to make data science accessible and exciting. -- James McConnachie * The Sunday Times *Grounding AI in tried-and-true methods makes it seem less alien: Computers are simply faster ways to solve familiar problems. Hence the book’s title, a portmanteau of AI and IQ—the point being that we need both. -- Sam Kean * Wall Street Journal *In an entertaining primer, two academic data scientists put the case for the defence on artificial intelligence, and show how we can harness its power for a better world. * The Times *At last, a book on the ideas behind AI and data science by people who really understand data. Cutting through the usual journalistic puff and myths, they clearly explain the underlying ideas behind the way that troughloads of data are being harnessed to build the algorithms that can carry out such extraordinary feats. But they are also clear about the limitations and potential risks of these algorithms, and the need for society to scrutinise and even regulate their use. A real page-turner, with fine stories and just enough detail: I learned a lot. -- David Spiegelhalter, Winton Professor of the Public Understanding of Risk, University of Cambridge

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book Synopsis.- A Flexible Knowledge Graph Error Detection Framework Combined with Semantic Information..- Session-level Normalization and Click-through Data Enhancement for Session-based Evaluation..- A Multi-Dimensional Early Warning Mechanism for Biological Invasions: A Case Study of Vespa Mandarinia..- DLP-FR: Learning Predictable Degradation for Robust Blind Face Restoration..- A joint relation extraction model utilizing message passing mechanism for feature fusion to alleviate FN problem from distant supervision..- Urban Traffic Management: A Predictive Approach Using Mobile Phone Data..- FedPSED: Federated Public Safety Emergency Detection Based on Adaptive Aggregation Strategy..- Evaluation of Retrieval-Augmented Generation: A Survey..- CMNet: Fast Time Series Forecasting Based on Hybrid Convolution-MLP Architecture..- A Domain Adaptive Based Reinforcement Learning Algorithm for Job Shop Scheduling Problems..- Large-scale Data Generation Using SWLBM on the Sunway TaihuLight Supercomputer and Subsequent Data Mining with Physics-Informed Neural Networks..- Enhancing Interaction Graph of Data Schema and Syntactic Structure with Pre-trained Language Model for Text-to-SQL..- Spatial Evolution Analysis of the Level of Digital Economy Development in China..- Predicting Calibrated Conversion Rate of Online Advertising Using a Multi-Task Mixture-of-Experts Calibration Model..- A Transformer-based Spatio-temporal Graph Neural Network for Anomaly Detection on Dynamic Graphs..- Morphological Semantic Ensemble Filtering of Massive Sentence Pairs for Neural Machine Translation..- Improving Event-Level Financial Sentiment Analysis with Retrieval-Augmented Multipath Chain-of-Thought Prompting..- POSRho:Efficient Spearman's Rho Calculation for Big Data..- A Fusion Tuning Method for Named Entity Recognition..- Design of AXI Bus-Based IP Core for Image Processing..- Automated Clinical Summary Generation Via Integrating Structured and Unstructured Data..- A Review on Deep Learning for Sequential Recommendation Systems: Key Technologies and Directions..- TrajUT: Intruder Trajectory Recovery on Utility Tunnel via Video Surveillance Systems..- Spatio-temporal Graph Fusion Network-based Multivariate Time Series Forecasting of Environmental Factors in Utility Tunnels..- Improving spatial co-location pattern mining with enhanced neighbor relationship measures..- PFG: Generation of Paper-Style Handwritten Formulas for Enhancing Handwritten Mathematical Expression Recognition.

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Book SynopsisA balanced and thought-provoking guide to all the big questions about AI and ethics Can computers understand morality? Can they respect privacy? And what can we do to make AI safe and fair? The artificial intelligence revolution has begun. Today, there are self-driving cars on our streets, autonomous weapons in our armies, robot surgeons in our hospitals and AI''s presence in our lives will only increase. Some see this as the dawn of a new era in innovation and ease; others are alarmed by its destructive potential. But one thing is clear: this is a technology like no other, one that raises profound questions about the very definitions of human intelligence and morality. In Moral AI, world-renowned researchers in moral psychology, philosophy, and artificial intelligence Jana Schaich Borg, Walter Sinnott-Armstrong and Vincent Conitzer tackle these thorny issues head-on. Writing lucidly and calmly, they lay out the recent advances in this still nascent field, peeling away the exaggeration and misleading arguments. Instead, they offer clear examinations of the moral concerns at the heart of AI programs, from racial equity to personal privacy, fake news to autonomous weaponry. Ultimately, they argue that artificial intelligence can be built and used safely and ethically, but that its potential cannot be achieved without careful reflection on the values we wish to imbue it with. This is an essential primer for any thinking person.Trade ReviewA philosopher, data scientist and computer scientist tackle the key ethical challenges of AI: safety, privacy, fairness, responsibility and how to inject human morality into AI. Practical and peppered with lively examples. This is a must-have as AI fundamentally changes all of our lives. Balanced, thoughtful and engaging -- Julian SavulescuCan we build and use AI ethically? I believe this book gives the best answer to this question: yes, but it is up to us, so we all have to make an effort. If you want to understand the impact of AI on our lives, and how to make it a positive one, you need to read it -- Francesca RossiTaking on the challenge of AI calls for perspectives grounded in multiple areas of expertise, and that is what Moral AI provides. . . A judicious and deeply-informed guide -- Peter RailtonMoral AI is a gracefully written and balanced book which should be required reading for all humans and generations of ChatGPTs. Written by an exceptional interdisciplinary team, it eschews hyperbole and brings what the Greeks would call phronesis - or practical wisdom - to the modern challenge of artificial intelligence -- Joseph J. FinsThe authors blend their expertise from diverse fields to provide fresh insights and feasible suggestions for balancing AI advancement with ethical considerations. The book is a timely and significant contribution, particularly relevant now. It serves as a guiding light, both for those who are getting started on the journey, and for those who are looking for a fresh perspective -- Abhishek Gupta

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Book SynopsisHow 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

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Book SynopsisProvocative, hopeful essays imagine a future that is not reduced to algorithms.What is human flourishing in an age of machine intelligence, when many claim that the world’s most complex problems can be reduced to narrow technical questions? Does more computing make us more intelligent, or simply more computationally powerful? We need not always resist reduction; our ability to simplify helps us interpret complicated situations. The trick is to know when and how to do so. Against Reduction offers a collection of provocative and illuminating essays that consider different ways of recognizing and addressing the reduction in our approach to artificial intelligence, and ultimately to ourselves.Inspired by a widely read manifesto by Joi Ito that called for embracing the diversity and irreducibility of the world, these essays offer persuasive and compelling variations on resisting reduction. Among other things, the writers draw on indigenous epistemology to

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Book SynopsisHow to empower people and communities with user-centric data ownership, transparent and accountable algorithms, and secure digital transaction systems.  Data is now central to the economy, government, and health systems—so why are data and the AI systems that interpret the data in the hands of so few people? Building the New Economy calls for us to reinvent the ways that data and artificial intelligence are used in civic and government systems. Arguing that we need to think about data as a new type of capital, the authors show that the use of data trusts and distributed ledgers can empower people and communities with user-centric data ownership, transparent and accountable algorithms, machine learning fairness principles and methodologies, and secure digital transaction systems.   It’s well known that social media generate disinformation and that mobile phone tracking apps threaten privacy. But these same technologies m

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Book SynopsisAn expert on mind considers how animals and smart machines measure up to human intelligence.Octopuses can open jars to get food, and chimpanzees can plan for the future. An IBM computer named Watson won on Jeopardy! and Alexa knows our favorite songs. But do animals and smart machines really have intelligence comparable to that of humans? In Bots and Beasts, Paul Thagard looks at how computers (bots) and animals measure up to the minds of people, offering the first systematic comparison of intelligence across machines, animals, and humans.Thagard explains that human intelligence is more than IQ and encompasses such features as problem solving, decision making, and creativity. He uses a checklist of twenty characteristics of human intelligence to evaluate the smartest machines--including Watson, AlphaZero, virtual assistants, and self-driving cars--and the most intelligent animals--including octopuses, dogs, dolphins, bees, and chimpanzees. Neither a

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Book SynopsisThis unique book on intelligence analysis covers several vital but often overlooked topics. It teaches the evidential and inferential issues involved in ''connecting the dots'' to draw defensible and persuasive conclusions from masses of evidence: from observations we make, or questions we ask, we generate alternative hypotheses as explanations or answers; we make use of our hypotheses to generate new lines of inquiry and discover new evidence; and we test the hypotheses with the discovered evidence. To facilitate understanding of these issues and enable the performance of complex analyses, the book introduces an intelligent analytical tool, called Disciple-CD. Readers will practice with Disciple-CD and learn how to formulate hypotheses; develop arguments that reduce complex hypotheses to simpler ones; collect evidence to evaluate the simplest hypotheses; and assess the relevance and the believability of evidence, which combine in complex ways to determine its inferential force and theTrade Review'Intelligence Analysis as Discovery of Evidence, Hypotheses, and Arguments is a valuable resource for those interested in improving intelligence analysis. It provides both prospective and current intelligence analysts with an easy to read and understand explanation of a complex process, along with suggestions for how to more effectively implement that process. The examples from intelligence analysis and comparison to similar practices in other fields such as medicine, law, and law enforcement help the reader better understand how the interplay of evidence, hypotheses, and arguments can develop in different contexts. In that sense, this book provides a wonderful platform for improving intelligence analysis by learning and employing best scientific reasoning practices.' Stephen Marrin, James Madison University, Virginia'This is an important work in several ways. The authors both help readers learn the basics and give advanced training in the craft of analytical reasoning by providing sophisticated tools to guide understanding of its strengths, its probabilistic nature, and its limitations. 'Deep' learning is what distinguishes experts from gifted amateurs; this book will help amateurs develop expert habits through guided learning and practice. I would not only recommend this book for students of intelligence, but also for students of law, journalism, and national security. The first several chapters should be mandatory reading for 'customers' and critics of intelligence, including policymakers, legislators, and professional journalists who are quick to ask 'why didn't you connect the dots?' while they themselves probably have no idea how difficult that may be.' Mark T. Clark, Director, National Security Studies, California State University, San Bernardino, and President, Association for the Study of Middle East and Africa (ASMEA)Table of Contents1. Intelligence analysis: 'connecting the dots'; 2. Marshaling thoughts and evidence for imaginative analysis; 3. Disciple-CD: a cognitive assistant for connecting the dots; 4. Evidence; 5. Divide and conquer: a necessary approach to complex analysis; 6. Assessing the believability of evidence; 7. Chains of custody; 8. Recurrent substance-blind combinations of evidence; 9. Major sources of uncertainty in masses of evidence; 10. Assessing and reporting uncertainty: some alternative methods; 11. Analytic bias; 12. Learning and reusing analytic expertise: beyond Disciple-CD.

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Book SynopsisThis book provides an in-depth exploration of human commonsense reasoning using formal logic in large-scale knowledge representation. Of special interest to artificial intelligence researchers, linguists, and cognitive and social psychologists, it serves as a foundation for humanlike artificial intelligence.Table of ContentsPart I. Commonsense Psychology: 1. Commonsense psychology and psychology; 2. Commonsense psychology and computers; 3. Formalizing commonsense psychology; 4. Commonsense psychology and language; Part II. Background Theories: 5. Eventualities and their structure; 6. Traditional set theory; 7. Substitution, typical elements, and instances; 8. Logic reified; 9. Functions and sequences; 10. Composite entities; 11. Defeasibility; 12. Scales; 13. Arithmetic; 14. Change of state; 15. Causality; 16. Time; 17. Event structure; 18. Space; 19. Persons; 20. Modality; Part III. Commonsense Psychology Theories: 21. Knowledge management; 22. Similarity comparisons; 23. Memory; 24. Envisioning; 25. Explanation; 26. Managing expectations; 27. Other agent reasoning; 28. Goals; 29. Goal themes; 30. Threats and threat detection; 31. Plans; 32. Goal management; 33. Execution envisionment; 34. Causes of failure; 35. Plan elements; 36. Planning modalities; 37. Planning goals; 38. Plan construction; 39. Plan adaptation; 40. Design; 41. Decisions; 42. Scheduling; 43. Monitoring; 44. Execution modalities; 45. Execution control; 46. Repetitive execution; 47. Mind-body interaction; 48. Observation of plan executions; 49. Emotions.

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