Artificial intelligence (AI) Books
Pragmatic Bookshelf Programming Machine Learning: From Coding to Deep
Book SynopsisYou've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain.
£36.57
Edward Elgar Publishing Ltd Advanced Introduction to Law and Artificial
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
£18.95
Manning Publications Deep Learning and the Game of Go
Book SynopsisIt's nearly impossible to build a competent Go-playing machine using conventional programming techniques, let alone have it win. By applying advanced AI techniques, in particular deep learning and reinforcement learning, users can train their Go-bot in the rules and tactics of the game. Deep Learning and the Game of Go opens up the world of deep learning and AI by teaching readers to build their own Go-playing machine. Key Features · Getting started with neural networks · Building your Go AI · Improving how your Go-bot plays and reacts Audience No deep learning experience required. All you need is high school level math and basic Python skills. This book even teaches you how to play Go! Author Bio Max Pumperla is a Data Scientist and Engineer specializing in Deep Learning at the artificial intelligence company skymind.ai. He is the cofounder of the Deep Learning platform aetros.com. Kevin Ferguson has 18 years of experience in distributed systems and data science. He is a data scientist at Honor, and has experience at companies such as Google and Meebo. Together, Max and Kevin are co-authors of betago, one of very few open source Go bots, developed in Python.
£39.59
Icon Books Introducing Artificial Intelligence: A Graphic
Book SynopsisArtificial Intelligence is no longer the stuff of science fiction.Half a century of research has resulted in machines capable of beating the best human chess players, and humanoid robots which are able to walk and interact with us. But how similar is this 'intelligence' to our own? Can machines really think? Is the mind just a complicated computer program? Addressing major issues in the design of intelligent machines, such as consciousness and environment, and covering everything from the influential groundwork of Alan Turing to the cutting-edge robots of today, Introducing Artificial Intelligence is a uniquely accessible illustrated introduction to this fascinating area of science.
£7.99
Skyhorse Publishing Heart of the Machine: Our Future in a World of
Book SynopsisFor Readers of Ray Kurzweil and Michio Kaku, a New Look at the Cutting Edge of Artificial Intelligence Imagine a robotic stuffed animal that can read and respond to a child’s emotional state, a commercial that can recognize and change based on a customer’s facial expression, or a company that can actually create feelings as though a person were experiencing them naturally. Heart of the Machine explores the next giant step in the relationship between humans and technology: the ability of computers to recognize, respond to, and even replicate emotions. Computers have long been integral to our lives, and their advances continue at an exponential rate. Many believe that artificial intelligence equal or superior to human intelligence will happen in the not-too-distance future; some even think machine consciousness will follow. Futurist Richard Yonck argues that emotion, the first, most basic, and most natural form of communication, is at the heart of how we will soon work with and use computers. Instilling emotions into computers is the next leap in our centuries-old obsession with creating machines that replicate humans. But for every benefit this progress may bring to our lives, there is a possible pitfall. Emotion recognition could lead to advanced surveillance, and the same technology that can manipulate our feelings could become a method of mass control. And, as shown in movies like Her and Ex Machina, our society already holds a deep-seated anxiety about what might happen if machines could actually feel and break free from our control. Heart of the Machine is an exploration of the new and inevitable ways in which mankind and technology will interact. The paperback edition has a new foreword by Rana el Kaliouby, PhD, a pioneer in artificial emotional intelligence, as well as the cofounder and CEO of Affectiva, the acclaimed AI startup spun off from the MIT Media Lab.Trade Review"Yonck is a sure-footed guide and is not without a sense of humor . . . [He] provides a compelling and thorough history of the interaction between our emotional lives and our technology." —Ray Kurzweil, The New York Times Book Review "A fascinating, and sometimes disturbing, look at a rapidly approaching future where smart machines understand and manipulate our emotions—and ultimately bond with us in ways that blur the line between ourselves and our technology." —Martin Ford, New York Times bestselling author of Rise of the Robots: Technology and the Threat of a Jobless Future “Richard Yonck’s Heart of the Machine is a fascinating speculation on the near- and far-term significance of emotions for user interfaces, machine-mediated communication between humans, and what technology and humans may become.” —Vernor Vinge, computer scientist and Hugo Award–winning author of Rainbows End “Your world is about to change in shocking and amazing ways. The line between machines and humanity is blurring giving us a strange and beautiful tomorrow. Yonck takes us on a journey through this world from the science and technology of today and into the possibilities and perils that lay just over the horizon. If you want to catch a glimpse of the future open this book.” —Brian David Johnson, former chief futurist at Intel and founder of the 21st Century Robot Project "[Yonck] makes a compelling argument for why affective computing (technology that can read, interpret, replicate, and experience emotions and use those abilities to influence us) is the key to AI and the heart of how we will work with computers. . . . an engaging read." —Library Journal “Very important for any decision-maker and a must-read for corporations for planning their road map. It is also recommended to everyone who is curious enough to understand the future. Even the very near future.” —Yoram Levanon, chief science officer at Beyond Verbal Communication, Ltd. "How we interact with technology is changing: it is becoming more relational and conversational. Yonck makes a very strong case why our devices and advanced AI systems need to have emotional intelligence, specifically the ability to sense human emotions and adapt accordingly. This book highlights key considerations both for academic researchers as well as business leaders looking for commercial applications of AI." —Rana el Kaliouby, cofounder and CEO of Affectiva "By using the futurist’s most valuable communications tool—the scenario—to introduce his chapters, Yonck moves between anecdotes from research in affective computing and AI/robotics to speculative scenarios, all with the even hand of a skilled storyteller.” —Cynthia G. Wagner, consulting editor at Foresight Signals, former editor of The Futurist magazine
£15.24
Harvard Business Review Press HBR's 10 Must Reads on AI, Analytics, and the New
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
£16.14
Vintage Publishing The AI Con
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£15.29
John Wiley & Sons Network Models in Finance Expanding the Tools for
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£60.00
Pearson Education Engineering AI Systems
Book SynopsisDr. Len Bass is a seasoned researcher with over 30 years in software architecture and more than a decade in DevOps. He has been teaching DevOps to graduate students for seven years and is the author of a bestselling book on software architecture, along with three books on DevOps. Dr. Qinghua Lu is a principal research scientist at CSIRO's Data61, leading the Software Engineering for AI and Responsible AI science teams. She is a coauthor of Responsible AI: Best Practices for Creating Trustworthy AI Systems (Addison-Wesley, 2024). Prof. Dr. Ingo Weber is a professor at the Technical University of Munich and Director of Digital Transformation and ICT Infrastructure at Fraunhofer-Gesellschaft. He has written numerous publications and textbooks, including DevOps: A Software Architect's Perspective and Architecture for Blockchain Applications. Dr. Liming Zhu is a research director at CSIRO's Data61 and i
£35.99
Stripe Matter Inc An Oral History of the Scaling Era
Book SynopsisAn inside view of the AI revolution, from the people and companies making it happen. How did we build large language models? How do they think, if they think? Will we be able to make them share our goalsand what happens if we can't?In a series of in-depth interviews with leading AI researchers and company foundersincluding Anthropic CEO Dario Amodei, DeepMind cofounder Demis Hassabis, OpenAI cofounder Ilya Sutskever, MIRI cofounder Eliezer Yudkowsky, and Meta CEO Mark ZuckerbergDwarkesh Patel provides the first comprehensive and contemporary portrait of the technology that is transforming our world. Drawn from his interviews on the Dwarkesh Podcast, these curated excerpts range from the technical details of how LLMs work to the economic, philosophical, and safety considerations of creating artificial general intelligenceAI that can do anything humans can, and more. Patel's conversations cut through the noise and the hype to explore the topics compelling those at the forefront of the field: the power of scaling, the nature of training, the potential for misalignment, the inputs required to achieve AGI, and the economic and social ramifications of superintelligence. At a time of great promise and great uncertainty, An Oral History of the Scaling Era offers readers unprecedented insight into a transformative moment in the AI's developmentand a revealing vision of what comes next.
£21.86
John Wiley & Sons Inc Computational Intelligence
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.
£72.86
MIT Press Ltd Probabilistic Robotics
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£85.50
Oxford University Press Superintelligence Paths Dangers Strategies
Book SynopsisThis seminal book injects the topic of superintelligence into the academic and popular mainstream. What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? In a tour de force of analytic thinking, Bostrom lays a foundation for understanding the future of humanity and intelligent life.Trade ReviewWorth reading. * Elon Musk, Founder of SpaceX and Tesla *I highly recommend this book * Bill Gates *very deep ... every paragraph has like six ideas embedded within it. * Nate Silver *Nick Bostrom makes a persuasive case that the future impact of AI is perhaps the most important issue the human race has ever faced. Instead of passively drifting, we need to steer a course. Superintelligence charts the submerged rocks of the future with unprecedented detail. It marks the beginning of a new era * Stuart Russell, Professor of Computer Science, University of California, Berkley *Those disposed to dismiss an 'AI takeover' as science fiction may think again after reading this original and well-argued book * Martin Rees, Past President, Royal Society *This superb analysis by one of the worlds clearest thinkers tackles one of humanitys greatest challenges: if future superhuman artificial intelligence becomes the biggest event in human history, then how can we ensure that it doesnt become the last? * Max Tegmark, Professor of Physics, MIT *Terribly important ... groundbreaking... extraordinary sagacity and clarity, enabling him to combine his wide-ranging knowledge over an impressively broad spectrum of disciplines - engineering, natural sciences, medicine, social sciences and philosophy - into a comprehensible whole... If this book gets the reception that it deserves, it may turn out the most important alarm bell since Rachel Carson's Silent Spring from 1962, or ever * Olle Haggstrom, Professor of Mathematical Statistics *Valuable. The implications of introducing a second intelligent species onto Earth are far-reaching enough to deserve hard thinking * The Economist *There is no doubting the force of [Bostroms] arguments the problem is a research challenge worthy of the next generations best mathematical talent. Human civilisation is at stake * Financial Times *His book Superintelligence: Paths, Dangers, Strategies became an improbable bestseller in 2014 * Alex Massie, Times (Scotland) *Ein Text so nüchtern und cool, so angstfrei und dadurch umso erregender, dass danach das, was bisher vor allem Filme durchgespielt haben, auf einmal höchst plausibel erscheint. A text so sober and cool, so fearless and thus all the more exciting that what has until now mostly been acted through in films, all of a sudden appears most plausible afterwards. (translated from German) * Georg Diez, DER SPIEGEL *Worth reading.... We need to be super careful with AI. Potentially more dangerous than nukes * Elon Musk, Founder of SpaceX and Tesla *A damn hard read * Sunday Telegraph *I recommend Superintelligence by Nick Bostrom as an excellent book on this topic * Jolyon Brown, Linux Format *Every intelligent person should read it. * Nils Nilsson, Artificial Intelligence Pioneer, Stanford University *An intriguing mix of analytic philosophy, computer science and cutting-edge science fiction, Nick Bostrom's Superintelligence is required reading for anyone seeking to make sense of the recent surge of interest in artificial intelligence (AI). * Colin Garvey, Icon *Table of ContentsPreface ; 1. Past Developments and Present Capabilities ; 2. Roads to Superintelligence ; 3. Forms of Superintelligence ; 4. Singularity Dynamics ; 5. Decisive Strategic Advantage ; 6. Intellectual Superpowers ; 7. The Superintelligent Will ; 8. Is the Default Outcome Doom? ; 9. The Control Problem ; 10. Oracles, Genies, Sovereigns, Tools ; 11. Multipolar Scenarios ; 12. Acquiring Values ; 13. Design Choices ; 14. The Strategic Picture ; 15. Nut-Cutting Time
£20.24
MIT Press Machine Agency
Book SynopsisAn accessible philosophy of technology textbook intended for interested students who don''t necessarily have a background in philosophy of science--
£38.70
Ebury Publishing Digital Dharma
Book SynopsisAI has the potential to help us create a more peaceful, just, sustainable, healthy and joyful world. Digital Dharma shows you a path.' Sam Altman, CEO of OpenAIIn a world captivated yet bewildered by artificial intelligence, spiritual icon Deepak Chopra explores AI''s untapped potential to unlock the mystery of consciousness, positioning AI not as a threat, but as a powerful catalyst for personal and spiritual growth.Digital Dharma shows how the most popular, freely available chatbots can serve as guides through every level of human potential survival and safety, emotional connection, self-worth, abundance, creativity, wisdom and the infinite possibilities of cosmic consciousness.Featuring personal assessments and practical exercises, Deepak Chopra invites you to explore a relationship with AI not merely as a technological tool, but as a partner in shaping a future where human potential solves pressing global issues and empowers
£15.29
Edinburgh University Press Minds and Computers
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
£26.99
Oxford University Press How To Think About AI
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£9.89
Barcharts, Inc A.I. Machine Learning
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.
£9.45
Jones and Bartlett Publishers, Inc Programming Game AI By Example
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.
£37.04
Penguin Putnam Inc How to Create a Mind
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£13.88
Dover Publications Inc. Pierce J Introduction to Information Theory
Book SynopsisCovers encoding and binary digits, entropy, language and meaning, efficient encoding and the noisy channel, and explores ways in which information theory relates to physics, cybernetics, psychology, and art. 1980 edition.
£16.64
Legend Press Ltd The Fourth Education Revolution: Will Artificial
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£13.49
Random House USA Inc Life 30 Being Human in the Age of Artificial
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?
£17.10
Pearson Education (US) Python for Programmers
Book Synopsis Paul Deitel, CEO and Chief Technical Officer of Deitel & Associates, Inc., is a graduate of MIT, where he studied Information Technology. Through Deitel & Associates, Inc., he has delivered hundreds of programming courses worldwide to clients, including Cisco, IBM, Siemens, Sun Microsystems, Dell, Fidelity, NASA at the Kennedy Space Center, the National Severe Storm Laboratory, White Sands Missile Range, Rogue Wave Software, Boeing, SunGard Higher Education, Nortel Networks, Puma, iRobot, Invensys and many more. He and his co-author, Dr. Harvey M. Deitel, are the world's best-selling programming-language textbook/professional book/video authors. Dr. Harvey Deitel, Chairman and Chief Strategy Officer of Deitel & Associates, Inc., has over 50 years of experience in the computer field. Dr. Deitel earned B.S. and M.S. degrees in Electrical Engineering from MIT and a Ph.D. in Mathematics from Boston University. He has extensive college teaching experienTrade Review“The chapters are clearly written with detailed explanations of the example code. The modular structure, wide range of contemporary data science topics, and code in companion Jupyter notebooks make this a fantastic resource for readers of a variety of backgrounds. Fabulous Big Data chapter—it covers all of the relevant programs and platforms. Great Watson chapter! The chapter provides a great overview of the Watson applications. Also, your translation examples are great because they provide an ‘instant reward’—it’s very satisfying to implement a task and receive results so quickly. Machine Learning is a huge topic, and the chapter serves as a great introduction. I loved the California housing data example—very relevant for business analytics. The chapter was visually stunning.” —Alison Sanchez, Assistant Professor in Economics, University of San Diego “A great introduction to Big Data concepts, notably Hadoop, Spark, and IoT. The examples are extremely realistic and practical. The authors do an excellent job of combining programming and data science topics. The material is presented in digestible sections accompanied by engaging interactive examples. Nearly all concepts are accompanied by a worked-out example. A comprehensive overview of object-oriented programming in Python—the use of card image graphics is sure to engage the reader.” —Garrett Dancik, Eastern Connecticut State University “Covers some of the most modern Python syntax approaches and introduces community standards for style and documentation. The machine learning chapter does a great job of walking people through the boilerplate code needed for ML in Python. The case studies accomplish this really well. The later examples are so visual. Many of the model evaluation tasks make for really good programming practice. I can see readers feeling really excited about playing with the animations.” —Elizabeth Wickes, Lecturer, School of Information Sciences, University of Illinois at Urbana-Champaign “An engaging, highly accessible book that will foster curiosity and motivate beginning data scientists to develop essential foundations in Python programming, statistics, data manipulation, working with APIs, data visualization, machine learning, cloud computing, and more. Great walkthrough of the Twitter APIs—sentiment analysis piece is very useful. I’ve taken several classes that cover natural language processing and this is the first time the tools and concepts have been explained so clearly. I appreciate the discussion of serialization with JSON and pickling and when to use one or the other—with an emphasis on using JSON over pickle—good to know there’s a better, safer way!” —Jamie Whitacre, Data Science Consultant “For a while, I have been looking for a book in Data Science using Python that would cover the most relevant technologies. Well, my search is over. A must-have book for any practitioner of this field. The machine learning chapter is a real winner!! The dynamic visualization is fantastic.” —Ramon Mata-Toledo, Professor, James Madison University “I like the new combination of topics from computer science, data science, and stats. This is important for building data science programs that are more than just cobbling together math and computer science courses. A book like this may help facilitate expanding our offerings and using Python as a bridge for computer and data science topics. For a data science program that focuses on a single language (mostly), I think Python is probably the way to go.” —Lance Bryant, Shippensburg University “You’ll develop applications using industry standard libraries and cloud computing services.” —Daniel Chen, Data Scientist, Lander Analytics “Great introduction to Python! This book has my strongest recommendation both as an introduction to Python as well as Data Science.” —Shyamal Mitra, Senior Lecturer, University of Texas “IBM Watson is an exciting chapter. The code examples put together a lot of Watson services in a really nifty example.” —Daniel Chen, Data Scientist, Lander Analytics “Fun, engaging real-world examples will encourage readers to conduct meaningful data analyses. Provides many of the best explanations of data science concepts I’ve encountered. Introduces the most useful starter machine learning models—does a good job explaining how to choose the best model and what ‘the best’ means. Great overview of all the big data technologies with relevant examples.” —Jamie Whitacre, Data Science Consultant “A great introduction to deep learning.” —Alison Sanchez, University of San Diego “The best designed Intro to Data Science/Python book I have seen.” —Roland DePratti, Central Connecticut State University “I like the new combination of topics from computer science, data science, and stats.” —Lance Bryant, Shippensburg University “The book’s applied approach should engage readers. A fantastic job providing background on various machine learning concepts without burdening the users with too many mathematical details.” —Garrett Dancik, Assoc. Prof. of Computer Science/Bioinformatics, Eastern Connecticut State University “Helps readers leverage the large number of existing libraries to accomplish tasks with minimal code. Concepts are accompanied by rich Python examples that readers can adapt to implement their own solutions to data science problems. I like that cloud services are used.” —David Koop, Assistant Professor, U-Mass Dartmouth “I enjoyed the OOP chapter—doctest unit testing is nice because you can have the test in the actual docstring so things are traveling together. The line-by-line explanations of the static and dynamic visualizations of the die rolling example are just great.” —Daniel Chen, Data Scientist, Lander Analytics “A lucid exposition of the fundamentals of Python and Data Science. Thanks for pointing out seeding the random number generator for reproducibility. I like the use of dictionary and set comprehensions for succinct programming. ‘List vs. Array Performance: Introducing %timeit’ is convincing on why one should use ndarrays. Good defensive programming. Great section on Pandas Series and DataFrames—one of the clearest expositions that I have seen. The section on data wrangling is excellent. Natural Language Processing is an excellent chapter! I learned a tremendous amount going through it.” —Shyamal Mitra, Senior Lecturer, University of Texas “I like the discussion of exceptions and tracebacks. I really liked the Data Mining Twitter chapter; it focused on a real data source and brought in a lot of techniques for analysis (e.g., visualization, NLP). I like that the Python modules helped hide some of the complexity. Word clouds look cool.” —David Koop, Assistant Professor, U-Mass Dartmouth “I love the book! The examples are definitely a high point.” —Dr. Irene Bruno, George Mason University “I was very excited to see this book. I like its focus on data science and a general purpose language for writing useful data science programs. The data science portion distinguishes this book from most other introductory Python books.” —Dr. Harvey Siy, University of Nebraska at Omaha “I’ve learned a lot in this review process, discovering the exciting field of AI. I’ve liked the Deep Learning chapter, which has left me amazed with the things that have already been achieved in this field.” —José Antonio González Seco, Consultant “An impressive hands-on approach to programming meant for exploration and experimentation.” —Elizabeth Wickes, Lecturer, School of Information Sciences, University of Illinois at Urbana-Champaign “I was impressed at how easy it was to get started with NLP using Python. A meaningful overview of deep learning concepts, using Keras. I like the streaming example.” —David Koop, Assistant Professor, U-Mass Dartmouth “Really like the use of f-strings, instead of the older string-formatting methods. Seeing how easy TextBlob is compared to base NLTK was great. I never made word clouds with shapes before, but I can see this being a motivating example for people getting started with NLP. I’m enjoying the case-study chapters in the latter parts of the book. They are really practical. I really enjoyed working through all the Big Data examples, especially the IoT ones.” —Daniel Chen, Data Scientist, Lander Analytics “I really liked the live IPython input-output. The thing that I like most about this product is that it is a Deitel & Deitel book (I’m a big fan) that covers Python.” —Dr. Mark Pauley, University of Nebraska at Omaha Table of ContentsPreface xviiBefore You Begin xxxiiiChapter 1: Introduction to Computers and Python 11.1 Introduction 21.2 A Quick Review of Object Technology Basics 31.3 Python 51.4 It’s the Libraries! 71.5 Test-Drives: Using IPython and Jupyter Notebooks 91.6 The Cloud and the Internet of Things 161.7 How Big Is Big Data? 171.8 Case Study—A Big-Data Mobile Application 241.9 Intro to Data Science: Artificial Intelligence—at the Intersection of CS and Data Science 261.10 Wrap-Up 29Chapter 2: Introduction to Python Programming 312.1 Introduction 322.2 Variables and Assignment Statements 322.3 Arithmetic 332.4 Function print and an Intro to Single- and Double-Quoted Strings 362.5 Triple-Quoted Strings 382.6 Getting Input from the User 392.7 Decision Making: The if Statement and Comparison Operators 412.8 Objects and Dynamic Typing 452.9 Intro to Data Science: Basic Descriptive Statistics 462.10 Wrap-Up 48Chapter 3: Control Statements 493.1 Introduction 503.2 Control Statements 503.3 if Statement 513.4 if...else and if...elif...else Statements 523.5 while Statement 553.6 for Statement 553.7 Augmented Assignments 573.8 Sequence-Controlled Iteration; Formatted Strings 583.9 Sentinel-Controlled Iteration 593.10 Built-In Function range: A Deeper Look 603.11 Using Type Decimal for Monetary Amounts 613.12 break and continue Statements 643.13 Boolean Operators and, or and not 653.14 Intro to Data Science: Measures of Central Tendency—Mean, Median and Mode 673.15 Wrap-Up 69Chapter 4: Functions 714.1 Introduction 724.2 Defining Functions 724.3 Functions with Multiple Parameters 754.4 Random-Number Generation 764.5 Case Study: A Game of Chance 784.6 Python Standard Library 814.7 math Module Functions 824.8 Using IPython Tab Completion for Discovery 834.9 Default Parameter Values 854.10 Keyword Arguments 854.11 Arbitrary Argument Lists 864.12 Methods: Functions That Belong to Objects 874.13 Scope Rules 874.14 import: A Deeper Look 894.15 Passing Arguments to Functions: A Deeper Look 904.16 Recursion 934.17 Functional-Style Programming 954.18 Intro to Data Science: Measures of Dispersion 974.19 Wrap-Up 98Chapter 5: Sequences: Lists and Tuples 1015.1 Introduction 1025.2 Lists 1025.3 Tuples 1065.4 Unpacking Sequences 1085.5 Sequence Slicing 1105.6 del Statement 1125.7 Passing Lists to Functions 1135.8 Sorting Lists 1155.9 Searching Sequences 1165.10 Other List Methods 1175.11 Simulating Stacks with Lists 1195.12 List Comprehensions 1205.13 Generator Expressions 1215.14 Filter, Map and Reduce 1225.15 Other Sequence Processing Functions 1245.16 Two-Dimensional Lists 1265.17 Intro to Data Science: Simulation and Static Visualizations 1285.18 Wrap-Up 135Chapter 6: Dictionaries and Sets 1376.1 Introduction 1386.2 Dictionaries 1386.3 Sets 1476.4 Intro to Data Science: Dynamic Visualizations 1526.5 Wrap-Up 158Chapter 7: Array-Oriented Programming with NumPy 1597.1 Introduction 1607.2 Creating arrays from Existing Data 1607.3 array Attributes 1617.4 Filling arrays with Specific Values 1637.5 Creating arrays from Ranges 1647.6 List vs. array Performance: Introducing %timeit 1657.7 array Operators 1677.8 NumPy Calculation Methods 1697.9 Universal Functions 1707.10 Indexing and Slicing 1717.11 Views: Shallow Copies 1737.12 Deep Copies 1747.13 Reshaping and Transposing 1757.14 Intro to Data Science: pandas Series and DataFrames 1777.15 Wrap-Up 189Chapter 8: Strings: A Deeper Look 1918.1 Introduction 1928.2 Formatting Strings 1938.3 Concatenating and Repeating Strings 1968.4 Stripping Whitespace from Strings 1978.5 Changing Character Case 1978.6 Comparison Operators for Strings 1988.7 Searching for Substrings 1988.8 Replacing Substrings 1998.9 Splitting and Joining Strings 2008.10 Characters and Character-Testing Methods 2028.11 Raw Strings 2038.12 Introduction to Regular Expressions 2038.13 Intro to Data Science: Pandas, Regular Expressions and Data Munging 2108.14 Wrap-Up 214Chapter 9: Files and Exceptions 2179.1 Introduction 2189.2 Files 2199.3 Text-File Processing 2199.4 Updating Text Files 2229.5 Serialization with JSON 2239.6 Focus on Security: pickle Serialization and Deserialization 2269.7 Additional Notes Regarding Files 2269.8 Handling Exceptions 2279.9 finally Clause 2319.10 Explicitly Raising an Exception 2339.11 (Optional) Stack Unwinding and Tracebacks 2339.12 Intro to Data Science: Working with CSV Files 2359.13 Wrap-Up 241Chapter 10: Object-Oriented Programming 24310.1 Introduction 24410.2 Custom Class Account 24610.3 Controlling Access to Attributes 24910.4 Properties for Data Access 25010.5 Simulating “Private” Attributes 25610.6 Case Study: Card Shuffling and Dealing Simulation 25810.7 Inheritance: Base Classes and Subclasses 26610.8 Building an Inheritance Hierarchy; Introducing Polymorphism 26710.9 Duck Typing and Polymorphism 27510.10 Operator Overloading 27610.11 Exception Class Hierarchy and Custom Exceptions 27910.12 Named Tuples 28010.13 A Brief Intro to Python 3.7’s New Data Classes 28110.14 Unit Testing with Docstrings and doctest 28710.15 Namespaces and Scopes 29010.16 Intro to Data Science: Time Series and Simple Linear Regression 29310.17 Wrap-Up 301Chapter 11: Natural Language Processing (NLP) 30311.1 Introduction 30411.2 TextBlob 30511.3 Visualizing Word Frequencies with Bar Charts and Word Clouds 31911.4 Readability Assessment with Textatistic 32411.5 Named Entity Recognition with spaCy 32611.6 Similarity Detection with spaCy 32711.7 Other NLP Libraries and Tools 32811.8 Machine Learning and Deep Learning Natural Language Applications 32811.9 Natural Language Datasets 32911.10 Wrap-Up 330Chapter 12: Data Mining Twitter 33112.1 Introduction 33212.2 Overview of the Twitter APIs 33412.3 Creating a Twitter Account 33512.4 Getting Twitter Credentials—Creating an App 33512.5 What’s in a Tweet? 33712.6 Tweepy 34012.7 Authenticating with Twitter Via Tweepy 34112.8 Getting Information About a Twitter Account 34212.9 Introduction to Tweepy Cursors: Getting an Account’s Followers and Friends 34412.10 Searching Recent Tweets 34712.11 Spotting Trends: Twitter Trends API 34912.12 Cleaning/Preprocessing Tweets for Analysis 35312.13 Twitter Streaming API 35412.14 Tweet Sentiment Analysis 35912.15 Geocoding and Mapping 36212.16 Ways to Store Tweets 37012.17 Twitter and Time Series 37012.18 Wrap-Up 371Chapter 13: IBM Watson and Cognitive Computing 37313.1 Introduction: IBM Watson and Cognitive Computing 37413.2 IBM Cloud Account and Cloud Console 37513.3 Watson Services 37613.4 Additional Services and Tools 37913.5 Watson Developer Cloud Python SDK 38113.6 Case Study: Traveler’s Companion Translation App 38113.7 Watson Resources 39413.8 Wrap-Up 395Chapter 14: Machine Learning: Classification, Regression and Clustering 39714.1 Introduction to Machine Learning 39814.2 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1 40314.3 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2 41314.4 Case Study: Time Series and Simple Linear Regression 42014.5 Case Study: Multiple Linear Regression with the California Housing Dataset 42514.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction 43814.7 Case Study: Unsupervised Machine Learning, Part 2—k-Means Clustering 44214.8 Wrap-Up 455Chapter 15: Deep Learning 45715.1 Introduction 45815.2 Keras Built-In Datasets 46115.3 Custom Anaconda Environments 46215.4 Neural Networks 46315.5 Tensors 46515.6 Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset 46715.7 Visualizing Neural Network Training with TensorBoard 48615.8 ConvnetJS: Browser-Based Deep-Learning Training and Visualization 48915.9 Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset 48915.10 Tuning Deep Learning Models 49715.11 Convnet Models Pretrained on ImageNet 49815.12 Wrap-Up 499Chapter 16: Big Data: Hadoop, Spark, NoSQL and IoT 50116.1 Introduction 50216.2 Relational Databases and Structured Query Language (SQL) 50616.3 NoSQL and NewSQL Big-Data Databases: A Brief Tour 51716.4 Case Study: A MongoDB JSON Document Database 52016.5 Hadoop 53016.6 Spark 54116.7 Spark Streaming: Counting Twitter Hashtags Using the pyspark-notebook Docker Stack 55116.8 Internet of Things and Dashboards 56016.9 Wrap-Up 571Index 573
£42.74
MIT Press The New Fire
Book SynopsisAI is revolutionizing the world. Here’s how democracies can come out on top.Artificial intelligence is revolutionizing the modern world. It is ubiquitous—in our homes and offices, in the present and most certainly in the future. Today, we encounter AI as our distant ancestors once encountered fire. If we manage AI well, it will become a force for good, lighting the way to many transformative inventions. If we deploy it thoughtlessly, it will advance beyond our control. If we wield it for destruction, it will fan the flames of a new kind of war, one that holds democracy in the balance. As AI policy experts Ben Buchanan and Andrew Imbrie show in The New Fire, few choices are more urgent—or more fascinating—than how we harness this technology and for what purpose. The new fire has three sparks: data, algorithms, and computing power. These components fuel viral disinformation campaigns, new hacking tools, and military weapons that
£21.60
HarperCollins Publishers Autonomy The Quest to Build the Driverless Car
Book Synopsis'A fascinating hybrid. Part freewheeling history of the rise of the modern autonomous vehicle, part intimate memoir from an insider who was on the front lines for much of that history, Autonomy will more than bring readers up to speed on one of today's most closely watched technologies' Brian Merchant, author of The One DeviceFrom the ultimate insider a former General Motors executive and current advisor to the Google Self-Driving Car project comes the definitive story of the race between Google, Tesla and Uber to create the driverless car.We stand on the brink of a technological revolution. In the near future, most of us will not own automobiles, but will travel instead in driverless electric vehicles summoned at the touch of an app. We will be liberated from driving, so that the time we spend in cars can be put to more productive use. We will prevent more than 90 percent of car crashes, provide freedom of mobility to the elderly and disabled and decrease our dependence on fossil fuTrade Review‘If you want a glimpse of how the future is being engineered today, there is no better book’ Jeffrey Sachs, author of The End of Poverty ‘An entertaining and accessible account of the biggest disruption in the history of the auto industry, and indeed the entire transportation industry’ Rick Wagoner, Former Chairman and Chief Executive Officer, General Motors ‘An insider’s view into the thrilling who-will-win-it race to invent and control driverless cars – and the radically altered future that will follow in their wake’ Robin Chase, Cofounder, Zipcar, and author of Peers Inc ‘A rich and entertaining insider’s account . . . required reading for anyone interested in the future of mobility’ Roger Martin, coauthor of Playing to Win ‘Tells the remarkable story of innovators who are changing transportation as we know it’ Clayton M. Christensen, author of The Innovator’s Dilemma ‘Takes us inside the auto industry as it is today – and what may be a very different industry tomorrow’ Daniel Yergin, author of The Prize and The Quest ‘Essential reading’ The Times
£9.49
Oxford University Press Inc Deceitful Media
Book SynopsisArtificial intelligence (AI) is often discussed as something extraordinary, a dream--or a nightmare--that awakens metaphysical questions on human life. Yet far from a distant technology of the future, the true power of AI lies in its subtle revolution of ordinary life. From voice assistants like Siri to natural language processors, AI technologies use cultural biases and modern psychology to fit specific characteristics of how users perceive and navigate the external world, thereby projecting the illusion of intelligence. Integrating media studies, science and technology studies, and social psychology, Deceitful Media examines the rise of artificial intelligence throughout history and exposes the very human fallacies behind this technology. Focusing specifically on communicative AIs, Natale argues that what we call AI is not a form of intelligence but rather a reflection of the human user. Using the term banal deception, he reveals that deception forms the basis of all human-computer interactions rooted in AI technologies, as technologies like voice assistants utilize the dynamics of projection and stereotyping as a means for aligning with our existing habits and social conventions. By exploiting the human instinct to connect, AI reveals our collective vulnerabilities to deception, showing that what machines are primarily changing is not other technology but ourselves as humans. Deceitful Media illustrates how AI has continued a tradition of technologies that mobilize our liability to deception and shows that only by better understanding our vulnerabilities to deception can we become more sophisticated consumers of interactive media.Trade Reviewa real breath of fresh air ... fundamental reading for an understanding of AI as a socio-material phenomenon * Domenico Napolitano, Prometheus *Deceitful Media makes a compelling case that the development of artificial intelligence is inextricably woven together with fallacies of human perception. Analyzing archival documents from the 1950s onward, Simone Natale demonstrates the prevalence of what he calls 'banal deception,' the everyday taken-for-granted interactions that attribute human-equivalent intelligence to algorithmic processes that in themselves are quite different. A remarkable achievement, this accessible and well-written book is a 'must-read' for media scholars, cultural critics, and anyone interested in the significance of artificial intelligence for our time. * N. Katherine Hayles, author of Postprint: Books and Becoming Computational *From the time of Alan Turing's Game of Imitation, the benchmark of machine intelligence has been deceptive communicative behavior. In Deceitful Media, Simone Natale provides a decisive and revealing analysis of the history, significance, and social consequences of deception in artificial intelligence, demonstrating how and why deceit is not a bug to be fixed but a defining feature of both the theory and practice of AI. * David J. Gunkel, Northern Illinois University *A fundamental fear surrounding artificial intelligence is that it will one day become a technology of deception. As Simone Natale informs us in Deceitful Media, that day is already here. However, such deception is not the malicious kind of science fiction; rather, the deceit of AI is one enacted in our minds as they encounter technologies carefully crafted to our social nature. By situating AI within the context of media and communication theory, Natale dispels the hype surrounding AI as a technology, replacing it with a theoretical lens informed by the seemingly mundane elements of our ongoing interactions with AI as forms of media. As a result, Deceitful Media provides us with not only a new way to think about AI, but also a more grounded approach to assessing its impact for ourselves and society. * Andrea Guzman, Northern Illinois University *A remarkable critical history of the artifice central to artificial intelligence. Natale has peered beyond the scandalously uncanny valleys, the many muddily mediated human-machine thought experiments, and scurrilous bids for grants and investor capital to uncover the dark heart of artificial intelligence: namely, the everyday ordinary ways that 'banal deception' is integrated into our lives. In so doing, Deceitful Media offers pressingly ethical, sober, and sophisticated pathways to reclaiming the unnatural ordinariness of the human psyche in the shadow of artificial intelligence. Highly readable and deeply instructive. * Benjamin Peters, University of Tulsa *Table of ContentsTable of Contents Acknowledgments Introduction Chapter 1. The Turing Test: Cultural life of an idea Chapter 2. How to dispel magic: Computers, interfaces, and the problem of the observer Chapter 3. The Eliza effect: Joseph Weizenbaum and the emergence of chatbots Chapter 4. Of daemons, dogs and trees: Situating AI in software Chapter 5. How to create a bot: Programming deception at the Loebner Prize Chapter 6. To believe in Siri: A critical analysis of voice assistants Conclusion: Our sophisticated selves Bibliography
£25.64
Nova Science Publishers Inc Machine Learning Research Progress
Book SynopsisAs a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn". At a general level, there are two types of learning: inductive, and deductive. Inductive machine learning methods extract rules and patterns out of massive data sets. The major focus of machine learning research is to extract information from data automatically, by computational and statistical methods. Hence, machine learning is closely related not only to data mining and statistics, but also theoretical computer science. This book presents new and important research in this field.
£232.49
Icon Books Artificial Intelligence: Modern Magic or
Book SynopsisArtificial intelligence has long been a mainstay of science fiction and increasingly it feels as if AI is entering our everyday lives, with technology like Apple's Siri now prominent, and self-driving cars almost upon us.But what do we actually mean when we talk about 'AI'? Are the sentient machines of 2001 or The Matrix a real possibility or will real-world artificial intelligence look and feel very different? What has it done for us so far? And what technologies could it yield in the future?AI expert Yorick Wilks takes a journey through the history of artificial intelligence up to the present day, examining its origins, controversies and achievements, as well as looking into just how it works. He also considers the future, assessing whether these technologies could menace our way of life, but also how we are all likely to benefit from AI applications in the years to come.Entertaining, enlightening, and keenly argued, this is the essential one-stop guide to the AI debate.
£8.54
John Murray Press Futureproof: 9 Rules for Humans in the Age of
Book SynopsisA New York Times bestselling author and tech columnist's counter-intuitive guide to staying relevant - and employable - in the machine age by becoming irreplaceably human.It's not a future scenario any more. We've been taught that to compete with automation and AI, we'll have to become more like the machines themselves, building up technical skills like coding. But, there's simply no way to keep up. What if all the advice is wrong? And what do we need to do instead to become futureproof?We tend to think of automation as a blue-collar phenomenon that will affect truck drivers, factory workers, and other people with repetitive manual jobs. But it's much, much broader than that. Lawyers are being automated out of existence. Last year, JPMorgan Chase built a piece of software called COIN, which uses machine learning to review complicated contracts and documents. It used to take the firm's lawyers more than 300,000 hours every year to review all of those documents. Now, it takes a few seconds, and requires just one human to run the program. Doctors are being automated out of existence, too. Last summer, a Chinese tech company built a deep learning algorithm that diagnosed brain cancer and other diseases faster and more accurately than a team of 15 top Chinese doctors.Kevin Roose has spent the past few years studying the question of how people, communities, and organisations adapt to periods of change, from the Industrial Revolution to the present. And the insight that is sweeping through Silicon Valley as we speak -- that in an age dominated by machines, it's human skills that really matter - is one of the more profound and counter-intuitive ideas he's discovered. It's the antidote to the doom-and-gloom worries many people feel when they think about AI and automation. And it's something everyone needs to hear.In nine accessible, prescriptive chapters, Roose distills what he has learned about how we will survive the future, that the way to become futureproof is to become incredibly, irreplaceably human.Trade ReviewA concise, insightful and sophisticated guide to maintaining humane values in an age of new machines -- The New York Times Book ReviewWhile we need to rewrite the rules of the twenty-first-century economy, Kevin's book is a great look at how people can do this on a personal level to always put humanity first -- Andrew YangLightly written and engaging * The Times *Kevin Roose provides a clear, compelling strategy for surviving the next wave of technology with our jobs - and souls - intact... Futureproof is the survival guide you need. * Charles Duhigg, The Power of Habit *AI will be a far bigger game changer for the world than COVID-19. And unless we start thinking and planning for it far more seriously now, we will be in even greater peril. Futureproof is a brilliant book that explains what we need to do, all of us, right now * Anthony Seldon *Roose offers an upbeat, practical guide for dealing with "a world that is increasingly arranged by and for machines" . . . Helpful advice to quell workers' anxiety * Kirkus Reviews *PRAISE FOR YOUNG MONEY - If Kevin Roose's finely crafted Young Money does not scare you straight about the life of a young financial analyst on Wall Street, it can't be done. Roose's frolic through Wall Street's playpen is a must-read. * House of Cards; Money and Power *Despite all the press about Wall Street, the stories that don't usually get told are those of the recent college graduates who clamour for the chance to work 100 hour plus weeks at the big banks. Kevin Roose's new book, which follows a handful of analysts through the trials and tribulations of their early years on the Street, is a thoughtful exploration of their motivations and their experiences - and it's a great read. * The Smartest Guys in the Room and All the Devils are Here *A cautionary true-life tale, Young Money should be required reading for every college student who is contemplating a job on Wall Street. As for the rest of us, who remember Wall Street before 2008, Kevin Roose has provided a great window into how that world has changed-and how it hasn't. * The Predator's Ball *Highly entertaining and impressive ...Roose's captivating read is sure to appeal to readers young and old who are interested in the zeitgeist of Wall Street since the crash * Publisher's Weekly *[Young Money] offers a compelling glimpse of Wall Street in the post-2008 recession era...thought provoking, excellent book * Booklist *The young people who have flocked to Wall Street are often badly used, caught up in power struggles among middle management and little appreciated ... [Young Money] captures the daily indignities to which the junior capitalists are subjected * Kirkus Reviews *
£9.99
O'Reilly Media Lean AI
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.
£29.99
Harvard Business Review Press Prediction Machines: The Simple Economics of
Book SynopsisNamed one of "The five best books to understand AI" by The EconomistThe impact AI will have is profound, but the economic framework for understanding it is surprisingly simple.Artificial intelligence seems to do the impossible, magically bringing machines to life—driving cars, trading stocks, and teaching children. But facing the sea change that AI brings can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many either cower in fear or predict an impossibly sunny future.But in Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this masterful stroke, they lift the curtain on the AI-is-magic hype and provide economic clarity about the AI revolution as well as a basis for action by executives, policy makers, investors, and entrepreneurs.In this new, updated edition, the authors illustrate how, when AI is framed as cheap prediction, its extraordinary potential becomes clear: Prediction is at the heart of making decisions amid uncertainty. Our businesses and personal lives are riddled with such decisions. Prediction tools increase productivity—operating machines, handling documents, communicating with customers. Uncertainty constrains strategy. Better prediction creates opportunities for new business strategies to compete. The authors reset the context, describing the striking impact the book has had and how its argument and its implications are playing out in the real world. And in new material, they explain how prediction fits into decision-making processes and how foundational technologies such as quantum computing will impact business choices.Penetrating, insightful, and practical, Prediction Machines will help you navigate the changes on the horizon.Trade Review"It's a must read for economists; it forces us to think more deeply about the essence of AI and its connection to prediction. And it's a must read for the public who need to know the enormous dangers the old/new AI poses to our own and our children's economic futures and freedoms." — Journal of Economic LiteratureAdvance Praise for Prediction Machines:"What does AI mean for your business? Read this book to find out." — Hal Varian, Chief Economist, Google". . . framing AI in terms of its predictive capabilities is not only a realistic portrayal of its capabilities today, but also one that business leaders can both understand and act upon. For that alone the book is worth reading." — Forbes"This book, written by three brilliant minds from the University of Toronto, is invaluable for business leaders looking for a primer on how AI might impact them." — Business Insider"A must-read for business leaders who need to know where AI is heading and how best to harness the new technology." — Journal of Economic Literature"Prediction Machines does a good job of showing where computers work best and where humans still have an edge." — the New York Times". . . a useful way to look at the fast-changing world of machine learning . . ." — the Financial Times"Consider it a CEO guide to parsing and prioritizing AI opportunities." — McKinsey Quarterly"Prediction Machines provides a very accessible and high-level overview of machine learning and the power and limits of the predictions provided by AI algorithms. The book is a must-read for business leaders and executives." — TechTalks"The authors . . .offer a compelling framework for mapping out the likely impact of AI on economies in the decades ahead." — BlackRock Investment Management
£20.90
Harvard Business Review Press All-in On AI: How Smart Companies Win Big with
Book SynopsisA Wall Street Journal bestsellerA Publisher's Weekly bestsellerA fascinating look at the trailblazing companies using artificial intelligence to create new competitive advantage, from the author of the business classic, Competing on Analytics, and the head of Deloitte's US AI practice.Though most organizations are placing modest bets on artificial intelligence, there is a world-class group of companies that are going all-in on the technology and radically transforming their products, processes, strategies, customer relationships, and cultures.Though these organizations represent less than 1 percent of large companies, they are all high performers in their industries. They have better business models, make better decisions, have better relationships with their customers, offer better products and services, and command higher prices.Written by bestselling author Tom Davenport and Deloitte's Nitin Mittal, All-In on AI looks at artificial intelligence at its cutting edge from the viewpoint of established companies like Anthem, Ping An, Airbus, and Capital One.Filled with insights, strategies, and best practices, All-In on AI also provides leaders and their teams with the information they need to help their own companies take AI to the next level.If you're curious about the next phase in the implementation of artificial intelligence within companies, or if you're looking to adopt this powerful technology in a more robust way yourself, All-In on AI will give you a rare inside look at what the leading adopters are doing, while providing you with the tools to put AI at the core of everything you do.Trade Review"Use All-In On AI to keep up with the evolving workplace, transform processes, and put AI at the core of everything you do." — TD magazine (Association for Talent Development)Named one of "4 of the Most Anticipated Business Books You Need to Read in 2023" by Inc. magazineAdvance Praise for All-In on AI:"Most organizations only scratch the surface of what is possible with AI. If you want to learn what it means to commit to AI as a transformational business resource, read this book and follow its prescriptions." — Marco Iansiti, David Sarnoff Professor of Business Administration, Harvard Business School; coauthor, Competing in the Age of AI"AI is the most transformational technology available today, and its primary benefits will go to those companies that exploit it aggressively. All-In on AI is an excellent guide to extracting the maximum value from AI." — Piyush Gupta, CEO, DBS Bank"AI is most easily incorporated in the strategies and practices of startups that have embraced its promise as essential to differentiation and performance. Davenport and Mittal identify the multifaceted organizational capabilities necessary for legacy companies to capture the benefits of AI, which will be essential to their competitive success." — Gary Loveman, Chairman and CEO, Well"All-In on AI describes the leadership, technology, and organizational change strategies that deliver the inherent value to make AI-fueled businesses possible." — Rajeev Ronanki, Senior Vice President, Elevance Health; author, You and AI
£23.75
Edward Elgar Publishing Ltd Research Handbook on Public Management and
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
£171.00
The History Press Ltd Faking It: Artificial Intelligence in a Human
Book Synopsis‘Refreshingly clear-eyed … Faking It is an insightful and intelligent book that’s a must for those looking for facts about AI hype.’ – Books+Publishing‘AI will be as big a game-changer as the smart phone and the personal computer – or bigger! This book will help you navigate the revolution.’ – Dr Karl KruszelnickiArtificial intelligence is, as the name suggests, artificial and fundamentally different to human intelligence. Yet often the goal of AI is to fake human intelligence. This deceit has been there from the very beginning. We’ve been trying to fake it since Alan Turing answered the question ‘Can machines think?’ by proposing that machines pretend to be humans.Now we are starting to build AI that truly deceives us. Powerful AIs such as ChatGPT can convince us they are intelligent and blur the distinction between what is real and what is simulated. In reality, they lack true understanding, sentience and common sense. But this doesn’t mean they can’t change the world.Can AI systems ever be creative? Can they be moral? What can we do to ensure they are not harmful? In this fun and fascinating book, Professor Toby Walsh explores all the ways AI fakes it, and what this means for humanity – now and in the future.Trade Review‘Refreshingly clear-eyed … Faking It is an insightful and intelligent book that’s a must for those looking for facts about AI hype.' -- Books+Publishing‘AI will be as big a game-changer as the smart phone and the personal computer – or bigger! This book will help you navigate the revolution.’ -- Dr Karl Kruszelnicki‘Faking It includes a whistlestop tour of AI history, providing a long list of grifts and false dawns, from the 1770 marvel, the Mechanical Turk, a chess-playing automaton secretly linked to a human player, to ELIZA, the 1967 natural language model that could hold a conversation to the level of tuned-out coworker.’ — KURT JOHNSON, THE AGE
£19.54
Cambridge University Press Artificial Intelligence
Book SynopsisFully revised and updated, this comprehensive new edition covers modern AI and machine learning for undergraduate and graduate students. Includes new chapters on deep learning including generative AI, causality and social impact, new social impact sections, major revisions to knowledge graphs, reasoning and decision making, and more AIPython code.Trade Review'This is an important textbook. Based on their broad experience, the authors harmonize some of the most exciting recent developments in the field, such as generative AI, with more traditional methods, within a unified agent framework. This will broaden the perspective of those relatively new to the field, for whom AI and deep learning appear almost synonymous.' Yoav Shoham, Stanford University and AI21 Labs'This book is a tour de force. It provides a comprehensive introduction to so many topics in modern AI. The clarity of the exposition and the ability to capture the intuition underlying complex concepts make this book compelling and appealing to a broad audience.' Pascal Van Hentenryck, Georgia Institute of Technology'This new edition offers an up-to-date account of AI, presenting the field in an accessible and unified manner. I particularly like the 'relations-late' approach, in which first-order logic and relational AI are covered later, after thoroughly covering more basic, feature-based methods. The hybrid data-driven/model-based approach to agent design that the authors propose will be essential to the development of reliable and trustworthy intelligent systems.' Kevin Patrick Murphy, Google Brain, author of Probabilistic Machine Learning'Poole and Mackworth's now classic textbook has guided my senior undergraduate AI class since its first edition. Coupled with online resources, the book presents a comprehensive overview, with technical substance and many pointers for further study, in a coherent structure that fosters learning of key interrelated concepts. The third edition updates the content to cover the massive recent AI advances.' Jesse Hoey, University of Waterloo'Machine learning has undergone spectacular advances over the last few years, but to harvest the new capabilities one needs an engineering framework to build computational agents. This book teaches students about the concepts and techniques that make that possible.' Rodney Brooks, MIT and Robust AI'Wide-ranging, well-organized, up-to-date, and in-depth coverage of the AI world. The numerous figures, algorithms, and extensive references make this a valuable resource that readers will return to repeatedly. Instructors and students will benefit from the well-crafted end-of-chapter exercises. The thought-provoking social impact sections in each chapter and the social impact chapter admirably address the positive and harmful impacts on people. These complement the strong technical descriptions, wisely encouraging researchers and practitioners to limit the risks by highlighting human-centred AI. Poole and Mackworth are highly acclaimed experts who eagerly present their subject with enthusiasm and thoroughness.' Ben Shneiderman, University of Maryland, author of Human-Centered AI'This revised and extended edition of Artificial Intelligence: Foundations of Computational Agents should become the standard text of AI education. Computer science students will find in this volume a broad and uniquely coherent perspective on many computational models of learning, reasoning, and decision-making. Students of causal inference, in particular, will rejoice at viewing the causal revolution reconnected to its roots in formal logic and probabilistic decision-making, strengthened and reinforced by concrete algorithms, challenging exercises, and open source AIPython codes. Highly recommended.' Judea Pearl, UCLA, Turing Award winner and author of Causality and The Book of Why'This textbook is impressively comprehensive, covering all the major AI paradigms that have been introduced and studied over the years. At the same time, it is up to date with the latest technical advances and interdisciplinary perspectives on social impacts. I expect it to be a valuable resource for both teachers and students.' Peter Stone, University of Texas at Austin'Artificial Intelligence: Foundations of Computational Agents is a great AI textbook written by prominent leaders in the field. It covers everything you want to know about AI in a very accessible style, accompanied by a wide range of thoughtful and challenging exercises. I find this book to be an extremely valuable resource, not only for teaching, but even more so for offering an updated reference to a wide spectrum of foundational subjects at the current frontier of AI.' Rina Dechter, University of California, Irvine, author of Constraint Programming'Poole and Mackworth's book has been my go-to resource for students who need an introduction to Artificial Intelligence. While the previous versions have provided a complete overview of the field, the newer version organizes this information in a crystal clear manner. The division of the topics based on what the agent knows, what is in the world, and what the effects of its actions are allow for a logical flow of topics inside AI. As a comprehensive textbook for AI that includes slides, solutions, and code, this book is a must-have on the bookshelf for AI instructors, students, researchers, and practitioners.' Sriraam Natarajan, University of Texas at Dallas'This is a great foundational book on the science of AI, covering the main concepts and techniques using a simple structured approach. The extensive material on the social impact of AI provides much needed attention to the responsible design and use of AI. AI researchers can find here the indispensable foundational knowledge and the needed ethical attitude to create beneficial AI innovation.' Francesca Rossi, IBM Fellow'The latest edition of Poole and Mackworth's book emphasizes the societal impacts of AI in every chapter, making it an essential read for anyone interested in AI, especially those who will shape its future to ensure these powerful technologies benefit society and minimize harms.' Saleema Amershi, Microsoft Research'This textbook provides an amazing introduction to the field of AI. By bringing together learning, reasoning, and decision-making, it shows the rich interconnections across the various AI subfields. The writing is just at the right level to introduce students to the different facets of AI. The updated edition seamlessly integrates the exciting developments in deep learning into the broader AI context. The text also highlights the societal impact of AI, including AI ethics and computational sustainability.' Carla Gomes, Cornell University'Poole and Mackworth - two pioneers of AI - present an admirably broad and complete introduction to the field, with a very useful focus on intelligent agents. From deep learning to causal reasoning, from Bayesian networks to knowledge graphs, from fundamental algorithms to effective heuristics, this book covers a wide range of important topics, each accompanied by a timely section on social impact. Highly recommended!' Holger Hoos, RWTH Aachen'Poole and Mackworth's Artificial Intelligence: Foundations of Computational Agents 3e is a tour de force. This is a comprehensive and clearly written text that takes the reader through core concepts in symbolic AI and machine learning, providing pathways for broad introductory undergraduate courses, or focused graduate courses. It's an outstanding resource for student and instructor alike. Whether you're a seasoned AI researcher or a student entering the field, you'll learn a great deal from reading this book.' Sheila McIlraith, University of TorontoTable of ContentsPreface; Part I. Agents in the World: 1. Artificial intelligence and agents; 2. Agent architectures and hierarchical control; Part II. Reasoning and Planning with Certainty: 3. Searching for solutions; 4. Reasoning with constraints; 5. Propositions and inference; 6. Deterministic planning; Part III. Learning and Reasoning with Uncertainty: 7. Supervised machine learning; 8. Neural networks and deep learning; 9. Reasoning with uncertainty; 10. Learning with uncertainty; 11. Causality; Part IV. Planning and Acting with Uncertainty; 12. Planning with uncertainty; 13. Reinforcement learning; 14. Multiagent systems; Part V. Representing Individuals and Relations: 15. Individuals and relations; 16. Knowledge graphs and ontologies; 17. Relational learning and probabilistic reasoning; Part VI. The Big Picture: 18. The social impact of artificial intelligence; 19. Retrospect and prospect; Appendices; References; Index of Algorithms; Index.
£57.99
C Hurst & Co Publishers Ltd The Algorithm: How AI Can Hijack Your Career and
Book SynopsisArtificial intelligence is being used, on a massive scale, to decide who gets hired, fired and promoted. Through whistleblower exclusives, leaked internal documents and astonishing real-world practices, journalist Hilke Schellmann reveals the secret rise of AI in the world of work. Testing them herself, she discovers that many algorithms making these high-stakes calculations do more harm than good, and traces their origins to troubling pseudoscientific ideas about people’s ‘true’ essence. Interviewing experts, developers and ordinary workers, The Algorithm offers fascinating and alarming truths. From software analysing interviewees’ facial expressions and tone of voice, to video games assessing their performance, to ‘personality profiles’ built from candidates’ social media, almost all major employers use AI in recruitment. Programmes track their staff’s activity, group dynamics and physical health, identifying who is productive, a bully, worth long-term investment, or likely to quit. But can we trust them? In a world of severe job insecurity, workplace algorithms are on the brink of dominating or even threatening us—if we don’t fight back.Trade Review‘The best available case study [of] … the use of artificial intelligence by human resource departments.' -- The New York Times, 'Top 5 Books on Artificial Intelligence'‘Focuses on how the technology is already deployed in personnel decisions in the workplace — with alarming results.’ -- Financial Times'Schellmann pulls the curtain back on the AI-driven "HR tech" revolution taking over hiring and managing employees, and finds tools that are arbitrary, ineffective, discriminatory and likely unlawful. Reads like a dozen scandals waiting to erupt.' -- Gavin Mueller, author of 'Breaking Things at Work''A disturbing investigation into use of AI systems in hiring, firing, and employee surveillance. As Schellmann demonstrates, AI has moved into crucial areas of our lives, but the process has been so fast and silent that its influence is almost invisible. She argues that HR managers should be required to understand how their algorithms work, and there must be greater human input to personnel decisions. This eye-opening book makes it hard to disagree.' -- 'Kirkus Reviews''In "The Algorithm", Hilke Schellmann has done the impossible: she has rendered the baffling 'Wild West' of AI immensely readable and approachable. Schellmann gives us the dark and hidden history of tech innovation and the marketplace through the stories of those whose lives have been smashed by its glitches.' -- Eliza Griswold, Pulitzer Prize-winning author of 'Amity and Prosperity''One of the most important topics of our time--one that impacts all of us more than we realise. The book takes a balanced approach to illuminating the current state of AI in the workplace. It's not just about incredible benefits or doomsday scenarios, but a real look into the current state of these tools, the incentive systems driving their proliferation, the mixed results they provide, and how we might ensure better outcomes. Highly recommended.' -- Ryan Fuller, former vice president for workplace intelligence, Microsoft'A fresh, important perspective on how AI is changing many critical workplace decisions in organizations. Schellmann's research is thorough and clever, and exposes the many of problems that AI and its proponents have already created for companies and employees.' -- David Futrell, former senior director of organization performance, Walmart'Hilke Schellmann was one of the first journalists to understand the dangers of AI passing judgement on workers, and The Algorithm is an absolutely vital book about the risks and harms of the systems already operating--on us--today.' -- Clay Shirky, author of "Cognitive Surplus" and "Here Comes Everybody"
£19.80
Arcturus Publishing Artificial Intelligence
Book SynopsisWhat is artificial intelligence? What problems does it solve? Should we fear its potential? In this highly accessible guide to the subject, Richard Urwin explains how AI came about and how it has developed over the years through the construction of ever more sophisticated computer programs. From primitive calculators and early robotics to stock market analytics and ChatGPT, readers can explore the history and far-reaching capabilities of this dynamic field and its potentially frightening possibilities. Includes:• The History of Artificial Intelligence• Data-mining and Statistics• Deep learning• Swarm Intelligence• The singularity By turns fascinating and scary, Artificial Intelligence will take the reader on an amazing journey of this field''s world-changing potential.
£9.49
Manning Publications Deep Learning with R, Second Edition
Book SynopsisDeep learning from the ground up using R and the powerful Keras library! In Deep Learning with R, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Deep Learning with R, Second Edition shows you how to put deep learning into action. It's based on the revised new edition of François Chollet's bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks. about the technology Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R. what's inside Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation about the reader For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required.
£41.39
Everyman Chess The AI Revolution in Chess
Book SynopsisIn 2018 DeepMind published the shocking results of their chess-playing artificial intelligence software, AlphaZero. Chess players looked in disbelief and immediately wondered how AI would affect the future of chess. Less than a year later, a whole new wave of chess engines emerged that were based on using neural networks to evaluate positions in a completely new way. This book is about the extraordinary impact that AI has had on modern chess. The games of top chess players since the end of 2018 have reflected the use of these new engines in home analysis. They have significantly developed opening theory as well as the general understanding of middlegame concepts. By analysing these games with the help of neural network engines, FIDE Master Joshua Doknjas discusses numerous exciting ideas and examines areas of chess that had previously been overlooked. With thorough explanations, questions, and exercises, this book provides fascinating material for masters and less experienced players alike.
£17.09
Ebury Publishing CoIntelligence
Book SynopsisEthan Mollick is a Professor of Management at Wharton, specializing in entrepreneurship and innovation. His research has been featured in various publications, including Forbes, the New York Times, and the Wall Street Journal. He is the creator of numerous educational games on a variety of topics. He lives and teaches in Philadelphia, Pennsylvania.
£15.29
John Wiley & Sons Inc Convergence Artificial Intelligence and Quantum
Book SynopsisPrepare for the coming convergence of AI and quantum computing A collection of essays from 20 renowned, international authors working in industry, academia, and government, Convergence: Artificial Intelligence and Quantum Computing explains the impending convergence of artificial intelligence and quantum computing. A diversity of viewpoints is presented, each offering their view of this coming watershed event. In the book, you'll discover that we're on the cusp of seeing the stuff of science fiction become reality, with huge implications for ripping up the existing social fabric, global economy, and current geopolitical order. Along with an incisive foreword by Hugo- and Nebula-award winning author David Brin, you'll also find: Explorations of the increasing pace of technological developmentExplanations of why seemingly unusual and surprising breakthroughs might be just around the cornerMaps to navigate the potential minefields that await us as AI and quantum computing come togetherA fascinating and thought-provoking compilation of insights from some of the leading technological voices in the world, Convergence convincingly argues that we should prepare for a world in which very little will remain the same and shows us how to get ready.Table of ContentsPreface xi Foreword xix Part I Policy and Regulatory Impacts 1 Chapter 1 Quantum Inflection Points 3Jim Gable Chapter 2 Quantum Delegation 11Mandy Sweeney and Chris Gauthier Chapter 3 The Problem of Machine Actorhood 23Patrick Thaddeus Jackson Chapter 4 Data Privacy, Security, and Ethical Governance Under Quantum AI 37Sarah Pearce Chapter 5 The Challenge of Quantum Noise 45Philip Johnson Chapter 6 A New Kind of Knowledge Discovery 53Ramin Ayanzadeh and Milton Halem Part II Economic Impacts 61 Chapter 7 Quantum Tuesday: How the U.S. Economy Will Fall, and How to Stop It 63Alexander W. Butler Chapter 8 Quantum-AI Space Communications 83Mason Peck Chapter 9 Quantum Planet Hacking 93Philip L. Frana Chapter 10 Ethics and Quantum AI for Future Public Transit Systems 111Benjamin Crawford Chapter 11 The Road to a Better Future 119Denise Ruffner and André M. König Part III Social Impacts 127 Chapter 12 The Best Numbers Are in Sight. But Understanding? 129Roald Hoffmann and Jean-Paul Malrieu Chapter 13 The Advancement of Intelligence or the End of It? 143Kate Jeffery Chapter 14 Quantum of Wisdom 157Colin Allen and Brett Karlan Chapter 15 Human Imagination and HAL 167Erik Viirre Chapter 16 A Critical Crossroad 175Joseph N. Pelton Chapter 17 Empathetic AI and Personalization Algorithms 183Philippe Beaudoin and Alexander W. Butler Chapter 18 Should We Let the Machine Decide What Is Meaningful? 193J. M. Taylor Chapter 19 The Ascent of Quantum Intelligence in Steiner’s Age of the Consciousness Soul 205Stephen R. Waite Chapter 20 Quantum Computing’s Beautiful Accidents 213Christopher Savoie Appendix A What Is Quantum Computing? 221Philip L. Frana Appendix B What Is Artificial Intelligence? 239Philip L. Frana Glossary 247 References 251 Index 259 About the Editor 271
£19.99
BenBella Books Marketing Artificial Intelligence: AI, Marketing,
Book Synopsis
£18.69
Oxford University Press Data Science Ethics
Book SynopsisThis book examines a variety of different concepts related to data science ethics and techniques that can help with, or lead to, ethical concerns, whilst featuring cautionary tales that illustrate the importance and potential impact of data science ethics.Trade ReviewAn excellent reading with both depth and breadth on some of the most important challenges and risks data scientists, businesses, governments and societies face today as Artificial Intelligence adoption grows. These are topics everyone needs to be aware of, and this is one of the very few must read books on these issues * Theodoros Evgeniou, Professor of Decision Sciences and Technology Management at INSEAD, France *This is an important and timely book for data scientists, written in a clear and engaging way. Motivated by many relevant examples, the author successfully de-mystifies data ethics lingo and presents a comprehensive view of ethical considerations during the entire data science lifecycle. * Galit Shmueli, Tsing Hua Distinguished Professor, Institute of Service Science and Institute Director, College of Technology Management, National Tsing Hua University, Taiwan *Table of ContentsFoster Provost: Foreword Preface 1: Introduction to Data Science Ethics 2: Ethical Data Gathering 3: Ethical Data Preprocessing 4: Ethical Modelling 5: Ethical Evaluation 6: Ethical Deployment 7: Conclusion
£34.19
Harvard Business Review Press More Human
Book SynopsisAI has the potential to transform leadership and the human experience of work or to lead us into an automated and uninspiring work reality. Which one will it be?
£25.60
Basic Books Deep Medicine: How Artificial Intelligence Can
Book SynopsisA visit to a physician these days is cold: physicians spend most of their time typing at computers, making minimal eye contact. Appointments generally last only a few minutes, with scarce time for the doctor to connect to a patient's story, or explain how and why different procedures and treatments might be undertaken. As a result, errors abound: indeed, misdiagnosis is the fourth-leading cause of death in the United States, trailing only heart disease, cancer, and stroke. This is because, despite having access to more resources than ever, doctors are vulnerable not just to the economic demand to see more patients, but to distraction, burnout, data overload, and their own intrinsic biases. Physicians are simply overmatched. As Eric Topol argues in Deep Medicine, artificial intelligence can help. Natural-language processing could automatically record notes from our doctor visits; virtual psychiatrists could better predict the risk of suicide or other mental health issues for vulnerable patients; deep-learning software will make every physician a master diagnostician; and we could even use smartphone apps to take our own medical "selfies" for skin exams and receive immediate analysis. . On top of that, the virtual smartphone assistants of today--Alexa, Siri, Cortana--could analyze our daily health data to reduce the need for doctor visits and trips to the emergency room, and support for people suffering from asthma, epilepsy, and heart disease. By integrating tools like these into their daily medical practice, doctors would be able to spend less time collecting and cataloging information, and more time providing thorough, intimate, and meaningful care for their patients, as no machine can.Artificial intelligence can also help remedy the debilitating cost of healthcare, both for individuals and the economy writ large. The medical sector now absorbs 20 percent of the US gross domestic product--it is largest sector by dollars and jobs. And it's very inefficient. Take the cost of medical scans: There are over 20 million medical scans performed in the US every day, and an MRI, for example, costs hundreds to thousands of dollars. AI could process 260 million medical scans (more than 2 weeks' worth) in less than 24 hours for a cost of only $1000. We pay billions and billions of dollars for the same work today.The American health care system needs a serious reboot, and artificial intelligence is just the thing to press the restart button. As innovative as it is hopeful, Deep Medicine ultimately shows us how we can leverage artificial intelligence for better care at lower costs with more empathy, for the benefit of patients and physicians alike.
£22.50
Aperture Aperture 257
Book Synopsis
£18.95
MIT Press Ltd The Human Edge
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.
£61.20
