Artificial intelligence (AI) Books
Duke University Press Killer Apps
Book SynopsisIn Killer Apps Jeremy Packer and Joshua Reeves provide a detailed account of the rise of automation in warfare, showing how media systems are central to building weapons systems with artificial intelligence in order to more efficiently select and eliminate military targets. Drawing on the insights of a wide range of political and media theorists, Packer and Reeves develop a new theory for understanding how the intersection of media and military strategy drives today's AI arms race. They address the use of media to search for enemies in their analyses of the history of automated radar systems, the search for extraterrestrial life, and the development of military climate science, which treats the changing earth as an enemy. As the authors demonstrate, contemporary military strategy demands perfect communication in an evolving battlespace that is increasingly inhospitable to human frailties, necessitating humans' replacement by advanced robotics, machine intelligence, and media systems.Trade Review“In this crucial new book, Jeremy Packer and Joshua Reeves offer a provocative, media-centric analysis of automated killing machines. Engaging with an armada of flying sensors, robotic submarines, and AI weapons already in use, they show that big data, computer vision, and super intelligence emerge not just to order and organize the battlefield, but to produce new enemies. Clever and incisive, the book provides a haunting look at warfare of the near future.” -- Lisa Parks, coeditor of * Life in the Age of Drone Warfare *“This is an excellent book: well designed, thoroughly engaging, informative and, unfortunately, extremely topical and timely. The authors have gone to great lengths to make Killer Apps relentlessly up to date, providing readers with the latest in weapons developments, including AI drones and ‘swarmanoid’ robotics. With its impressive grounding in theory and hardware, it will become the go-to book for critical understandings of the intersection of warfare, media, and enmity.” -- Geoffrey Winthrop-Young, author of * Kittler and the Media *“By focusing first and foremost on the epistemological function of military media, Packer and Reeves have produced a range of rigorous and highly engaging analyses, with broad applicability across a range of possible fields of research.” -- Malcolm Ogden * Critical Studies in Media Communication *"The book is a tour de force regarding the rise of automation in warfare. I recommend Killer Apps to anyone interested in media technology studies as well as political science and international security studies." -- José de Arimatéia da Cruz * International Social Science Review *"Through a careful presentation of technological developments in the domain of military affairs, coupled with a rigorous historical analysis, an effective application of media theory, and a vast array of case studies, Jeremy Packer and Joshua Reeves convincingly present an account of how we arrived where we are today in a world on the cusp of embracing new forms of executing war that will be largely dependent on AI." -- Joseph Michael Gratale * European Journal of American Studies *“For the national security, intelligence, and defense communities, Killer Apps presents both a valuable scholarly resource and a deeply ambiguous set of questions. . . . [Packer and Reeves] force readers to transcend the humanist epistemological orientation in order to understand what the machine age has truly ushered in.” -- Zac Rogers * Parameters *Table of ContentsAcknowledgments vii Preface to an Inauthentic Document ix Introduction. Event Matrix (DoD) 1 1. Identification Friend or Foe (DoD) 29 2. Centralized Control/Decentralized Execution (DoD) 48 3. Hostile Environment (DoD) 61 4. In Extremis (DoD) 89 5. Intelligence, Surveillance, and Reconnaissance (DoD) 109 6. Autonomous Operation (DoD) 124 7. Vital Ground (DoD) 139 8. Escalation (DoD) 159 9. Unidentified Flying Objects (USAF) 175 Conclusion. Armistice (DoD) 198 Notes 217 References 235 Index 261
£98.60
Duke University Press Killer Apps
Book SynopsisJeremy Packer and Joshua Reeves provide a critical account of the history and future of automation in warfare by highlighting the threats posed by the latest advances in media technology and artificial intelligence.Trade Review“In this crucial new book, Jeremy Packer and Joshua Reeves offer a provocative, media-centric analysis of automated killing machines. Engaging with an armada of flying sensors, robotic submarines, and AI weapons already in use, they show that big data, computer vision, and super intelligence emerge not just to order and organize the battlefield, but to produce new enemies. Clever and incisive, the book provides a haunting look at warfare of the near future.” -- Lisa Parks, coeditor of * Life in the Age of Drone Warfare *“This is an excellent book: well designed, thoroughly engaging, informative and, unfortunately, extremely topical and timely. The authors have gone to great lengths to make Killer Apps relentlessly up to date, providing readers with the latest in weapons developments, including AI drones and ‘swarmanoid’ robotics. With its impressive grounding in theory and hardware, it will become the go-to book for critical understandings of the intersection of warfare, media, and enmity.” -- Geoffrey Winthrop-Young, author of * Kittler and the Media *“By focusing first and foremost on the epistemological function of military media, Packer and Reeves have produced a range of rigorous and highly engaging analyses, with broad applicability across a range of possible fields of research.” -- Malcolm Ogden * Critical Studies in Media Communication *"The book is a tour de force regarding the rise of automation in warfare. I recommend Killer Apps to anyone interested in media technology studies as well as political science and international security studies." -- José de Arimatéia da Cruz * International Social Science Review *"Through a careful presentation of technological developments in the domain of military affairs, coupled with a rigorous historical analysis, an effective application of media theory, and a vast array of case studies, Jeremy Packer and Joshua Reeves convincingly present an account of how we arrived where we are today in a world on the cusp of embracing new forms of executing war that will be largely dependent on AI." -- Joseph Michael Gratale * European Journal of American Studies *“For the national security, intelligence, and defense communities, Killer Apps presents both a valuable scholarly resource and a deeply ambiguous set of questions. . . . [Packer and Reeves] force readers to transcend the humanist epistemological orientation in order to understand what the machine age has truly ushered in.” -- Zac Rogers * Parameters *Table of ContentsAcknowledgments vii Preface to an Inauthentic Document ix Introduction. Event Matrix (DoD) 1 1. Identification Friend or Foe (DoD) 29 2. Centralized Control/Decentralized Execution (DoD) 48 3. Hostile Environment (DoD) 61 4. In Extremis (DoD) 89 5. Intelligence, Surveillance, and Reconnaissance (DoD) 109 6. Autonomous Operation (DoD) 124 7. Vital Ground (DoD) 139 8. Escalation (DoD) 159 9. Unidentified Flying Objects (USAF) 175 Conclusion. Armistice (DoD) 198 Notes 217 References 235 Index 261
£25.19
Duke University Press Critical AI
Book SynopsisThis issue provides an overview of the emerging interdisciplinary field of Critical AI, which seeks to demystify artificial intelligence; counter its mythologizing as a marvelous and impenetrable black box; and translate, interpret, and critique its operations, from data collection and model architecture to decision making. Artists and researchers are developing new methods, practices, and concepts for this critical project, which is both historicist and attentive to the institutional, technological, and epistemic transformations still underway. Contributors to this special issue collectively articulate and evince just such a critical approach to AI, one that combines humanistic and technical inquiry in its exploration of disciplinary and epistemological questions on the one hand, and the techniques of machine learning on the other. Featured contributions articulate some of the social, cultural, and ethicopolitical dimensions of machine learning in domains such as ecologies, art, poeti
£10.99
Apress Ansible From Beginner to Pro
Table of Contents1. Getting Started2. The Inventory File3. Installing Wordpress4. Ansible Roles5. Parameterising Playbooks6. Writing Your Own Modules7. Orchestrating AWS8. Testing with Test Kitchen9. Advanced AnsibleAppendix A. Installing AnsibleAppendix B. YAML Files
£49.49
APress Practical Artificial Intelligence
Book SynopsisChapter 1: Logic & AI.- Chapter 2: Automated Theorem Proving & First Order Logic.- Chapter 3: Agents.- Chapter 4: Mars Rover.- Chapter 5: Multi-Agent Systems.- Chapter 6: Communication in a Multi-Agent System using WCF.- Chapter 7: Cleaning Agents: A multi-Agent System Problem.- Chapter 8: Simulation.- Chapter 9: Support Vector Machines.- Chapter 10: Decision Trees.- Chapter 11: Neural Networks.- Chapter 12: Handwritten Digit Recognition. - Chapter 13: Clustering & Multi-Objective Clustering.- Chapter 14: Metaheuristics.- Chapter 15: Game Programming.- Chapter 16: Game Theory - Adversarial Search & Othello Game.- Chapter 17: Reinforcement Learning.Table of ContentsPractical Artificial IntelligenceChapter 1: Logic & AIChapter 2: Automated Theorem Proving & First Order LogicChapter 3: AgentsChapter 4: Mars RoverChapter 5: Multi-Agent SystemsChapter 6: Communication in a Multi-Agent System using WCFChapter 7: Cleaning Agents: A multi-Agent System ProblemChapter 8: SimulationChapter 9: Support Vector MachinesChapter 10: Decision TreesChapter 11: Neural NetworksChapter 12: Handwritten Digit RecognitionChapter 13: Clustering & Multi-Objective ClusteringChapter 14: MetaheuristicsChapter 15: Vines & CopulasChapter 16: Game ProgrammingChapter 17: Sliding Tiles Puzzle & Othello GameChapter 18: Reinforcement Learning
£63.74
APress Building an Effective Data Science Practice
Book SynopsisGain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You'll start by delving into the fundamentals of data science classes of data science problems, data science techniques and their applications and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practiceprovides a common base of reTable of ContentsPart One: Fundamentals1. Introduction: The Data Science Process2. Data Science and your business 3. Monks vs. Cowboys: Data Science CulturesPart Two: Classes of Problems4. Classification 5. Regression6. Natural Language Processing 7. Clustering8. Anomaly Detection9. Recommendations10. Computer Vision11. Sequential Decision Making Part Three: Techniques & Technologies12. Overview13. Data Capture14. Data Preparation15. Data Visualization16. Machine Learning17. Inference18. Other tools and services19. Reference Architecture20. Monks vs. Cowboys: PraxisPart Four: Building Teams and Executing Projects21. The Skills Framework22. Building and structuring the team23. Data Science Projects Appendix FAQs
£37.99
APress Practical MATLAB Deep Learning
Book SynopsisHarness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that provides an introduction to deep learning and using MATLAB''s deep-learning toolboxes. In this book, you''ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. This edition includes new and expanded projects, and covers generative deep learning and reinforcement learning.Over the course of the book, you''ll learn to model complex systems and apply deep learning to problems in those areas. Applications include: Aircraft navigation An aircraft that lands on Titan, the moon of Saturn, using reinforcement learning Stock market prediction Natural language processing Music creation usng generative deep learning Plasma control Earth sensor processing for spacecraft MATLAB Bluetooth data acquisition applied to danTable of Contents1. What is deep learning? – no changes except editoriala. Machine learning vs. deep learningb. Approaches to deep learningc. Recurrent deep learningd. Convolutional deep learning2. MATLAB machine and deep learning toolboxesa. Describe the functionality and applications of each toolboxb. Demonstrate MATLAB toolboxes related to Deep Learningc. Include the text toolbox generative toolbox and reinforcement learning toolboxd. Add more detail on each3. Finding Circles – no changes except editorial.4. Classifying movies – no changes except editorial.5. Tokamak disruption detection – this would be updated.6. Classifying a pirouette – no changes except editorial.7. Completing sentences - This would be revamped using the MATLAB Text Processing Toolbox.8. Terrain based navigation-The example in the original book would be changed to a regression approach that can interpolate position. We would switch to a terrestrial example applicable to drones.9. Stock prediction – this is a very popular chapter. We would improve the algorithm.10. Image classification – no changes except editorial.11. Orbit Determination – add inclination to the algorithm.12. Earth Sensors – a new example on how to use neural networks to measure roll and yaw from any Earth sensor.13. Generative deep learning example. This would be a neural network that generates pictures after learning an artist’s style.14. Reinforcement learning. This would be a simple quadcopter hovering control system. It would be simulation based although readers would be able to apply this to any programmable quadcopter.
£46.74
APress Winning the National Security AI Competition
Book SynopsisIn introducing the National Security Commission on AI''s final report, Eric Schmidt, former Google CEO, and Robert Work, former Deputy Secretary of Defense, wrote: The human talent deficit is the government''s most conspicuous AI deficit and the single greatest inhibitor to buying, building, and fielding AI-enabled technologies for national security purposes. Drawing upon three decades of leading hundreds of advanced analytics and AI programs and projects in government and industry, Chris Whitlock and Frank Strickland address in this book the primary variable in the talent deficit, i.e., large numbers of qualified AI leaders.The book quickly moves from a case for action to leadership principles and practices for effectively integrating AI into programs and driving results in AI projects. The chapters convey 37 axioms - enduring truths for developing and deploying AI - and over 100 leader practices set among 50 cases and examples, 40 of which focus on AI iTable of ContentsForewordIntroduction Chapter 1. The Three Imperatives to Develop AI Leaders Chapter 2. How Leaders Should Think and Talk About AI Chapter 3. Leading the Program Chapter 4. Government Programming and Budgeting for AI Leaders Chapter 5. Leading the Project Chapter 6. Data Science for AI Leaders Chapter 7. Leading the People Chapter 8. Leading the Technology Endnotes About AI Leaders
£46.74
APress Python Debugging for AI Machine Learning and
Book SynopsisThis book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software. It will teach you a novel pattern-oriented approach to diagnose and debug abnormal software structure and behavior. The book begins with an introduction to the pattern-oriented software diagnostics and debugging process that, before performing Python debugging, diagnoses problems in various software artifacts such as memory dumps, traces, and logs. Next, you'll learn to use various debugging patterns through Python case studies that model abnormal software behavior. You'll also be exposed to Python debugging techniques specific to cloud native and machine learning environments and explore how recent advances in AI/ML can help in Python debugging. Over the course of the book, case studies will show you how to resolve issues around environmental problems, crashes, hangs, resource spikes, leaks, and performancedegradatioTable of ContentsChapter 1: Fundamental Vocabulary.- Chapter 2: Pattern-Oriented Debugging.- Chapter 3: Elementary Diagnostics Patterns.- Chapter 4: Debugging Analysis Patterns.- Chapter 5: Debugging Implementation Patterns.- Chapter 6: IDE Debugging in Cloud.- Chapter 7: Debugging Presentation Patterns.- Chapter 8: Debugging Architecture Patterns.- Chapter 9: Debugging Design Patterns.- Chapter 10: Debugging Usage Patterns.- Chapter 11: Case Study: Resource Leaks.- Chapter 12: Case Study: Deadlock.- Chapter 13: Challenges of Python Debugging in Cloud Computing.- Chapter 14: Challenges of Python Debugging in AI and Machine Learning.- Chapter 15: What AI and Machine Learning Can Do for Python Debugging.- Chapter 16: The List of Debugging Patterns.
£38.24
APress Building AI Driven Marketing Capabilities
Book SynopsisFrom understanding various technologies as an enabler to marketing efforts and its impact on decision making and mapping of various facets of customer experience, this book is recommended for marketers and learners to understand the advantages of using technology.Table of Contents1. From Data to Action: Leveraging AI in marketing1.1 AI & Marketing: Core Elements 1.2 Unleashing AI driven competitive advantage through IoT and Big Data Analytics1.3 Challenges of using AI technologies in the area of Marketing1.4 Core benefits of AI Marketing1.5 AI and future of Marketing 2. Informed Data driven decision making 2.1 Using Big data analytics for market intelligence2.2 Application of Big data analytics to marketing mix elements2.3 AI led Cognitive Data Quality Management2.4 AI-enabled marketing decisions 3. AI Marketing & Predicting Consumer Choices3.1 The value of social media for Improving Customer Engagement3.2 Optimizing marketing value, retention, customer satisfaction and loyalty3.3 Strategic applications of AI in different stages of customer journey3.4 AI in segmentation, targeting and positioning3.5 Internet trends and customer sentiment analysis 4. Unlocking Data in understanding Customers4.1 Customer Analytics4.1.1 Descriptive Customer Analytics4.1.2 Predictive Customer Analytics4.1.3 Prescriptive Customer Analytics4.2 Marketing Analytics: AI for Data Driven Marketing4.3 Customer Data Visualization & Information Management4.4 Mapping Customer Journey through big data analytics 5. Improving Experiences and Customer Satisfaction with AI5.1 AI and Product Life Cycle Management (PLM)5.2 Opportunities and Challenges of applying AI for PLM5.3 AI and granular personalization5.4 Use of AI to provide each segment of a target with tailored content 6. Value Creation & Value Capture with Artificial Intelligence6.1 Role of AI in optimizing Pricing6.2 Optimizing marketing value, retention and loyalty6.3 XR on value co-creation and customer engagement6.4 Creating value with data analytics6.5 Customer Value Modelling6.6 Marketing intelligence for optimal marketing return6.7 Creating value with data analytics 7. Reliable & Profitable AI driven Distribution7.1 Using AI for Distribution Process Management7.2 Smart Distribution7.3 Prediction of consumer behavior and improving lead generation7.4 Optimizing sales territory design with AI7.5 AI based delivery system7.6 AI integrated Logistics, inventory management, warehousing and transportation 8. Artificial Intelligence driven Promotions and Social Networking8.1 Network Modelling, Visualization and Analyzing Tools8.2 Role of Centrality in Social Networks: Influencer Marketing8.3 Sentiment Analysis and Public Opinion Mining8.4 Review Mining and Rating8.5 Big Data & scalability in Social Networks8.6 AI powered Chatbots and conversational experiences8.7 Propensity modelling for advertisement targeting and lead scoring8.8 Advertising Optimization & Viral Effects8.9 Fake News, Misinformation & Rumor Detection 9. Optimizing the future of Digital Marketing with A.I.9.1 Enhancing Interactive User Experience with AI9.2 Content Creation & Curation with AI9.3 Aligning marketing metrics with business goals9.4 Web analytics for digital marketing 10. Ethics of Artificial Intelligence for Marketing10.1 Dark side of AI in Marketing10.1.1 Consumers’ data protection rights10.1.2 Concerns about AI-enabled marketing decisions 10.1.3 Legal Concerns and Compliance issues10.2 Piracy, Security and Consumerism10.3 Ethical, Moral & Societal Challenges of AI 11. Case Studies on applications of AI11.1 AI driven cyber security and privacy11.2 Applications of AI in health care11.3 Applications of AI in tourism11.4 Applications of AI in manufacturing11.5 Applications of AI in finance
£42.49
Apress MATLAB Machine Learning Recipes
Book SynopsisChapter 1. An Overview of Machine Learning.- Chapter 2. Data Representation.- Chapter 3. MATLAB Graphics.- Chapter 4. Kalman Filters.- Chapter 5. Adaptive Control.- Chapter 6. Neural Aircraft Control.- Chapter 7. Fuzzy Logic.- Chapter 8. Classification with Neural Nets.- Chapter 9. Simple Neural Nets.- Chapter 10. Data Classification. - Chapter 11. Neural Nets with Deep Learning.- Chapter 12. Multiple Hypothesis Testing.- Chapter 13. Autonomous Driving with MHT.- Chapter 14. Case-Based Expert Systems.- Chapter 15. Spacecraft Attitude Determination Using Neural Nets. -Appendix A Brief History of Autonomous Learning.- Appendix B. Software for Autonomous Learning.
£42.49
O'Reilly Media Practical Data Science with SAP
Book SynopsisAre you using SAP ERP and eager to unlock the enormous value of its data? With this practical guide, SAP veterans Greg Foss and Paul Modderman show you how to use several data analysis tools to solve interesting problems with your SAP data.
£41.99
O'Reilly Media Analytical Skills for AI and Data Science
Book SynopsisWhile several market-leading companies have successfully transformed through data- and AI-driven approaches to business, the vast majority have yet to reap the benefits. This practical guide presents a battle-tested method to help you translate business decisions into tractable descriptive, predictive, and prescriptive problems.
£47.99
O'Reilly Media 97 Things About Ethics Everyone in Data Science
Book SynopsisBeing ethical takes constant diligence, and in many situations identifying the right choice can be difficult. In this in-depth book, contributors from top companies in technology, finance, and other industries share experiences and lessons learned from collecting, managing, and analyzing data ethically.
£29.99
O'Reilly Media Practical Fairness
Book SynopsisFairness is becoming a paramount consideration for data scientists. This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that's fair and free of bias.
£33.74
John Wiley and Sons Ltd Artifictional Intelligence: Against Humanity's
Book SynopsisRecent startling successes in machine intelligence using a technique called ‘deep learning’ seem to blur the line between human and machine as never before. Are computers on the cusp of becoming so intelligent that they will render humans obsolete? Harry Collins argues we are getting ahead of ourselves, caught up in images of a fantastical future dreamt up in fictional portrayals. The greater present danger is that we lose sight of the very real limitations of artificial intelligence and readily enslave ourselves to stupid computers: the ‘Surrender’. By dissecting the intricacies of language use and meaning, Collins shows how far we have to go before we cannot distinguish between the social understanding of humans and computers. When the stakes are so high, we need to set the bar higher: to rethink ‘intelligence’ and recognize its inherent social basis. Only if machine learning succeeds on this count can we congratulate ourselves on having produced artificial intelligence.Trade Review“In an age when heady promises and dark warnings from advocates of a fast-approaching “Technological Singularity” regularly make front-page news, this book offers timely words of caution.”J. Mark Bishop, Director of the Tungsten Centre for Intelligent Data Analytics, Goldsmiths, University of London“By highlighting artificial intelligence’s fundamental failures, Professor Collins provides an overdue correction to "the market-driven urge to advertise its successes”. Authoritative and technically accurate, this book will be essential for students of AI, policy makers, business innovators and the broader public for many years.”Alan Blackwell, Computer Laboratory, University of Cambridge“[Harry Collins examines] pervasive existential fears over artificial intelligence and its perceived threat in the ‘deep learning’ era. Collins probes this idea trenchantly and in considerable detail. Pointing to computers’ inability to factor in social context, master natural language use well enough to pass a severe Turing test, or wield embodied cognition, he argues that the real danger we face is not a takeover by superior computers, but slavery to stupid ones.”Barbara Kiser, Nature“[E]ven as a non-industry expert, Collins has still read deeply in this area, and consequently is posing some important, challenging questions. Having already experienced long periods of AI winters this book provides a robust challenge to those techno solutionist optimists who see AI-delivered solutions through overly rose-tinted glasses.”Simon Cocking, Irish Tech News“If you are looking for a balanced debate on artificial intelligence, or are engaged in a critique of deep learning, concerned with the implications of singularity on society, intrigued by the notion of equivalence of human and machine intelligence, a critical observer of automation vs augmentation debate, perplexed by the ongoing interest in Turing test, or curious about what AI narratives attract AI research funding, then this book, by a critical scholar, a reflective narrator and a far-sighted teacher, Harry Collins, is for you.”Karamjit S. Gill, AI & Society“Collins has provided a distinctive perspective to the conversation on AI.”Metascience“[P]resents some interesting questions, most notably about how an embeddedness based on layers of data abstraction may or may not map onto embeddedness in social context. […] Collins's frameworks often prove useful for questioning and analyzing what tends to be very messy data, and the book is sure to produce lively discussion among students and established scholars alike.”Sarah E. Sachs, Contemporary SociologyTable of Contents Chapter 1. Computers in Social Life and the Danger of the ‘Surrender’ Chapter 2. Expertise and Writing about AI: Some Reflections on the Project Chapter 3. Language and ‘Repair’ Chapter 4. Humans, Social Contexts and Bodies Chapter 5. Six Levels of Artificial Intelligence Chapter 6. Deep Learning: Precedent-Based, Pattern-Recognising Computers Chapter 7. Kurzweil’s Brain and the Sociology of Knowledge Chapter 8. How Humans Learn What Computers Can’t Chapter 9. Two Models of Artificial Intelligence and the Way Forward Chapter 10. The Editing Test and Other New Versions of the Turing Test Appendix 1: How the Internet Works Today Appendix 2: Little Dogs
£49.50
John Wiley and Sons Ltd Artifictional Intelligence: Against Humanity's
Book SynopsisRecent startling successes in machine intelligence using a technique called ‘deep learning’ seem to blur the line between human and machine as never before. Are computers on the cusp of becoming so intelligent that they will render humans obsolete? Harry Collins argues we are getting ahead of ourselves, caught up in images of a fantastical future dreamt up in fictional portrayals. The greater present danger is that we lose sight of the very real limitations of artificial intelligence and readily enslave ourselves to stupid computers: the ‘Surrender’. By dissecting the intricacies of language use and meaning, Collins shows how far we have to go before we cannot distinguish between the social understanding of humans and computers. When the stakes are so high, we need to set the bar higher: to rethink ‘intelligence’ and recognize its inherent social basis. Only if machine learning succeeds on this count can we congratulate ourselves on having produced artificial intelligence.Trade Review“In an age when heady promises and dark warnings from advocates of a fast-approaching “Technological Singularity” regularly make front-page news, this book offers timely words of caution.”J. Mark Bishop, Director of the Tungsten Centre for Intelligent Data Analytics, Goldsmiths, University of London“By highlighting artificial intelligence’s fundamental failures, Professor Collins provides an overdue correction to "the market-driven urge to advertise its successes”. Authoritative and technically accurate, this book will be essential for students of AI, policy makers, business innovators and the broader public for many years.”Alan Blackwell, Computer Laboratory, University of Cambridge“[Harry Collins examines] pervasive existential fears over artificial intelligence and its perceived threat in the ‘deep learning’ era. Collins probes this idea trenchantly and in considerable detail. Pointing to computers’ inability to factor in social context, master natural language use well enough to pass a severe Turing test, or wield embodied cognition, he argues that the real danger we face is not a takeover by superior computers, but slavery to stupid ones.”Barbara Kiser, Nature“[E]ven as a non-industry expert, Collins has still read deeply in this area, and consequently is posing some important, challenging questions. Having already experienced long periods of AI winters this book provides a robust challenge to those techno solutionist optimists who see AI-delivered solutions through overly rose-tinted glasses.”Simon Cocking, Irish Tech News“If you are looking for a balanced debate on artificial intelligence, or are engaged in a critique of deep learning, concerned with the implications of singularity on society, intrigued by the notion of equivalence of human and machine intelligence, a critical observer of automation vs augmentation debate, perplexed by the ongoing interest in Turing test, or curious about what AI narratives attract AI research funding, then this book, by a critical scholar, a reflective narrator and a far-sighted teacher, Harry Collins, is for you.”Karamjit S. Gill, AI & Society“Collins has provided a distinctive perspective to the conversation on AI.”Metascience“[P]resents some interesting questions, most notably about how an embeddedness based on layers of data abstraction may or may not map onto embeddedness in social context. […] Collins's frameworks often prove useful for questioning and analyzing what tends to be very messy data, and the book is sure to produce lively discussion among students and established scholars alike.”Sarah E. Sachs, Contemporary SociologyTable of Contents Chapter 1. Computers in Social Life and the Danger of the ‘Surrender’ Chapter 2. Expertise and Writing about AI: Some Reflections on the Project Chapter 3. Language and ‘Repair’ Chapter 4. Humans, Social Contexts and Bodies Chapter 5. Six Levels of Artificial Intelligence Chapter 6. Deep Learning: Precedent-Based, Pattern-Recognising Computers Chapter 7. Kurzweil’s Brain and the Sociology of Knowledge Chapter 8. How Humans Learn What Computers Can’t Chapter 9. Two Models of Artificial Intelligence and the Way Forward Chapter 10. The Editing Test and Other New Versions of the Turing Test Appendix 1: How the Internet Works Today Appendix 2: Little Dogs
£15.99
John Wiley and Sons Ltd An Introduction to Communication and Artificial
Book SynopsisCommunication and artificial intelligence (AI) are closely related. It is communication – particularly interpersonal conversational interaction – that provides AI with its defining test case and experimental evidence. Likewise, recent developments in AI introduce new challenges and opportunities for communication studies. Technologies such as machine translation of human languages, spoken dialogue systems like Siri, algorithms capable of producing publishable journalistic content, and social robots are all designed to communicate with users in a human-like way. This timely and original textbook provides educators and students with a much-needed resource, connecting the dots between the science of AI and the discipline of communication studies. Clearly outlining the topic's scope, content and future, the text introduces key issues and debates, highlighting the importance and relevance of AI to communication studies. In lively and accessible prose, David Gunkel provides a new generation with the information, knowledge, and skills necessary to working and living in a world where social interaction is no longer restricted to humans. The first work of its kind, An Introduction to Communication and Artificial Intelligence is the go-to textbook for students and scholars getting to grips with this crucial interdisciplinary topic.Trade Review“Gunkel’s book is an accessible but technically savvy monograph introducing students and scholars of communication and computer science to the intersections between AI and communication. … Gunkel’s book will also be a particularly useful resource to instructors, not only due to its accessible language and wide- reaching scope, but also thanks to the five ‘Maker exercises’ included in the last section. These provide useful entry points for students that are not versed in computer programming for experimenting with simple computer programs.”Communication Theory “An introduction to communication and artificial intelligence aims and succeeds in making sense of AI for students and scholars in social sciences.”CommunicationsTable of ContentsPreface Part I: Introduction and Orientation 1 Introduction 2 Communication and AI 3 Basic Concepts and Terminology Part II: Applications 4 Machine Translation 5 Natural Language Processing 6 Computational Creativity 7 Social Robots Part III: Impact and Consequences 8 Social Issues 9 Social Responsibility and Ethics Part IV: Maker Exercises Introduction Exercise 1 – Demystifying ELIZA Exercise 2 – Algorithms Exercise 3 – Machine Translation Exercise 4 – Chatbot and Quasi-Loebner Prize Exercise 5 – Template NLG Notes References Index
£17.09
Business Expert Press A.I. and Remote Working: A Paradigm Shift in
Book SynopsisThe world of work is undergoing the most significant change since the Industrial revolution. Cognitive A.I. is driving world change faster than at any time in history. There are massive advantages for employers who act and act quickly. At precisely the same time, COVID has been a wake-up call. Organizations have discovered that they employ too many people, and the realization – many can be more productive working remotely. Productivity increases, reduction in office space and management are all being actioned through home working.A significant study on Homeworkers indicates that worldwide, 1 in 5 will be working from home. Already many Global companies have announced this year plans to reduce office space by 40%. Productivity results that have been realized from remote working have exceeded expectations, which will accelerate.This innovative book will guide you through A.I., how it will affect employment and existing processes, and what the employer and employee can expect in the new and rapidly changing world of work.
£21.80
now publishers Inc Convex Optimization for Machine Learning
Book SynopsisThis book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal of the book is tohelp develop a sense of what convex optimization is, and how it can be used in a widening array of practical contexts with a particular emphasis on machine learning.The first part of the book covers core concepts of convex sets, convex functions, and related basic definitions that serve understanding convex optimization and its corresponding models. The second part deals with one very useful theory, called duality, which enables us to: (1) gain algorithmic insights; and (2) obtain an approximate solution to non-convex optimization problems which are often difficult to solve. The last part focuses on modern applications in machine learning and deep learning.A defining feature of this book is that it succinctly relates the “story” of how convex optimization plays a role, via historical examples and trending machine learning applications. Another key feature is that it includes programming implementation of a variety of machine learning algorithms inspired by optimization fundamentals, together with a brief tutorial of the used programming tools. The implementation is based on Python, CVXPY, and TensorFlow. This book does not follow a traditional textbook-style organization, but is streamlined via a series of lecture notes that are intimately related, centered around coherent themes and concepts. It serves as a textbook mainly for a senior-level undergraduate course, yet is also suitable for a first-year graduate course. Readers benefit from having a good background in linear algebra, some exposure to probability, and basic familiarity with Python.Trade ReviewThe topic is surely still of great interest, since courses on Convex Optimization, in conjunction or not with Machine Learning applications, are ubiquitous in Engineering curricula around the world. What appears as somewhat novel here is the juxtaposition of Part I and II on convex optimization and duality with Part III on machine learning applications. The emphasis on Python, TensorFlow etc. is also practically very important and surely appreciated by the students, especially if presented via challenging practical problems. More than completeness, I believe that what is important is that the book gives a meaningful “cut” through these topics, as this books appears to do. It seems important that the author tries to motivate and link together as much as possible part III with the previous parts, explaining why part I and II are important for part III, but also highlighting what the limits of convex models are and at which point they need be superseded by more general models. Giuseppe Carlo Calafiore, Professor at the Politecnico di Torino, Italy, and visiting Professor at UC Berkeley -- Giuseppe Carlo CalafioreI have looked at the manuscript and my impression is positive, the aims and scope are actual and comprehensive. The intended audience is senior undergraduates and early graduate, which differs the book significantly from several competing books , and this should be an advantage. I would say that a good senior undergraduate level textbook on convex optimization would, in my opinion, be very timely. Arkadi Nemirovski, Georgia Tech, USA -- Arkadi NemirovskiTable of Contents Preface 1 Convex Optimization Basics 1.1 Overview of the book 1.2 Definition of convex optimization 1.3 Tractability of convex optimization and gradient descent 1.4 Linear Program 1.5 Least Squares 1.6 Test error, regularization and CVXPY implementation 1.7 Computed tomography 1.8 Quadratic program 1.9 Second-order cone program 1.10 Semi-definite program 1.11 SDP relaxation 1.12 Problem Sets 2 Duality 2.1 Strong duality 2.2 Interior point method 2.3 Proof of strong duality theorem 2.4 Weak duality 2.5 Lagrange relaxation for Boolean problems 2.6 Lagrange relaxation for the MAXCUT problem 2.7 Problem Sets 3 Machine Learning Applications 3.1 Supervised learning and optimization 3.2 Logistic regression 3.3 Deep learning 3.4 Deep learning II 3.5 DL: TensorFlow implementation 3.6 Unsupervised Learning: Generative modeling 3.7 Generative Adversarial Networks (GANs) 3.8 GANs: TensorFlow implementation 3.9 Wasserstein GAN 3.10 Wasserstein GAN II 3.11 Wasserstein GAN: TensorFlow implementation 3.12 Fair machine learning 3.13 A fair classifier and its connection to GANs 3.14 A fair classifier: TensorFlow implementation Appendices
£109.25
Now Publishers Differential Privacy in Artificial Intelligence From Theory to Practice
Book SynopsisThis book delves into the theoretical underpinnings of differential privacy, its use in machine learning systems, practical implementation details, and its broader social and legal ramifications
£95.00
Information Age Publishing Transforming Healthcare with Big Data and AI
Book SynopsisHealthcare and technology are at a convergence point where significant changes are poised to take place. The vast and complex requirements of medical record keeping, coupled with stringent patient privacy laws, create an incredibly unwieldy maze of health data needs. While the past decade has seen giant leaps in AI, machine learning, wearable technologies, and data mining capacities that have enabled quantities of data to be accumulated, processed, and shared around the globe. Transforming Healthcare with Big Data and AI examines the crossroads of these two fields and looks to the future of leveraging advanced technologies and developing data ecosystems to the healthcare field. This book is the product of the Transforming Healthcare with Data conference, held at the University of Southern California. Many speakers and digital healthcare industry leaders contributed multidisciplinary expertise to chapters in this work. Authors’ backgrounds range from data scientists, healthcare experts, university professors, and digital healthcare entrepreneurs. If you have an understanding of data technologies and are interested in the future of Big Data and A.I. in healthcare, this book will provide a wealth of insights into the new landscape of healthcare.
£44.96
Information Age Publishing Transforming Healthcare with Big Data and AI
Book SynopsisHealthcare and technology are at a convergence point where significant changes are poised to take place. The vast and complex requirements of medical record keeping, coupled with stringent patient privacy laws, create an incredibly unwieldy maze of health data needs. While the past decade has seen giant leaps in AI, machine learning, wearable technologies, and data mining capacities that have enabled quantities of data to be accumulated, processed, and shared around the globe. Transforming Healthcare with Big Data and AI examines the crossroads of these two fields and looks to the future of leveraging advanced technologies and developing data ecosystems to the healthcare field. This book is the product of the Transforming Healthcare with Data conference, held at the University of Southern California. Many speakers and digital healthcare industry leaders contributed multidisciplinary expertise to chapters in this work. Authors’ backgrounds range from data scientists, healthcare experts, university professors, and digital healthcare entrepreneurs. If you have an understanding of data technologies and are interested in the future of Big Data and A.I. in healthcare, this book will provide a wealth of insights into the new landscape of healthcare.
£82.80
Information Age Publishing AI Smart Kit: Agile Decision-Making on AI
Book SynopsisThere are many myths about Artificial Intelligence (AI) relating to what it is and what it can and cannot do. The people making decisions on AI projects are often not technologically savvy and unable to find easy answers. The spending on and the returns from AI projects are not necessarily straightforward. Part of the reason for this is the lack of understanding of the impact of critical decision criteria. AI touches on many ethical concepts - data privacy, validity, and, more importantly, its potential misuse. AI often replaces human decision-making, as managers do not clearly understand the implications of those choices. This book provides an easy and accessible guide for practitioners without a technological background to understand AI. It guides the reader through the fundamental issues confronting decision-makers. It offers advice on 'how to ask relevant questions' using the 15 decision scales. There is currently no comparable book on the market that acts as a pocketbook management reference guide for the AI layman.Table of Contents What is AI? AI Manager's Dilemma AI Smart Kit Scales Scale 1: AI Expertise Level Scale 2: AI Interoperability Level Scale 3: AI and Global Embeddedness Scale 4: AI Data Types Scale 5: AI Data Management Scale 6: AI and Human Teams Scale 7: AI and Human Productivity Scale 8: AI Onboarding Scale 9: AI and the Sensory Experience Scale 10: AI and the Human Interface Scale 11: AI and Regulations Scale 12: AI and IP Scale 13: AI and Impact on Sustainable Development Scale 14: AI and Accountability Scale 15: AI and Crises
£34.15
Information Age Publishing AI Smart Kit: Agile Decision-Making on AI
Book SynopsisThere are many myths about Artificial Intelligence (AI) relating to what it is and what it can and cannot do. The people making decisions on AI projects are often not technologically savvy and unable to find easy answers. The spending on and the returns from AI projects are not necessarily straightforward. Part of the reason for this is the lack of understanding of the impact of critical decision criteria. AI touches on many ethical concepts - data privacy, validity, and, more importantly, its potential misuse. AI often replaces human decision-making, as managers do not clearly understand the implications of those choices. This book provides an easy and accessible guide for practitioners without a technological background to understand AI. It guides the reader through the fundamental issues confronting decision-makers. It offers advice on 'how to ask relevant questions' using the 15 decision scales. There is currently no comparable book on the market that acts as a pocketbook management reference guide for the AI layman.Table of Contents What is AI? AI Manager's Dilemma AI Smart Kit Scales Scale 1: AI Expertise Level Scale 2: AI Interoperability Level Scale 3: AI and Global Embeddedness Scale 4: AI Data Types Scale 5: AI Data Management Scale 6: AI and Human Teams Scale 7: AI and Human Productivity Scale 8: AI Onboarding Scale 9: AI and the Sensory Experience Scale 10: AI and the Human Interface Scale 11: AI and Regulations Scale 12: AI and IP Scale 13: AI and Impact on Sustainable Development Scale 14: AI and Accountability Scale 15: AI and Crises
£61.75
Information Age Publishing A Primer on Business Analytics: Perspectives from
Book SynopsisThis book will provide a comprehensive overview of business analytics, for those who have either a technical background (quantitative methods) or a practitioner business background. Business analytics, in the context of the 4th Industrial Revolution, is the "new normal" for businesses that operate in this digital age. This book provides a comprehensive primer and overview of the field (and related fields such as Business Intelligence and Data Science). It will discuss the field as it applies to financial institutions, with some minor departures to other industries. Readers will gain understanding and insight into the field of data science, including traditional as well as emerging techniques. Further, many chapters are dedicated to the establishment of a data-driven team – from executive buy-in and corporate governance to managing and quantifying the return of data-driven projects.
£44.96
Information Age Publishing A Primer on Business Analytics: Perspectives from
Book SynopsisThis book will provide a comprehensive overview of business analytics, for those who have either a technical background (quantitative methods) or a practitioner business background. Business analytics, in the context of the 4th Industrial Revolution, is the "new normal" for businesses that operate in this digital age. This book provides a comprehensive primer and overview of the field (and related fields such as Business Intelligence and Data Science). It will discuss the field as it applies to financial institutions, with some minor departures to other industries. Readers will gain understanding and insight into the field of data science, including traditional as well as emerging techniques. Further, many chapters are dedicated to the establishment of a data-driven team – from executive buy-in and corporate governance to managing and quantifying the return of data-driven projects.
£82.80
Academica Press Artificial Intelligence: A Dependent Legal Person
Book SynopsisJo Bac’s groundbreaking legal study asks why and how the United States legal system should grant legal personhood to artificial intelligence (AI). This new legal status of AI is visualized as a dependent person, and the AI dependent legal person would be determined by an inextricable connection between AI and a new type of corporate body, introduced here as “AI-Human Amalgamation” (AI-HA).Artificial Intelligence has been defined as one or more computer programs with an ability to create work that is unforeseen by humans. This includes AI capacity to generate unforeseen innovations, patentable inventions, and/or infringe the rights of other patent holders. At present, AI is an entity unrecognized by law. The fact that AI is neither a natural nor a legal person indicates that it cannot be considered the owner of rights or bearer of liabilities. This in turn creates tension both in society and legal systems because questions such as who should hold the rights of AI or be liable for autonomous acts of AI remain unanswered.This book dynamically argues that the AI dependent legal person and AI-HA are necessary to address these new challenges. The creativity and actions of AI and AI-HA would be distinct from those performed by human beings involved in the creation of this amalgamation, such as AI’s operators or programmers. As such, this structure would constitute an amalgamation based on human beings and AI cooperation (AI-HA). As a dependent legal person, AI would hold the patent rights to its own inventions, thus ensuring favorable conditions for the incentives of the U.S. patent system. In addition, the proposed legal framework with the use of legislative instruments could address any liability concerns arising from foreseen and unforeseen actions, omissions, and AI’s failure to act.
£201.00
Arcler Education Inc Chatbots and Text Generation
Book SynopsisChatbots such as ChatGPT are based on language models developed by using AI techniques. They are based on the Generative Pre-training Transformer (GPT) architecture and are trained on a huge amount of text data. This book edition covers different topics from chatbots and text generation, including: chatbots functioning, chatbot applications, implementation of text generation models and text generation applications.Table of Contents Section 1 Chatbot Functioning Chapter 1 Development of an E-Commerce Chatbot for a University Shopping Mall Chapter 2 Factors Affecting Consumers Adoption of AI-Based Chatbots: The Role of Anthropomorphism Chapter 3 A Novel Framework for Arabic Dialect Chatbot Using Machine Learning Chapter 4 Framework for Educational Domain-Based Multichatbot Communication System Chapter 5 Emerging Technologies of Natural Language-Enabled Chatbots: A Review and Trend Forecast Using Intelligent Ontology Extraction and Patent Analytics Section 2 Chatbot Functioning Chapter 6 A Multi-Industry Analysis of the Future Use of AI Chatbots Chapter 7 Vik: A Chatbot to Support Patients with Chronic Diseases Chapter 8 A Smart Chatbot for Interactive Management in Beta Thalassemia Patients Chapter 9 Multi-Chatbot or Single-Chatbot? The Effects of M-Commerce Chatbot Interface on Source Credibility, Social Presence, Trust, and Purchase Intention Chapter 10 Development of NLP-Integrated Intelligent Web System for E-Mental Health Section 3 Implementing Text Generation Chapter 11 Enhancing Text Generation Via Parse Tree Embedding Chapter 12 Research and Implementation of Text Generation Based on Text Augmentation and Knowledge Understanding Chapter 13 An Integrated Deep Generative Model for Text Classification and Generation Chapter 14 Rapid Text Retrieval and Analysis Supporting Latent Dirichlet Allocation Based on Probabilistic ModelsSection 4 Text Generation Applications Chapter 15 The Automatic Question Generation System for CET Chapter 16 Exploration of Cross-Modal Text Generation Methods in Smart Justice Chapter 17 Feature Extraction and Intelligent Text Generation of Digital Music
£158.40
Arcler Education Inc Generative AI Models
Book SynopsisThe generative AI is especially powerful in several areas, such as text generation (product description, article writing), image and video generation (AI-generated pictures and videos for marketing industry), and voice and sound generation (for film industry). This book edition covers different topics of generative AI models, including: image generation techniques, video generation techniques, speech / voice generation techniques, and societal and ethical issues of these models.Table of ContentsSection 1 Image Generation TechniquesChapter 1 Research on Image Generation and Style Transfer Algorithm Based on Deep LearningChapter 2 An Overview of Image Caption Generation MethodsChapter 3 Application of an Improved DCGAN for Image GenerationChapter 4 Private Face Image Generation Method Based on Deidentification in Low LightChapter 5 Application of Remote Sensing Image Data Scene Generation Method in Smart CitySection 2 Video Generation TechniquesChapter 6 Realistic Speech-Driven Talking Video Generation with Personalized PoseChapter 7 Video Transformation in Big Video Era and its Impact on Content EditingChapter 8 A Fast Depth-Map Generation Algorithm Based on Motion Search from 2D Video ContentsChapter 9 Adaptive Content Management for UGC Video Delivery in Mobile Internet EraSection 3 Voice and Speech GenerationChapter 10 Generating the Voice of the Interactive Virtual AssistantChapter 11 Voice Quality Modelling for Expressive Speech SynthesisChapter 12 Prosodically Rich Speech Synthesis Interface Using Limited Data of Celebrity VoiceChapter 13 Resources for Development of Hindi Speech Synthesis System: An OverviewSection 4 Societal and Ethical IssuesChapter 14 How AI-Human Symbiotes May Reinvent Innovation and What the New Centaurs Will Mean for CitiesChapter 15 AI, Automation and New JobsChapter 16 Discussion on the Development of Artificial Intelligence in TaxationChapter 17 AI and Zen: AI Films as Reflections on Reality and IllusionChapter 18 Ecologically Sound Procedural Generation of Natural Environments
£168.30
Collective Ink Is Intelligence an Algorithm?
Book SynopsisHow do we understand the world around us? How do we solve problems? Often the answer to these questions follows a certain pattern, an algorithm if you wish. This is the case when our analytical left-brain side is at work. However, there are also elements in our behaviour where intelligence appears to follow a more elusive path, which cannot easily be characterised as a specific sequence of steps. Is Intelligence an Algorithm? offers an insight into intelligence as it functions in nature, like human or animal intelligence, but also sheds light on modern developments in the field of artificial intelligence, proposing further architectural solutions for the creation of a so-called global Webmind.
£11.99
Edward Elgar Publishing Ltd Elgar Companion to Regulating AI and Big Data in
Book SynopsisCommitted to highlighting the regulatory needs and priorities of emerging economies in the context of AI and big data, this expertly crafted Companion explores the nature and role of regulation in the Global South from a techno-dependent societal perspective. It not only amplifies the unspoken and underrepresented voices in AI and data regulation scholarly discourse, but also provides a novel approach to otherwise recipient economies in an age of digital transformation.Covering central themes such as regulatory flows, self-regulation and AI ethics, contextual regulation, and regulatory devices, the Companion brings together an array of eminent academics from across the globe. Chapters critically reflect on the nature and role of regulation, charting the tapestry of regulatory influence and capacity, values, and relationships of dependence and vulnerability attendant on advancing AI and mass data sharing. The regulatory challenges facing emerging economies and post-colonial societies are examined, and contributors engage new frames of thinking and solutions from perspectives beyond the interests of techno-colonialism.International and interdisciplinary in scope, this Companion will be an interesting read for academics and students in development studies, law and development, innovation and technology studies, and regulation and governance.Table of ContentsContents : Introduction to the Elgar Companion to Regulating AI and Big Data in Emergent Economies 1 Mark Findlay, Li Min Ong and Wenxi Zhang PART I EDITORS’ REFLECTIONS: REGULATORY FLOWS 1 The ongoing AI-regulation debate in the EU and its influence on the emergent economies – a new case for the ‘Brussels Effect’? 22 Shu Li, Béatrice Schütte and Suvi Sankari 2 Challenges and opportunities of ethical AI and digital technology use in emerging economies 42 Meera Sarma, Chaminda Senaratne and Thomas Matheus 3 Private-public data governance in Indonesia’s smart cities: promises and pitfalls 59 Berenika Drazewska PART II EDITORS’ REFLECTIONS: SELF-REGULATION AND AI ETHICS 4 The challenges of industry self-regulation of AI in emerging economies: implications of the case of Russia for public policy and institutional development 81 Gleb Papyshev and Masaru Yarime 5 The place of the African relational and moral theory of Ubuntu in the global artificial intelligence and big data discussion: critical reflections 99 Beatrice Okyere-Manu 6 The values of an AI ethical framework for a developing nation: considerations for Malaysia 115 Jaspal Kaur Sadhu Singh PART III EDITORS’ REFLECTIONS: CONTEXTUAL REGULATION 7 The relevance of culture in regulating AI and big data: the experience of the Macao SAR 138 Sara Migliorini and Rostam J. Neuwirth 8 Digital self-determination: an alternative paradigm for emerging economies 158 Wenxi Zhang, Li Min Ong and Mark Findlay PART IV EDITORS’ REFLECTIONS: REGULATORY DEVICES 9 Regulating AI in democratic erosion: context, imaginaries and voices in the Brazilian debate 183 Clara Iglesias Keller and João Carlos Magalhães 10 The importance and challenges of developing a regulatory agenda for AI in Latin America 201 Armando Guio Español, María Antonia Carvajal, Elena Tamayo Uribe and María Isabel Mejía 11 Artificial intelligence: dependency, coloniality and technological subordination in Brazil 228 Joyce Souza and Rodolfo Avelino Conclusion: reflecting on the ‘new’ North/South 245 Mark Findlay, Li Min Ong and Wenxi Zhang Index 259
£140.00
John Wiley & Sons Inc New Challenges for Knowledge: Digital Dynamics to
Book SynopsisDigital technologies are reshaping every field of social and economic lives, so do they in the world of scientific knowledge. “The New Challenges of Knowledge” aims at understanding how the new digital technologies alter the production, diffusion and valorization of knowledge. We propose to give an insight into the economical, geopolitical and political stakes of numeric in knowledge in different countries. Law is at the center of this evolution, especially in the case of national and international confusion about Internet, Science and knowledge.Trade Review“Sharing economy models are rippling through the world of scientific knowledge and research; open access brings challenges for developers, researchers, and policy makers – all treated here in the context of law-making” The Magpi, issue 60, Aug 2017Table of ContentsIntroduction . xiii Part 1. Production: Global Knowledge and Science in the Digital Era 1 Chapter 1. Current Knowledge Dynamics 3 1.1. Transparency of scientific data 4 1.2. Transparency of experimental protocol 6 1.3. A necessary form of research engineering 7 1.4. Confusion between data and scientific results: avoiding manipulation of research results 8 Chapter 2. Digital Conditions for Knowledge Production 11 2.1. An economic system oriented toward innovation 11 2.2. What of knowledge and indeed the concept of the commons? 13 2.3. From analog to digital 14 2.4. User–producer: civil society enters the knowledge production system 16 2.5. The interactions between the various spheres of knowledge production 18 2.6. Collaboration between society and knowledge: producing authorities should be put into perspective 20 Chapter 3. The Dual Relationship between the User and the Developer 23 3.1. Legal arrangements for knowledge-sharing using development platforms 23 3.2. The user contributes to the creation and development of content process 25 Chapter 4. Researchers’ Uses and Needs for Scientific and Technical Information 29 4.1. The CNRS survey 29 4.2. Diverse uses and dual needs 31 4.3. An explanation through differentiated scientific analysis 33 Chapter 5. New Tools for Knowledge Capture 37 5.1. The growth of metadata exploitation 37 5.2. Are we moving toward a semantic Web? 38 5.3. Tools and limits for metadata processing 39 5.4. The challenges of the semantic Web 40 Chapter 6. Modes of Knowledge Sharing and Technologies 43 6.1. Data storage technologies and access allowing knowledge sharing 43 6.2. Exchange platforms and catalogs 44 6.3. Knowledge-processing and digital editions 45 Part 2. Sharing Mechanisms: Knowledge Sharing and the Knowledge-based Economy 47 Chapter 7. Business Model for Scientific Publication 49 7.1. The current economic model is changing so as to adapt to new conditions for knowledge sharing 49 7.2. Creation of a new model 51 7.3. The issues raised by the creation of a new economic model 52 7.4. A new economic model struggling to fine its niche 54 Chapter 8. Actor Strategy: International Scientific Publishing, Services with High Added Value and Research Communities 57 8.1. Publishing, editing and existing: live issues within the publication of Scientific and Technical Information (STI) 58 8.2. Who is subject to it? The other players in scientific publishing 59 8.3. The characteristics of SMS (Science of Man and Society) 60 8.4. Existing without publishing? New STI directions 62 8.5. Alternatives to scientific publishing 63 Chapter 9. New Approaches to Scientific Production 67 9.1. New means of access to scientific production: innovative models 67 9.2. Two main objectives: accelerating knowledge sharing and promoting scientific collaboration 71 9.3. The need for new analytical tools and the risk of reprivatization of scientific knowledge. 72 9.4. The absence of the usage doctrine and the risk of reprivatization of science: the case of social networks 74 Chapter 10. The Geopolitics of Science 77 10.1. National convergent research models 78 10.2. Science is a source of international cooperation 81 10.3. International scientific cooperation is accelerating 84 Chapter 11. Copyright Serving the Market 85 Part 3. Enhancement Knowledge Rights and Public Policies in the Wake of Digital Technology 89 Chapter 12. Legal Protection of Scientific Research Results in the Humanities and Social Sciences 91 12.1.Different legal protections for different kinds of science 91 12.2. Why protect? 92 12.3. How to protect 93 12.4. Protect against whom? 98 12.5. Changing the challenges of Internet protection 99 12.6. Legal obstacles related to the author’s right 100 Chapter 13. Development of Knowledge and Public Policies 103 13.1. Knowledge enhancement concerns everyone 104 13.2. What are the public policies for enhancing knowledge? 105 13.3. State establishment of connections between actors: a key tool in knowledge enhancement 107 13.4. Comparing the United States and the European Union 109 Chapter 14. From Author to Enhancer 111 14.1. Enhancing scientific research is a complex process 112 14.2. Scientific research enhancement follows a legislative framework intended to promote innovation 114 Chapter 15. The Right to Knowledge: Moving Toward a Universal Law? 117 15.1. Unclear regulatory frameworks 118 15.2. Developing legal frameworks related to the Internet is complicated 121 15.3. Proposals for developing legal frameworks for the Internet 123 Chapter 16. Governing by Algorithm 127 16.1. Statistics that foreshadow algorithms 128 16.2. Algorithmic governance and democratic opportunities 130 Chapter 17. Public Data and Science in e-Government 133 17.1. Disseminating data and disseminating science: a new requirement 134 17.2. Public data in the e-government 137 17.3. Science within e-government 139 Chapter 18. Surveillance, Sousveillance, Improper Capturing 141 18.1. The traditional legal framework for information capture 142 18.2. The clear need for a specific law 145 Chapter 19. Public Knowledge Policies in the Digital Age 149 19.1. GAFA domination and the oligopolization of the market 150 19.2. Isolated digital ecosystems 152 19.3. Regulation through competition law 153 19.4. Data protection: moving toward a law for the digital community 154 Chapter 20. The Politics of Creating Artificial Intelligence 157 20.1. History 158 20.2. Artificial intelligence has become a priority for public and private actors 160 20.4. The appearance of legal problems 162 Chapter 21. Security Policies in Artificial Intelligence 165 21.1. Security as a comment on machines and data 166 21.2. From the security of machines to the security of humans 169 Conclusion 175 Postscript 177 Glossary 179 Bibliography 185 Index 201
£125.06
ISTE Ltd and John Wiley & Sons Inc Virtual Reality and Augmented Reality: Myths and
Book SynopsisVirtual and Augmented Reality have existed for a long time but were stuck to the research world or to some large manufacturing companies. With the appearance of low-cost devices, it is expected a number of new applications, including for the general audience. This book aims at making a statement about those novelties as well as distinguishing them from the complexes challenges they raise by proposing real use cases, replacing those recent evolutions through the VR/AR dynamic and by providing some perspective for the years to come.Table of ContentsPreface xi Introduction xvBruno ARNALDI, Pascal GUITTON and Guillaume MOREAU Chapter 1. New Applications 1Bruno ARNALDI, Stéphane COTIN, Nadine COUTURE, Jean-Louis DAUTIN, Valérie GOURANTON, François GRUSON and Domitile LOURDEAUX 1.1. New industrial applications 1 1.1.1. Virtual reality in industry 1 1.1.2. Augmented reality and industrial applications 3 1.1.3. VR-AR for industrial renewal 4 1.1.4. And what about augmented reality? 12 1.2. Computer-assisted surgery 14 1.2.1. Introduction 14 1.2.2. Virtual reality and simulation for learning 16 1.2.3. Augmented reality and intervention planning 21 1.2.4. Augmented reality in surgery 26 1.2.5. Current conditions and future prospects 31 1.3. Sustainable cities 32 1.3.1. Mobility aids in an urban environment 33 1.3.2. Building and architecture 37 1.3.3. Cities and urbanism 41 1.3.4. Towards sustainable urban systems 46 1.4. Innovative, integrative and adaptive societies 48 1.4.1. Education 48 1.4.2. Arts and cultural heritage 54 1.4.3. Conclusion 60 1.5. Bibliography 61 Chapter 2. The Democratization of VR-AR 73Sébastien KUNTZ, Richard KULPA and Jérôme ROYAN 2.1. New equipment 73 2.1.1. Introduction 73 2.1.2. Positioning and orientation devices 74 2.1.3. Restitution devices 82 2.1.4. Technological challenges and perspectives 100 2.1.5. Conclusions on new equipment 109 2.2. New software 111 2.2.1. Introduction 111 2.2.2. Developing 3D applications 113 2.2.3. Managing peripheral devices 116 2.2.4. Dedicated VR-AR software solutions 119 2.2.5. Conclusion 120 2.3. Bibliography 121 Chapter 3. Complexity and Scientific Challenges 123Ferran ARGELAGUET SANZ, Bruno ARNALDI, Jean-Marie BURKHARDT, Géry CASIEZ, Stéphane DONIKIAN, Florian GOSSELIN, Xavier GRANIER, Patrick LE CALLET, Vincent LEPETIT, Maud MARCHAL, Guillaume MOREAU, Jérôme PERRET and Toinon VIGIER 3.1. Introduction: complexity 123 3.1.1. Physical model and detecting collisions 124 3.1.2. Populating 3D environments: single virtual human to a surging crowd 130 3.1.3. The difficulty of making 3D interaction natural 137 3.1.4. The difficulty of synthesizing haptic feedback 141 3.2. The real–virtual relationship in augmented reality 150 3.2.1. Acquisition and restitution equipment 151 3.2.2. Pose computation 152 3.2.3. Realistic rendering 156 3.3. Complexity and scientific challenges of 3D interaction 158 3.3.1. Introduction 158 3.3.2. Complexity and challenges surrounding the 3D interaction loop 158 3.3.3. Challenge 1: sensory-motor actions for interaction 159 3.3.4. Challenge 2: multisensory feedback 163 3.3.5. Challenge 3: users and perception 166 3.3.6. Conclusion 167 3.4. Visual perception 168 3.4.1. A glossary of terms related to unease, fatigue and physical discomfort 168 3.4.2. Display factors 173 3.4.3. Conclusion 179 3.5. Evaluation 179 3.5.1. Objectives and scope of this section 179 3.5.2. Evaluation: a complex problem 180 3.5.3. Evaluation using studies with human subjects 184 3.5.4. Drawbacks to overcome 193 3.5.5. Evolutions in measuring performance and behavior, characterizing participants 195 3.5.6. Conclusion and perspectives 200 3.6. Bibliography 201 Chapter 4. Towards VE that are More Closely Related to the Real World 217Géry CASIEZ, Xavier GRANIER, Martin HACHET, Vincent LEPETIT, Guillaume MOREAU and Olivier NANNIPIERI 4.1. “Tough” scientific challenges for AR 218 4.1.1. Choosing a display device . 218 4.1.2. Spatial localization 221 4.2. Topics in AR that are rarely or never approached 223 4.2.1. Introduction 223 4.2.2. Hybridization through a screen or HMD 224 4.3. Spatial augmented reality 227 4.3.1. Hybridization of the real world and the virtual world 227 4.3.2. Current evolutions 228 4.4. Presence in augmented reality . 229 4.4.1. Is presence in reality the model for presence in virtual environments? 229 4.4.2. Mixed reality: an end to the real versus virtual binary? 231 4.4.3. From mixed reality to mixed presence 231 4.4.4. Augmented reality: a total environment 232 4.5. 3D interaction on tactile surfaces 233 4.5.1. 3D interaction 234 4.5.2. 3D interaction on tactile surfaces 236 4.6. Bibliography 240 Chapter 5. Scientific and Technical Prospects 247Caroline BAILLARD, Philippe GUILLOTEL, Anatole LÉCUYER, Fabien LOTTE, Nicolas MOLLET, Jean-Marie NORMAND and Gaël SEYDOUX 5.1. The promised revolution in the field of entertainment 247 5.1.1. Introduction 247 5.1.2. Defining a new, polymorphic immersive medium 248 5.1.3. Promised experiences 251 5.1.4. Prospects 255 5.2. Brain-computer interfaces 258 5.2.1. Brain-computer interfaces: introduction and definitions 258 5.2.2. What BCIs cannot do 260 5.2.3. Working principle of BCIs . 261 5.2.4. Current applications of BCIs 263 5.2.5. The future of BCIs 268 5.3. Alternative perceptions in virtual reality 269 5.3.1. Introduction 269 5.3.2. Pseudo-sensory feedback 271 5.3.3. Alternative perception of movement 275 5.3.4. Altered perception of one’s body 278 5.3.5. Conclusion 283 5.4. Bibliography 284 Chapter 6. The Challenges and Risks of Democratization of VR-AR 289Philippe FUCHS 6.1. Introduction 289 6.2. Health and comfort problems 292 6.2.1. The different problems 292 6.2.2. Sensorimotor incoherences . 293 6.3. Solutions to avoid discomfort and unease 297 6.3.1. Presentation of the process . 297 6.3.2. Mitigation of the impact on visuo-vestibular incoherence 297 6.3.3. Removing visuo-vestibular incoherence by modifying the functioning of the interaction paradigm 298 6.3.4. Removing visuo-vestibular incoherence by modifying interfaces 299 6.3.5. Levels of difficulty in adapting 299 6.4. Conclusion 300 6.5. Bibliography 301 Conclusion 303Bruno ARNALDI, Pascal GUITTON and Guillaume MOREAU Postface 309Bruno ARNALDI, Pascal GUITTON and Guillaume MOREAU Glossary 315 List of Authors 317 Index 321
£128.66
ISTE Ltd and John Wiley & Sons Inc Metaheuristics for Portfolio Optimization: An
Book SynopsisThe book is a monograph in the cross disciplinary area of Computational Intelligence in Finance and elucidates a collection of practical and strategic Portfolio Optimization models in Finance, that employ Metaheuristics for their effective solutions and demonstrates the results using MATLAB implementations, over live portfolios invested across global stock universes. The book has been structured in such a way that, even novices in finance or metaheuristics should be able to comprehend and work on the hybrid models discussed in the book.Table of Contents1. A Brief Primer on Metaheuristics. 2. Heuristic Portfolio Selection. 3. Risk Budgeted Portfolio Optimization. 4. Heuristic Optimization of Equity Market Neutral Portfolios. 5. Metaheuristic 130-30 Portfolio Construction. 6. Metaheuristic Portfolio Rebalancing with Transaction Costs.
£125.06
ISTE Ltd and John Wiley & Sons Inc Beyond Artificial Intelligence: From Human
Book SynopsisThis book will present a complete modeling of the human psychic system that allows to generate the thoughts in a strictly organizational approach that mixes a rising and falling approach. The model will present the architecture of the psychic system that can generate sensations and thoughts, showing how one can feel thoughts. The model developed into an organizational architecture based on massive multiagent systems. The architecture will be fully developed, showing how an artificial system can be endowed with consciousness and intentionally generate thoughts and, especially, feel them. These results are multidisciplinary, combining both psychology and computer science disciplines.Table of ContentsTable of Definitions vii Introduction ix Chapter 1. The Organizational Architecture of the Psychic System and the Feeling of Thinking 1 1.1. The problem of the study of thought 2 1.2. The interpretation of neuronal aggregates 5 1.3. The function of the architecture of the Freudian model 7 1.4. The specific characteristics of the components of the system using a constructivist approach 14 1.5. The systemic layer and the regulators 26 1.6. The mental landscape 35 1.7. The feeling of thinking and the general organizational principle 45 1.8. The aim and the space of the regulators 55 1.9. The attractors 67 1.10. The generation of a representation 74 1.11. Unification between regulators and neuronal aggregates: the morphological model of the generating forms 78 1.12. The morphological and semantic conformation of the psychic system 86 1.13. The processing component of the visual sense with generating forms 91 1.14. The decisive intention to think 97 1.15. Linguistic capacity in the human conscious 101 1.16. An assessment of the functioning of the human psychic system 109 Chapter 2. The Computer Representation of an Artificial Consciousness 113 2.1. A multiagent design to generate an artificial psychic system 114 2.2. Designing the artificial psychic system using a multiagent approach 122 2.3. Self-control of the artificial psychic system using regulator agents 128 2.4. The organizational architecture of the system 133 2.5. Organizational memory and artificial experience 142 2.6. Affective and tendential states of the system 154 2.7. The production of representations and the sensation of thinking 161 2.7.1. Algorithm for the intentional production of a series of representations around a specific theme 163 2.8. The feeling of existing 176 2.9. The representation of the things and the apprehension of temporality 181 2.10. Multisystem deployment 186 2.11. The final fate of systems endowed with artificial consciousness 194 Conclusion 197 Bibliography 201 Index 205
£125.06
Edward Elgar Publishing Ltd Research Handbook on the Law of Artificial
Book SynopsisThe field of artificial intelligence has made tremendous advances in the last few decades, but as smart as AI is now, it is getting exponentially smarter and becoming more autonomous in its actions. This raises a host of challenges to current legal doctrine, including whether the output of AI entities should count as 'speech', the extent to which AI should be regulated under antitrust and criminal law statutes, and whether AI should be considered an independent agent and responsible for its actions under the law of tort or agency. Containing chapters written by leading U.S., EU, and International law scholars, the Research Handbook presents current law, statutes, and regulations on the role of law in an age of increasingly smart AI, addressing issues of law that are critical to the evolution of AI and its role in society. To provide a broad coverage of the topic, the Research Handbook draws upon free speech doctrine, criminal law, issues of data protection and privacy, legal rights for increasingly smart AI systems, and a discussion of jurisdiction for AI entities that will not be 'content' to stay within the geographical boundaries of any nation state or be tied to a particular physical location. Using numerous examples and case studies, the chapter authors discuss the political and jurisdictional decisions that will have to be made as AI proliferates into society and transforms our government and social institutions. The Research Handbook will also introduce designers of artificially intelligent systems to the legal issues that apply to the make-up and use of AI from the technologies, algorithms, and analytical techniques. This essential guide to the U.S., EU, and other International law, regulations, and statutes which apply to the emerging field of 'law and AI' will be a valuable reference for scholars and students interested in information and intellectual property law, privacy, and data protection as well as to legal theorists and social scientists who write about the future direction and implications of AI. The Research Handbook will also serve as an important reference for legal practitioners in different jurisdictions who may litigate disputes involving AI, and to computer scientists and engineers actively involved in the design and use of the next generation of AI systems.Contributors include: W. Barfield, S. Bayern, S.J. Blodgett-Ford, R.G.A. Bone, T. Burri, A. Chin, J.A. Cubert, M. de Cock Buning, S. De Conca, S-.A. Elvy, A. Ezrachi, R. Leenes, Y. Lev-Aretz, A.R. Lodder, R.P. Loui, T.M. Massaro, L.T. McCarty, J.O. McGinnis, F. Moslein, H. Norton, N. Packin, U. Pagallo, S. Quattrocolo, W. Samore, F. Shimpo, M.E. Stucke, R. van den Hoven van Genderen, L. Vertinsky, A. von Ungern-Sternberg, J.F. Weaver, Y-.H. Weng, I. WildhaberTable of ContentsContents: Forward: Curtis E. A. Karnow Part I Introduction to Law and Artificial Intelligence 1. Towards a Law of Artificial Intelligence Woodrow Barfield 2. Accelerating AI John O. McGinnis 3. Finding the Right Balance in Artificial Intelligence and Law L. Thorne McCarty 4. Learning Algorithms and Discrimination Nizan Packin and Yafit Lev-Aretz 5. The Principal Japanese AI and Robot Strategy and Research Toward Establishing Basic Principles Fumio Shimpo Part II Regulation of Artificial Intelligence 6. Artificial Intelligence and Private Law Shawn Bayern 7. Regulation of Artificial Intelligence John Frank Weaver 8. Legal Personhood in the Age of Artificially Intelligent Robots Robert van den Hoven van Genderen 9. Autonomous Driving: Regulatory Challenges Raised by Artificial Decision-Making and Tragic Choices Antje von Ungern-Sternberg Part III Fundamental Rights and Constitutional Law Issues 10. Artificial Intelligence and Privacy- AI Enters the House Through the Cloud Ronald Leenes and Silvia De Conca 11. Future Privacy: A Real Right to Privacy for Artificial Intelligence S. J. Blodgett-Ford 12. Artificial Intelligence and the First Amendment Toni M. Massaro and Helen Norton 13. Data Algorithms and Privacy in Surveillance: On Stages, Numbers, and the Human Factor Arno R. Lodder and Ronald P. Loui 14. The Impact of AI on Criminal Law, and its Twofold Procedures Ugo Pagallo and Serena Quattrocolo Patrt IV Intellectual Property 15. The Law of Artificial Intelligence Intellectual Property Jeremy A. Cubert and Richard G. A. Bone 16. Kinematically Abstract Claims in Surgical Robotics Patents Andrew Chin 17. Artificial Intelligence and the Patent System: Can a New Tool Render a Once Patentable Idea Obvious? William Samore 18. Thinking Machines and Patent Law Liza Vertinsky 19. Artificial Intelligence and the Creative Industry: New Challenges for the EU Paradigm for Art and Technology by Autonomous Creation Madeleine de Cock Buning Part V Applications of Artificial Intelligence 20. Free Movement of Algorithms: Artificially Intelligent Persons Conquer the European Union’s Internal Market Thomas Burri 21. The Artificially Intelligent Internet of Things and Article 2 of the Uniform Commercial Code Stacy-Ann Elvy 22. Artificial Intelligence and Robotics, the Workplace, and Workplace-Related Law Isabelle Wildhaber 23. Robotics Law 1.0: On Social System Design for Artificial Intelligence Yueh-Hsuan Weng 24. Antitrust, Algorithmic Pricing and Tacit Collusion Maurice E. Stucke and Ariel Ezrachi 25. Robots in the Boardroom: Artificial Intelligence and Corporate Law Florian Möslein Index
£260.00
Edward Elgar Publishing Ltd Autonomous Vehicles and the Law: Technology,
Book SynopsisAutonomous vehicles have attracted a great deal of attention in the media, however there are some inconsistencies between the perception of autonomous vehicles’ capabilities and their actual functions. This book provides an accessible explanation of how autonomous vehicles function, suggesting appropriate regulatory responses to the existing and emerging technology.Hannah YeeFen Lim explores the current capabilities of autonomous vehicles and importantly, highlights their inherent limitations. Lim provides a concise and easy to follow overview of the technology behind autonomous vehicles which encompasses hardware and software aspects, including machine learning algorithms. Having laid the technical foundation, the following chapters assess the current legal standards in negligence law that are applicable to autonomous vehicles taking the current technical limitations of the vehicles into account. Lim concludes by exploring the ethical issues associated with autonomous vehicles and proposes appropriate regulatory approaches. This book will be of great value to policy makers seeking a deeper understanding of the technology behind autonomous vehicles in order to inform and guide the development of laws and regulations. Legal practitioners will benefit from the discussion of recent use cases and applicable negligence law. Legal scholars researching artificial intelligence will also find the author’s easy to understand technical explanations and discourse on ethical considerations invaluable.Trade Review'Professor Lim's expertise in both law and computer science is evident in this clear and crisp assessment of liability issues surrounding Automated Vehicles (AV's). She demystifies the science and technology underlying this phenomenon that has captured the public imagination and left law and policy-makers scrambling. Transcending the hype around AVs, Professor Lim's thoughtful and tech-savvy application of negligence principles provides an essential framework through which the risks and benefits of AV technology can be more cogently assessed and addressed.' --Teresa Scassa, University of Ottawa, Canada'Self-driving cars are the vanguard of AI-based autonomous systems, machines which are about to transform our world. This book is a wonderful introduction and resource, both to the technology and the legal questions that we are facing. The author provides a clear how-to guide to regulating systems that are hard-to-understand but which, within a few years, will be piloting large pieces of metal at speed down roads that we used to have to ourselves. Recommended for anyone who thinks about what the future should look like.' --Dan Hunter, Swinburne Law School, AustraliaTable of ContentsContents 1. Introduction 2. How autonomous vehicles function 3. Verifiable Standards of Care 4. Software: Difficult to verify standards of care 5. The road less travelled for regulators 6. Ethical responsibilities and autonomous vehicles 7. For a smoother ride … Index
£75.00
Kogan Page Ltd Artificial Intelligence for Learning: How to use
Book SynopsisArtificial intelligence is creating huge opportunities for workplace learning and employee development. However, it can be difficult for L&D professionals to assess what difference AI can make in their organization and where it is best implemented. Artificial Intelligence for Learning is the practical guide L&D practitioners need to understand what AI is and how to use it to improve all aspects of learning in the workplace. It includes specific guidance on how AI can provide content curation and personalization to improve learner engagement, how it can be implemented to improve the efficiency of evaluation, assessment and reporting and how chatbots can provide learner support to a global workforce. Artificial Intelligence for Learning debunks the myths and cuts through the hype around AI allowing L&D practitioners to feel confident in their ability to critically assess where artificial intelligence can make a measurable difference and where it is worth investing in. There is also critical discussion of how AI is an aid to learning and development, not a replacement as well as how it can be used to boost the effectiveness of workplace learning, reduce drop off rates in online learning and improve ROI. With real-world examples from companies who have effectively implemented AI and seen the benefits as well as case studies from organizations including Netflix, British Airways and the NHS, this book is essential reading for all L&D practitioners needing to understand AI and what it means in practice.Trade Review"The world of workplace learning will be dominated by AI within a few years. Artificial Intelligence for Learning plots a clear and concise path through what is the biggest opportunity the industry has had for many years." * Paul McElvaney, CEO of Learning Pool *"Donald Clark has been at the leading edge of technology in learning for over 30 years. His take on tech is always informed by his detailed knowledge of learning theory. This book on AI is no exception - it's bold, thorough, bang up to date, well-researched, evidence-based and practical." * Kirstie Donnelly MBE, CEO of City & Guilds Group *Table of Contents Section - PART ONE: Introduction; Section - 01: Homo technus; Section - 02: What is AI?; Section - PART TWO: Teaching; Section - 03: Robot teacher fallacy; Section - 04: Teaching; Section - PART THREE: Chatbots; Section - 05: AI is the new UI; Section - 06: Chatbots; Section - 07: Building chatbots; Section - PART FOUR: Learning; Section - 08: Content creation; Section - 09: Video; Section - 10: Push learning; Section - 11: Adaptive learning; Section - 12: Learning organizations; Section - 13: Assessment; Section - PART FIVE: Data; Section - 14: Data analytics; Section - 15: Sentiment analysis; Section - PART SIX: Future; Section - 16: Future skills; Section - 17: Ethics and bias; Section - 18: Employment; Section - 19: The final frontier; Section - 20: Where next?; Section - 21: Index
£30.39
Kogan Page Ltd Driving Digital Transformation through Data and
Book SynopsisLeading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business. It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working. Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI.Trade Review"After years of progress in AI, might the hype be growing faster than the reality? Are we about to enter an 'AI autumn'? Not if Borek and Prill have anything to say about it! Digital transformation is tough - this book improves your odds." * Thomas C Redman, "the Data Doc", Harvard Business Review Blogger and Author *"Clear and to the point in a language that works for executives. A must-read for any leader." * Holger Kömm, Senior Director Advanced Analytics, Adidas *"A great in-depth introduction to how to add value to companies using digitalization, data and AI." * Patrick Glauner, Professor of Artificial Intelligence, Deggendorf Institute of Technology *"Provides great guidance on how to think of data products instead of projects - which is a key factor in mastering the challenges of digitalization." * Carsten Bange, Founder and CEO, BARC (Business Application Research Centre) *"A must-read for everyone involved into turning digitization and AI into real value for your company. Whether you're in the middle of the process looking for some orientation or just about getting started, this book will provide you with the advice you need!" * Alexander Thamm, CEO and Founder of Alexander Thamm GmbH *Table of Contents Chapter - 01: Introduction to delivering data and AI products; Chapter - 02: Developing the data and AI product strategy and goals; Chapter - 03: Setting up the data and AI product delivery organization; Chapter - 04: Identifying and defining data and AI products; Chapter - 05: Delivering high quality data and AI products; Chapter - 06: Designing the data and AI platform and architecture; Chapter - 07: Driving transformative change with data and AI products; Chapter - 08: The future of data and AI products in your organization
£31.99
Kogan Page Ltd Driving Digital Transformation through Data and
Book SynopsisLeading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business. It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working. Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI.Trade Review"After years of progress in AI, might the hype be growing faster than the reality? Are we about to enter an 'AI autumn'? Not if Borek and Prill have anything to say about it! Digital transformation is tough - this book improves your odds." * Thomas C Redman, "the Data Doc", Harvard Business Review Blogger and Author *"Clear and to the point in a language that works for executives. A must-read for any leader." * Holger Kömm, Senior Director Advanced Analytics, Adidas *"A great in-depth introduction to how to add value to companies using digitalization, data and AI." * Patrick Glauner, Professor of Artificial Intelligence, Deggendorf Institute of Technology *"Provides great guidance on how to think of data products instead of projects - which is a key factor in mastering the challenges of digitalization." * Carsten Bange, Founder and CEO, BARC (Business Application Research Centre) *"A must-read for everyone involved into turning digitization and AI into real value for your company. Whether you're in the middle of the process looking for some orientation or just about getting started, this book will provide you with the advice you need!" * Alexander Thamm, CEO and Founder of Alexander Thamm GmbH *Table of Contents Chapter - 01: Introduction to delivering data and AI products; Chapter - 02: Developing the data and AI product strategy and goals; Chapter - 03: Setting up the data and AI product delivery organization; Chapter - 04: Identifying and defining data and AI products; Chapter - 05: Delivering high quality data and AI products; Chapter - 06: Designing the data and AI platform and architecture; Chapter - 07: Driving transformative change with data and AI products; Chapter - 08: The future of data and AI products in your organization
£90.25
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
£98.67
Edward Elgar Publishing Ltd Handbook of Artificial Intelligence in Education
Book SynopsisGathering insightful and stimulating contributions from leading global experts in Artificial Intelligence in Education (AIED), this comprehensive Handbook traces the development of AIED from its early foundations in the 1970s to the present day. The Handbook evaluates the use of AI techniques such as modelling in closed and open domains, machine learning, analytics, language understanding and production to create systems aimed at helping learners, teachers, and educational administrators. Chapters examine theories of affect, metacognition and pedagogy applied in AIED systems; foundational aspects of AIED architecture, design, authoring and evaluation; and collaborative learning, the use of games and psychomotor learning. It concludes with a critical discussion of the wider context of Artificial Intelligence in Education, examining its commercialisation, social and political role, and the ethics of its systems, as well as reviewing the possible challenges and opportunities for AIED in the next 20 years. Providing a broad yet detailed account of the current field of Artificial Intelligence in Education, researchers and advanced students of education technology, innovation policy, and university management will benefit from this thought-provoking Handbook. Chapters will also be useful to support undergraduate courses in AI, computer science, and education.Trade Review‘The Handbook of Artificial Intelligence in Education is a great resource for people studying the field of AI and data-directed education, which is a complex tapestry, with many different elements feeding into its design and development. The Handbook supports readers to experience the full tapestry and to pull out and examine individual threads, without losing the underlying purpose. The field has grown tremendously in the past 30 years. Intelligent tutors now listen to and speak to learners, model student expertise and knowledge, examine theories (about how humans learn, think, collaborate and socialize), and support classroom orchestration, learning at scale, assessment, and human-machine interaction. The Handbook nicely documents progress in the field without overwhelming a new generation of AIED researchers.’ -- Beverly Woolf, author of Building Intelligent Interactive Tutors and University of Massachusetts, Amherst, US‘This is a really great Handbook written by some of the most well known authors in the field of Artificial Intelligence in Education (AIED). It describes both the most important topics and future trends in a very comprehensive way for a wide range of stakeholders.’ -- Cristóbal Romero, University of Córdoba, SpainTable of ContentsContents: Foreword xii PART I SCENE SETTING 1 Introduction 2 Benedict du Boulay, Antonija Mitrovic and Kalina Yacef 2 The history of artificial intelligence in education – the first quarter century 10 Gordon McCalla PART II THEORIES UNDERPINNING AIED 3 The role and function of theories in AIED 31 Stellan Ohlsson 4 Theories of metacognition and pedagogy applied to AIED systems 45 Roger Azevedo and Megan Wiedbusch 5 Theories of affect, meta-affect, and affective pedagogy 68 Ivon Arroyo, Kaśka Porayska-Pomsta and Kasia Muldner 6 Scrutable AIED 101 Judy Kay, Bob Kummerfeld, Cristina Conati, Kaśka Porayska-Pomsta and Ken Holstein PART III THE ARCHITECTURE AND DESIGN OF AIED SYSTEMS 7 Domain modeling for AIED systems with connections to modeling student knowledge: a review 127 Vincent Aleven, Jonathan Rowe, Yun Huang and Antonija Mitrovic 8 Student modeling in open-ended learning environments 170 Cristina Conati and Sébastien Lallé 9 Six instructional approaches supported in AIED systems 184 Vincent Aleven, Manolis Mavrikis, Bruce M. McLaren, Huy A. Nguyen, Jennifer Olsen and Nikol Rummel 10 Theory-driven design of AIED systems for enhanced interaction and problem-solving 229 Susanne Lajoie and Shan Li 11 Deeper learning through interactions with students in natural language 250 Vasile Rus, Andrew M. Olney and Arthur C. Graesser 12 Authoring tools to build AIED systems 273 Stephen Blessing, Stephen B. Gilbert and Steven Ritter PART IV ANALYTICS 13 Continuous student modeling for programming in the classroom: challenges, methods, and evaluation 287 Ye Mao, Samiha Marwan, Preya Shabrina, Yang Shi, Thomas W. Price, Min Chi and Tiffany Barnes 14 Human–AI co-orchestration: the role of artificial intelligence in orchestration 309 Ken Holstein and Jennifer Olsen 15 Using learning analytics to support teachers 322 Stanislav Pozdniakov, Roberto Martinez-Maldonado, Shaveen Singh, Hassan Khosravi and Dragan Gašević 16 Predictive modeling of student success 350 Christopher Brooks, Vitomir Kovanović and Quan Nguyen 17 Social analytics to support engagement with learning communities 370 Carolyn Rosé, Meredith Riggs and Nicole Barbaro PART V AIED SYSTEMS IN USE 18 Intelligent systems for psychomotor learning: A systematic review and two cases of study 390 Alberto Casas-Ortiz, Jon Echeverria and Olga C. Santos 19 Artificial intelligence techniques for supporting face-to-face and online collaborative learning 422 Roberto Martinez-Maldonado, Anouschka van Leeuwen and Zachari Swiecki 20 Digital learning games in artificial intelligence in education (AIED): a review 440 Bruce M. McLaren and Huy A. Nguyen 21 Artificial intelligence-based assessment in education 487 Ying Fang, Rod D. Roscoe and Danielle S. McNamara 22 Evaluations with AIEd systems 507 Kurt VanLehn 23 Large-scale commercialization of AI in school-based environments 526 Steven Ritter and Kenneth R. Koedinger 24 Small-scale commercialisation: the golden triangle of AI EdTech 539 Rosemary Luckin and Mutlu Cukurova 25 Critical perspectives on AI in education: political economy, discrimination, commercialization, governance and ethics 555 Ben Williamson, Rebecca Eynon, Jeremy Knox and Huw Davies 26 The ethics of AI in education 573 Kaśka Porayska-Pomsta, Wayne Holmes and Selena Nemorin PART VI THE FUTURE 27 The great challenges and opportunities of the next 20 years 608 1. AIED and equity 608 Maria Mercedes T. Rodrigo 2. Engaging learners in the age of information overload 610 Julita Vassileva 3. Pedagogical agents for all: designing virtual characters for inclusion and diversity in STEM 613 H. Chad Lane 4. Intelligent textbooks 616 Peter Brusilovsky and Sergey Sosnovsky 5. AI-empowered open-ended learning environments in STEM domains 620 Gautam Biswas 6. Ubiquitous-AIED: pervasive AI learning technologies 626 James C. Lester 7. Culture, ontology and learner modeling 629 Riichiro Mizoguchi 8. Crowdsourcing paves the way for personalized learning 632 Ethan Prihar and Neil Heffernan 9. AIED in developing countries: breaking seven WEIRD assumptions in the global learning XPRIZE field study 635 Jack Mostow 10. The future of learning assessment 639 Claude Frasson 11. Intelligent mentoring systems: tapping into AI to deliver the next generation of digital learning 642 Vania Dimitrova Index 653
£255.00
Edward Elgar Publishing Ltd Regulatory Insights on Artificial Intelligence:
Book SynopsisThis provocative book investigates the relationship between law and artificial intelligence (AI) governance, and the need for new and innovative approaches to regulating AI and big data in ways that go beyond market concerns alone and look to sustainability and social good. Taking a multidisciplinary approach, the contributors demonstrate the interplay between various research methods, and policy motivations, to show that law-based regulation and governance of AI is vital to efforts at ensuring justice, trust in administrative and contractual processes, and inclusive social cohesion in our increasingly technologically-driven societies. The book provides valuable insights on the new challenges posed by a rapid reliance on AI and big data, from data protection regimes around sensitive personal data, to blockchain and smart contracts, platform data reuse, IP rights and limitations, and many other crucial concerns for law’s interventions. The book also engages with concerns about the ‘surveillance society’, for example regarding contact tracing technology used during the Covid-19 pandemic. The analytical approach provided will make this an excellent resource for scholars and educators, legal practitioners (from constitutional law to contract law) and policy makers within regulation and governance. The empirical case studies will also be of great interest to scholars of technology law and public policy. The regulatory community will find this collection offers an influential case for law’s relevance in giving institutional enforceability to ethics and principled design.Trade Review‘Regulatory Insights on Artificial Intelligence is bursting with ideas. While many more questions are asked than answered, those questions require one to think deeply about important issues associated with AI. That thinking is sorely needed now, if this technology is to benefit us, rather than harm us.’ -- Rob Clark, Intellectual Property Forum (IPSANZ)‘Regulatory Insights on Artificial Intelligence provides a timely and important discussion of the regulation of a technology that is not only proliferating into our lives, but becoming disruptive in our economic and social institutions. I highly recommend the book for legal scholars, regulators, and anyone interested in a comprehensive text on the topic.’ -- Woodrow Barfield, Visiting Professor, University of Turin, Italy‘This book is an excellent resource for aiding the discussion on the imminent need for effective regulation, informed by interdisciplinary and multi-stakeholder approaches, that AI governance requires. It is a must read for those interested in the “next steps” to actually implementing or codifying AI governance into existing legal contexts.’ -- Christoph Lütge, Technical University of Munich, GermanyTable of ContentsContents: Preface xi 1 Regulatory insights on artificial intelligence: research for policy 1 Mark Findlay and Jolyon Ford 2 Editors’ reflections 16 Mark Findlay and Jolyon Ford 3 Artificial intelligence and sensitive inferences: new challenges for data protection laws 19 Damian Clifford, Megan Richardson and Normann Witzleb 4 Revaluing labour? Secondary data imperialism in platform economies 46 Mark Findlay and Josephine Seah 5 Gauging the acceptance of contact-tracing technology: an empirical study of Singapore residents’ concerns and trust in information sharing 70 Ong Ee Ing and Loo Wee Ling 6 Regulating personal data usage in COVID-19 control conditions 101 Mark Findlay and Nydia Remolina 7 Editors’ reflections 128 Mark Findlay and Jolyon Ford 8 Coding legal norms: an exploratory essay 132 Will Bateman 9 Artificial intelligence and the unconscionability principle 150 Dilan Thampapillai 10 The possibilities of IF-THEN-WHEN 162 Sally Wheeler 11 Doing it online: is mediation ready for the AI age? 187 Nadja M Alexander 12 Editors’ reflections 214 Mark Findlay and Jolyon Ford 13 Ethical AI frameworks: the missing governance piece 218 Jolyon Ford 14 The accountability of algorithms on social media platforms 239 Philippa Ryan 15 Models and data trade regulation and the road to an agreement 261 Henry Gao Index
£109.00
Edward Elgar Publishing Ltd Research Handbook on Intellectual Property and
Book SynopsisThis incisive Handbook offers novel theoretical and doctrinal insights alongside practical guidance on some of the most challenging issues in the field of artificial intelligence and intellectual property. Featuring all original contributions from a diverse group of international thought leaders, including top academics, judges, regulators and eminent practitioners, it offers timely perspectives and research on the relationship of AI to copyright, trademark, design, patent and trade secret law.The Handbook is divided into four thematic parts, beginning with topics that address the intersection of IP and AI broadly before focusing on issues associated with specific types of IP. Chapters tackle critical legal questions, from issues with protecting AI-generated ourput to the impact of AI on how trademarks are used, offering valuable lessons on technology regulation and how technological evolution will disrupt existing legal frameworks.Scholars and students of intellectual property law and its intersections with AI and related technologies will find this Handbook ’s cutting-edge contributions to be a crucial read. Its guidance on the practical legal implications of technological advances will also be of interest to IP practitioners, as well as policymakers and regulators.Trade Review‘A book of impressive breadth and thoughtfully curated analyses of doctrinal and policy issues at the intersection of AI and Intellectual Property (IP). The Handbook illuminates challenges across all IP fields and exposes the fragile normative bases on which many of our extant laws depend. It is a must have and a “go to” for meaningful engagement with the complex questions regarding the regulation of AI and IP — both nationally and globally.’ -- Ruth L. Okediji, Harvard Law School, USTable of ContentsContents: PART I MULTI-SUBJECT 1 Artificial intelligence and intellectual property: an introduction 2 Ryan Abbott 2 The human cause 21 Daniel J. Gervais 3 Considering intellectual property law for embodied forms of artificial intelligence 39 Woodrow Barfield, Argyro Karanasiou and Karni Chagnal-Feferkorn 4 AI replication of musical styles points the way to an exclusive rights regime 64 Sean M. O’Connor 5 The elusive intellectual property protection of trained machine learning models: a European perspective 83 Jean-Marc Deltorn 6 An abject failure of intelligence: intellectual property and artificial intelligence 112 Michael D. Pendleton PART II COPYRIGHT AND RELATED RIGHTS 7 The AI–copyright challenge: tech-neutrality, authorship, and the public interest 133 Carys J. Craig 8 Four theories in search of an A(I)uthor 155 Giancarlo Frosio 9 Copyright law should stay true to itself in the age of artificial intelligence 178 Alice Lee and Phoebe Woo 10 The protection of AI-generated pictures (photograph and painting) under copyright law 197 Yaniv Benhamou & Ana Andrijevic 11 Performers’ rights and artificial intelligence 217 Richard Arnold 12 AIn’t it just software? 224 Shubha Ghosh 13 Can artificial intelligence infringe copyright? Some reflections 244 Enrico Bonadio, Plamen Dinev and Luke McDonagh PART III TRADE MARKS AND DESIGNS 14 Computational trademark infringement and adjudication 258 Daryl Lim 15 Online shopping with artificial intelligence: what role for trade marks? 289 Anke Moerland and Christie Kafrouni 16 Trademark law, AI-driven behavioral advertising, and the Digital Services Act: toward source and parameter transparency for consumers, brand owners, and competitors 308 Martin Senftleben 17 A quotidian revolution: artificial intelligence and trade mark law 324 Dev S. Gangjee 18 The impact of AI on designs law 345 Trevor Cook PART IV PATENTS AND TRADE SECRETS 19 Legal fictions and the corporation as an inventive artificial intelligence 355 Dennis Crouch 20 Economic reasons to recognise AI inventors 375 Benjamin Mitra-Kahn 21 Reverse engineering (by) artificial intelligence 390 Shawn Bayern 22 Trade secrets versus the AI explainability principle 404 Rita Matulionyte and Tatiana Aranovich 23 The inventive step requirement and the rise of the AI machines 422 Noam Shemtov and Garry A. Gabison 24 Trade secrecy, factual secrecy and the hype surrounding AI 442 Sharon K. Sandeen and Tanya Aplin Index
£210.00
Edward Elgar Publishing Ltd Handbook on the Politics and Governance of Big
Book SynopsisDrawing on the theoretical debates, practical applications, and sectoral approaches in the field, this ground-breaking Handbook unpacks the political and regulatory developments in AI and big data governance. Covering the political implications of big data and AI on international relations, as well as emerging initiatives for legal regulation, it provides an accessible overview of ongoing data science discourses in politics, law and governance.With novel insights into existing and emerging debates, this cutting-edge Handbook highlights the mutual effects of big data and AI on society. Amongst other theoretical and sectoral issues, chapters analyse the liability of AI use in autonomous weapons, the role of big data in healthcare and education, the intersections between AI and gender in human rights law, and the ethics of public facial-recognition technology. Addressing the many open questions and future regulatory problems, it uses data science to investigate the dynamics between the technical aspects, societal dynamics and governance implications of big data and AI.Transdisciplinary in scope, this Handbook will be invaluable to students and researchers across the fields of politics, law, governance and data science, alongside policymakers concerned with the regulation and governance of AI and big data in public and private institutions.Trade Review‘Zwitter and Gstrein have astutely brought together an impressive collection of chapters that address key themes in the politics and governance of AI and big data. From social justice and gender to privacy and rights, the Handbook provides a solid introduction to key debates and their implications for societies.’ -- Evelyn Ruppert, Goldsmiths, University of London, UK‘This volume succeeds in bringing together a wide ranging collection of original studies in a field that is as fast developing as it is important to keep track of. The reader who is interested in normative political and governance perspectives on AI and big data will find insightful analyses and well-informed discussions of the key problems of regulation and policy making in a digital age.’ -- Jeroen van den Hoven, Delft University of Technology, the NetherlandsTable of ContentsContents: Foreword xiii PART I INTRODUCTION Introduction to the Handbook on the Politics and Governance of Big Data and Artificial Intelligence 2 Andrej Zwitter and Oskar J. Gstrein PART II CONCEPTUAL PERSPECTIVES 1 Can AI governance be progressive? Group interests, group privacy and abnormal justice 19 Linnet Taylor 2 Big Data and the humanitarian sector: emerging trends and persistent challenges 41 Susanne Schmuck, Andrej Zwitter and Oskar J. Gstrein 3 Digital twins: potentials, ethical issues and limitations 64 Dirk Helbing and Javier Argota Sánchez-Vaquerizo 4 Governing Digital Twin technology for smart and sustainable tourism: a case study in applying a documentation framework for architecture decisions 105 Eko Rahmadian, Daniel Feitosa and Andrej Zwitter PART III PRINCIPLE-BASED APPROACHES TO THE GOVERNANCE OF BIG DATA AND AI 5 Digital transitional justice: unpacking the black box 139 Christopher K. Lamont and Medlir Mema 6 Autonomous weaponry and IR theory: conflict and cooperation in the age of AI 167 Amelia Hadfield and Alex Leveringhaus 7 Understanding emergent technology, instability and power in international political economy 188 Malcolm Campbell-Verduyn 8 Governance of AI and gender: building on International Human Rights Law and relevant regional frameworks 211 Elizabeth Coombs and Halefom Abraha PART IV SECTORAL APPROACHES TO THE GOVERNANCE OF BIG DATA AND AI 9 Better technological security solutions through human-centred design and development 245 Andrew B. Wootton, Caroline L. Davey, Dagmar Heinrich and Maximilian Querbach 10 On the governance of privacy-preserving systems for the web: should Privacy Sandbox be governed? 279 Lukasz Olejnik 11 Experiments with facial recognition technologies in public spaces: in search of an EU governance framework 315 Catherine Jasserand 12 Big Data, AI and health data: between national, European, and international legal frameworks 358 Nikolaus Forgó, Emily Johnson, Iana Kazeeva and Elisabeth Steindl 13 Governing the ‘datafied’ school: bridging the divergence between universal education and student autonomy 395 Theresa Henne and Oskar J. Gstrein PART V AUTONOMOUS SYSTEMS, RIGHTS AND DUTIES 14 Artificial Intelligence and international human rights law: implications for humans and technology in the 21st century and beyond 430 Joshua C. Gellers and David J. Gunkel 15 Challenges posed by autonomous systems to liability regimes: finding a balance 456 Nynke E. Vellinga 16 Autonomous Weapons Systems in warfare: is Meaningful Human Control enough? 476 Taís Fernanda Blauth Index 504
£185.00
Edward Elgar Publishing Ltd Managing AI Wisely: From Development to
Book SynopsisArtificial Intelligence (AI) is being rapidly introduced into the workplace, creating debate around what AI means for our work and organizations. This book gives grounded counterweight to provocative newspaper headlines by using in-depth case studies of eight organizations’ experiences of implementing and using AI, providing readers with a solid understanding of what is actually happening in practice.Critical yet constructive, the authors address the challenges of implementing AI: organizing for data, testing and validating, algorithmic brokering, and changing work. Using a combination of existing literature and thorough practical examples, they provide answers to questions such as: What data do I need? When is a system good enough to actually take over tasks? And how can my employees be prepared for working with AI? The book presents four recommendations for WISE management of AI, requiring work-related insights, interdisciplinary knowledge, sociotechnical change processes, and ethical awareness.Offering insight into the unique characteristics of AI in organizations, this book will be essential reading for scholars of business and management, data analytics and information systems, technology and innovation, and computer science. With practical recommendations for managing the challenges of AI, it will also provide business managers with reflections to improve their own AI development and implementation processes.Trade Review‘Wonderfully written, this book will resonate with every manager who is currently grappling with implementing AI in their organization. By analyzing real-life case studies, the authors go way beyond the AI hype and dive into the intricate organizational and work challenges that arise with the introduction of AI in the workplace, providing actionable insights. A must read for all decision makers, developers and technology brokers at incumbent organizations!’ -- Stella Pachidi, University of Cambridge, UKTable of ContentsContents: 1. Introduction to managing AI wisely 2. What is AI? 3. Perspectives on AI and work 4. Methods and introduction to cases 5. Organizing for data 6. Testing and validating 7. Algorithmic brokers 8. Changing work 9. How can AI systems be managed wisely? Index
£78.00
Edward Elgar Publishing Ltd Handbook of Artificial Intelligence at Work:
Book SynopsisWith the advancement in processing power and storage now enabling algorithms to expand their capabilities beyond their initial narrow applications, technology is becoming increasingly powerful. This highly topical Handbook provides a comprehensive overview of the impact of Artificial Intelligence (AI) on work, assessing its effect on an array of economic sectors, the resulting nature of work, and the subsequent policy implications of these changes. Featuring contributions from leading experts across diverse fields, the Handbook of Artificial Intelligence at Work takes an interdisciplinary approach to understanding AI’s connections to existing economic, social, and political ecosystems. Considering a range of fields including agriculture, manufacturing, health care, education, law and government, the Handbook provides detailed sector-specific analyses of how AI is changing the nature of work, the challenges it presents and the opportunities it creates. Looking forward, it makes policy recommendations to address concerns, such as the potential displacement of some human labor by AI and growth in inequality affecting those lacking the necessary skills to interact with these technologies or without opportunities to do so.This vital Handbook is an essential read for students and academics in the fields of business and management, information technology, AI, and public policy. It will also be highly informative from a cross-disciplinary perspective for practitioners, as well as policy makers with an interest in the development of AI technology.Table of ContentsContents: 1 Introduction to the Handbook of Artificial Intelligence at Work: Interconnections and Policy Implications 1 Martha Garcia-Murillo and Ian MacInnes PART I CONCEPTUALIZING THE HUMAN WITH THE MACHINE 2 The computer says no: how automated decision systems affect workers’ role perceptions in socio-technical systems 16 Sabine T. Koeszegi, Setareh Zafari, and Reinhard Grabler 3 Responsible AI at work: incorporating human values 32 Andreas Theodorou and Andrea Aler Tubella 4 AI-enabled business model and human-in-the-loop (deceptive AI): implications for labor 47 Uma Rani and Rishabh Kumar Dhir 5 Tools for crowdworkers coding data for AI 76 Saiph Savage and Martha Garcia-Murillo PART II SECTORAL USES, APPLICATIONS, CHALLENGES, AND OPPORTUNITIES 6 AI and the transformation of agricultural work: economic, social, and environmental implications 96 Andrea Renda 7 AI in manufacturing and the role of humans: processes, robots, and systems 119 Panagiotis Stavropoulos, Kosmas Alexopoulos, Sotiris Makris, Alexios Papacharalampopoulos, Steven Dhondt, and George Chryssolouris 8 Workers and AI in the construction and operation of civil infrastructures 142 Jinding Xing, Zhe Sun, and Pingbo Tang 9 AI-based technology in home-based care in aging societies: challenges and opportunities 166 Naoko Muramatsu, Miloš Žefran, Emily Stiehl, and Thomas Cornwell 10 Artificial intelligence for professional learning 191 Wayne Holmes and Allison Littlejohn 11 Smart automation in entrepreneurial finance: the use of AI in private markets 212 Francesco Corea 12 The artificial creatives: the rise of combinatorial creativity from DALL-E to GPT-3 225 Giancarlo Frosio 13 The judicial system and the work of judges and lawyers in the application of law and sanctions assisted by AI 250 Karim Benyekhlef and Jie Zhu 14 AI and national security 276 Saiph Savage, Gabriela Avila, Norma Elva Chávez, and Martha Garcia-Murillo 15 Governance, government records, and the policymaking process aided by AI 291 Andrea Renda PART III THE LABOR IMPLICATIONS OF ARTIFICIAL INTELLIGENCE AT WORK 16 Recurrent memes and technological fallacies 315 David Heatly and Bronwyn Howell 17 AI and income inequality: the danger of exacerbating existing trends toward polarization in the US workforce 338 Dan Sholler and Ian MacInnes 18 The impact of AI on contracts and unionisation 356 Michael Walker Index 371
£200.00
