Search results for """ "PromptGenerated Media Using Artificial Intelligence""

Book SynopsisThis book explains the field of Generative Artificial Intelligence (AI), focusing on its potential and applications, and aims to provide you with an understanding of the underlying principles, techniques, and practical use cases of Generative AI models. The book begins with an introduction to the foundations of Generative AI, including an overview of the field, its evolution, and its significance in today's AI landscape. It focuses on generative visual models, exploring the exciting field of transforming text into images and videos. A chapter covering text-to-video generation provides insights into synthesizing videos from textual descriptions, opening up new possibilities for creative content generation. A chapter covers generative audio models and prompt-to-audio synthesis using Text-to-Speech (TTS) techniques. Then the book switch gears to dive into generative text models, exploring the concepts of Large Language Models (LLMs), natural language generation (NLG), fine-tuning, prompt tuning, and reinforcement learning. The book explores techniques for fixing LLMs and making them grounded and indestructible, along with practical applications in enterprise-grade applications such as question answering, summarization, and knowledge-based generation. By the end of this book, you will understand Generative text, and audio and visual models, and have the knowledge and tools necessary to harness the creative and transformative capabilities of Generative AI. What You Will LearnWhat is Generative Artificial Intelligence?What are text-to-image synthesis techniques and conditional image generation?What is prompt-to-audio synthesis using Text-to-Speech (TTS) techniques?What are text-to-video models and how do you tune them?What are large language models, and how do youtune them?Who This Book Is ForThose with intermediate to advanced technical knowledge in artificial intelligence and machine learning

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Book SynopsisThis timely book presents a detailed analysis of the role of law and regulation in the utilisation of Artificial Intelligence (AI) in the media sector. As well as contributing to the wider discussion on law and AI, the book also digs deeper by exploring pressing issues at the intersections of AI, media, and the law. Chapters critically re-examine various rights and responsibilities from the perspectives of incentives for accountable utilisation of AI in the industry.Featuring chapters from leading scholars in the field, Artificial Intelligence and the Media provides a timely and in-depth research-based contribution to complex themes - especially at the interface of new technology (including AI) with media and regulation. Analysing both legislative and ethical solutions, chapters explore what “AI” and “accountability” mean in terms of media practices, principles, and power relations, as well as how to address the AI revolution with informed law and policy in order to incentivise accountable utilisation of AI and to reduce negative societal impacts.Offering ideas for further research in the area, this book is key reading for academics and researchers in the fields of information and media law, regulation, and technology law. It may also interest media law practitioners, with research-based guidance for everyday practices and tools to prepare for future developments in the area.Trade Review‘Artificial Intelligence and the Media is an urgently needed contribution to the research on AI and its impacts. While much of the scholarship so far has been field-specific, what makes this volume especially poignant is its multidisciplinary approach to the questions about the roles AI can play for media industries but also for media consumers and users as citizens, and to democracy as a whole.’ -- Minna Aslama Horowitz, University of Helsinki, Finland and St. John's University, USTable of ContentsContents List of contributors vii Introduction to Artificial Intelligence and the Media 1 Taina Pihlajarinne and Anette Alén-Savikko PART I JOURNALISTIC PRINCIPLES AND ARTIFICIAL INTELLIGENCE 1 Bias, journalistic endeavours, and the risks of artificial intelligence 8 M.R. Leiser 2 Transparency in algorithmic journalism: from ethics to law and back 33 Anette Alén-Savikko 3 The journalistic exemption in personal data processing 61 Päivi Korpisaari PART II TRUST, DISINFORMATION AND PLATFORMS 4 Social media platforms as public trustees: an approach to the disinformation problem 93 Philip M. Napoli and Fabienne Graf 5 Artificial intelligence is not a panacea: policing content on social media platforms, three dilemmas and their ethical and legal implications 123 Jingrong Tong 6 The commercial unfairness of recommender systems on social media 148 Catalina Goanta and Gerasimos Spanakis PART III REMITS AND LIMITS OF EXCLUSIVE RIGHTS 7 Creations caused by humans (or robots)? Artificial intelligence and causation requirements for copyright protection in EU law 172 Ole-Andreas Rognstad 8 Artificial intelligence and intellectual property rights: the quest or plea for artificial intelligence as a legal subject 192 Rosa Maria Ballardini and Robert van den Hoven van Genderen 9 The European copyright system as a suitable incentive for AI-based journalism? 215 Taina Pihlajarinne, Alexander Thesleff, Leo Leppänen and Sini Valmari 10 Press publishers’ right and artificial intelligence 240 Juha Vesala 11 Access to data for training algorithms in machine learning: copyright law and ‘right-stacking’ 272 Inger B. Ørstavik Conclusions on Artificial Intelligence and the Media 296 Taina Pihlajarinne and Anette Alén-Savikko Index 300

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Book SynopsisAI expert Jerry Kaplan explains how generative AI will revolutionize virtually every human activity. Highly recommended. - Francis Fukuyama, Political scientist and author of The End of History and the Last Man Advances in Generative Artificial Intelligence (GAI) have created a new class of computer systems that exhibit astonishing proficiency on a wide variety of tasks with superhuman performance, producing novel text, images, music, and software by analyzing enormous collections of digitized information. Soon, these systems will provide expert medical care; offer legal advice; draft documents; write computer programs; tutor our children; and generate music and art. These advances will accelerate progress in science, art, and human knowledge, but they will also bring new dangers. Have we finally discovered the holy grail of AI - machines that match or exceed human intelligence? Which industries and professions will thrive, and which will wither? What risks and dangers will it pose? Ho

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This book explores the integration of Generative AI (GenAI), such as ChatGPT, into educational practices and research methodologies. With the potential to transform traditional learning environments, this book addresses both the opportunities and challenges that come with leveraging GenAI for enhancing teaching and learning experiences.This comprehensive guide delves into multiple aspects of GenAI in education. It covers critical topics such as project-based learning, personalised learning through AI, AI-driven student assessment, and the role of AI in special education. Furthermore, it examines ethical considerations, the readiness of students and teachers for AI technologies, and case studies on AIâs impact on student outcomes. With insights from educators, researchers, and practitioners from around the globe, the book provides a balanced perspective on both theoretical frameworks and practical implementations of GenAI technologies.Ideal for educators, researchers, a

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Book SynopsisThis cutting-edge textbook examines contemporary media business models in the context of Artificial Intelligence (AI) and digital transformation. AI has dramatically impacted media production and distribution, from recommendation engines to synthetic humans, from video-to-text tools to natural language models. AI is really the change agent of the media industry, answered a natural language generation model when AI was asked' about the subject of this book. It will open incredible opportunities. This book seeks to explore them.The media is examined through four sections. Principles' maps business models and the key tools of AI. Platforms' covers distribution channels in Games, Streamers, Social Networks, Broadcast and Digital Publishing. Producers' covers the engines of content-making, including Scripted, Entertainment, Factual, Content Marketing, Creators and Music. Finally, Pioneers' covers emerging sectors of Podcasting, Esports, the Metaverse and other AI-driven develTable of ContentsAcknowledgements. About the Author. Section A: Principles – 1. Introduction, 2. Media Management and Artificial Intelligence, 3. Media Business Models, 4. Overview of AI; Section B: Platforms – 5. Games, 6. Social Networks, 7. Streamers, 8. Broadcasters, 9. Digital Publishers; Section C: Producers – 10. Scripted, 11. Entertainment, 12. Factual, 13. Marketing Content, 14. Creators, 15. Music; Section D: Pioneers - 16. Podcasters, 17. Esports, 18. The Metaverse, 19. Future of the Media Business. Index.

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Book SynopsisFully revised and updated, this comprehensive new edition covers modern AI and machine learning for undergraduate and graduate students. Includes new chapters on deep learning including generative AI, causality and social impact, new social impact sections, major revisions to knowledge graphs, reasoning and decision making, and more AIPython code.Trade Review'This is an important textbook. Based on their broad experience, the authors harmonize some of the most exciting recent developments in the field, such as generative AI, with more traditional methods, within a unified agent framework. This will broaden the perspective of those relatively new to the field, for whom AI and deep learning appear almost synonymous.' Yoav Shoham, Stanford University and AI21 Labs'This book is a tour de force. It provides a comprehensive introduction to so many topics in modern AI. The clarity of the exposition and the ability to capture the intuition underlying complex concepts make this book compelling and appealing to a broad audience.' Pascal Van Hentenryck, Georgia Institute of Technology'This new edition offers an up-to-date account of AI, presenting the field in an accessible and unified manner. I particularly like the 'relations-late' approach, in which first-order logic and relational AI are covered later, after thoroughly covering more basic, feature-based methods. The hybrid data-driven/model-based approach to agent design that the authors propose will be essential to the development of reliable and trustworthy intelligent systems.' Kevin Patrick Murphy, Google Brain, author of Probabilistic Machine Learning'Poole and Mackworth's now classic textbook has guided my senior undergraduate AI class since its first edition. Coupled with online resources, the book presents a comprehensive overview, with technical substance and many pointers for further study, in a coherent structure that fosters learning of key interrelated concepts. The third edition updates the content to cover the massive recent AI advances.' Jesse Hoey, University of Waterloo'Machine learning has undergone spectacular advances over the last few years, but to harvest the new capabilities one needs an engineering framework to build computational agents. This book teaches students about the concepts and techniques that make that possible.' Rodney Brooks, MIT and Robust AI'Wide-ranging, well-organized, up-to-date, and in-depth coverage of the AI world. The numerous figures, algorithms, and extensive references make this a valuable resource that readers will return to repeatedly. Instructors and students will benefit from the well-crafted end-of-chapter exercises. The thought-provoking social impact sections in each chapter and the social impact chapter admirably address the positive and harmful impacts on people. These complement the strong technical descriptions, wisely encouraging researchers and practitioners to limit the risks by highlighting human-centred AI. Poole and Mackworth are highly acclaimed experts who eagerly present their subject with enthusiasm and thoroughness.' Ben Shneiderman, University of Maryland, author of Human-Centered AI'This revised and extended edition of Artificial Intelligence: Foundations of Computational Agents should become the standard text of AI education. Computer science students will find in this volume a broad and uniquely coherent perspective on many computational models of learning, reasoning, and decision-making. Students of causal inference, in particular, will rejoice at viewing the causal revolution reconnected to its roots in formal logic and probabilistic decision-making, strengthened and reinforced by concrete algorithms, challenging exercises, and open source AIPython codes. Highly recommended.' Judea Pearl, UCLA, Turing Award winner and author of Causality and The Book of Why'This textbook is impressively comprehensive, covering all the major AI paradigms that have been introduced and studied over the years. At the same time, it is up to date with the latest technical advances and interdisciplinary perspectives on social impacts. I expect it to be a valuable resource for both teachers and students.' Peter Stone, University of Texas at Austin'Artificial Intelligence: Foundations of Computational Agents is a great AI textbook written by prominent leaders in the field. It covers everything you want to know about AI in a very accessible style, accompanied by a wide range of thoughtful and challenging exercises. I find this book to be an extremely valuable resource, not only for teaching, but even more so for offering an updated reference to a wide spectrum of foundational subjects at the current frontier of AI.' Rina Dechter, University of California, Irvine, author of Constraint Programming'Poole and Mackworth's book has been my go-to resource for students who need an introduction to Artificial Intelligence. While the previous versions have provided a complete overview of the field, the newer version organizes this information in a crystal clear manner. The division of the topics based on what the agent knows, what is in the world, and what the effects of its actions are allow for a logical flow of topics inside AI. As a comprehensive textbook for AI that includes slides, solutions, and code, this book is a must-have on the bookshelf for AI instructors, students, researchers, and practitioners.' Sriraam Natarajan, University of Texas at Dallas'This is a great foundational book on the science of AI, covering the main concepts and techniques using a simple structured approach. The extensive material on the social impact of AI provides much needed attention to the responsible design and use of AI. AI researchers can find here the indispensable foundational knowledge and the needed ethical attitude to create beneficial AI innovation.' Francesca Rossi, IBM Fellow'The latest edition of Poole and Mackworth's book emphasizes the societal impacts of AI in every chapter, making it an essential read for anyone interested in AI, especially those who will shape its future to ensure these powerful technologies benefit society and minimize harms.' Saleema Amershi, Microsoft Research'This textbook provides an amazing introduction to the field of AI. By bringing together learning, reasoning, and decision-making, it shows the rich interconnections across the various AI subfields. The writing is just at the right level to introduce students to the different facets of AI. The updated edition seamlessly integrates the exciting developments in deep learning into the broader AI context. The text also highlights the societal impact of AI, including AI ethics and computational sustainability.' Carla Gomes, Cornell University'Poole and Mackworth - two pioneers of AI - present an admirably broad and complete introduction to the field, with a very useful focus on intelligent agents. From deep learning to causal reasoning, from Bayesian networks to knowledge graphs, from fundamental algorithms to effective heuristics, this book covers a wide range of important topics, each accompanied by a timely section on social impact. Highly recommended!' Holger Hoos, RWTH Aachen'Poole and Mackworth's Artificial Intelligence: Foundations of Computational Agents 3e is a tour de force. This is a comprehensive and clearly written text that takes the reader through core concepts in symbolic AI and machine learning, providing pathways for broad introductory undergraduate courses, or focused graduate courses. It's an outstanding resource for student and instructor alike. Whether you're a seasoned AI researcher or a student entering the field, you'll learn a great deal from reading this book.' Sheila McIlraith, University of TorontoTable of ContentsPreface; Part I. Agents in the World: 1. Artificial intelligence and agents; 2. Agent architectures and hierarchical control; Part II. Reasoning and Planning with Certainty: 3. Searching for solutions; 4. Reasoning with constraints; 5. Propositions and inference; 6. Deterministic planning; Part III. Learning and Reasoning with Uncertainty: 7. Supervised machine learning; 8. Neural networks and deep learning; 9. Reasoning with uncertainty; 10. Learning with uncertainty; 11. Causality; Part IV. Planning and Acting with Uncertainty; 12. Planning with uncertainty; 13. Reinforcement learning; 14. Multiagent systems; Part V. Representing Individuals and Relations: 15. Individuals and relations; 16. Knowledge graphs and ontologies; 17. Relational learning and probabilistic reasoning; Part VI. The Big Picture: 18. The social impact of artificial intelligence; 19. Retrospect and prospect; Appendices; References; Index of Algorithms; Index.

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Book SynopsisThis cutting-edge textbook examines contemporary media business models in the context of Artificial Intelligence (AI) and digital transformation. AI has dramatically impacted media production and distribution, from recommendation engines to synthetic humans, from video-to-text tools to natural language models. AI is really the change agent of the media industry, answered a natural language generation model when AI was asked' about the subject of this book. It will open incredible opportunities. This book seeks to explore them.The media is examined through four sections. Principles' maps business models and the key tools of AI. Platforms' covers distribution channels in Games, Streamers, Social Networks, Broadcast and Digital Publishing. Producers' covers the engines of content-making, including Scripted, Entertainment, Factual, Content Marketing, Creators and Music. Finally, Pioneers' covers emerging sectors of Podcasting, Esports, the Metaverse and other AI-driven develTable of ContentsAcknowledgements. About the Author. Section A: Principles – 1. Introduction, 2. Media Management and Artificial Intelligence, 3. Media Business Models, 4. Overview of AI; Section B: Platforms – 5. Games, 6. Social Networks, 7. Streamers, 8. Broadcasters, 9. Digital Publishers; Section C: Producers – 10. Scripted, 11. Entertainment, 12. Factual, 13. Marketing Content, 14. Creators, 15. Music; Section D: Pioneers - 16. Podcasters, 17. Esports, 18. The Metaverse, 19. Future of the Media Business. Index.

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Book SynopsisWill the use of artificial intelligence, algorithms, and smart machines be the end of journalism as we know itor its savior? Francesco Marconi, who has led the development of the Associated Press and Wall Street Journal's use of AI in journalism, offers a new perspective on the potential of these technologies.Trade ReviewFrancesco Marconi has it right. Artificial intelligence will augment—not automate—the news industry. Human judgment will be enhanced, not replaced. When you finish this book you do not fear the AI future in newsrooms. You have the tools to wonder what we will soon be able to do. -- Jay Rosen, New York UniversityOne part history, one part management strategy, and one part vision, Newsmakers provides readers with a detailed roadmap of how journalism workflows and content will change through the AI inspired process of iterative journalism. The result will be coverage that adjusts to readers’ information needs in real-time and increases the scale and scope of reporting. -- Jay Hamilton, Stanford UniversityIf you are a newsmaker or anyone interested in the future of journalism then this book is the perfect guide. Marconi is at the cutting edge of using AI technologies in the newsroom and one of the most intelligent strategic analysts of how they can help journalism survive and thrive in our radically changing digital media age. From news gathering to connecting to audiences he shows the plethora of opportunities and challenges presented by this complex set of tools and systems. If you are excited by or fearful of the prospect of the 'robot' age of news, this book will give you the facts and ideas to grapple with this rapidly evolving field. -- Charlie Beckett, London School of EconomicsNewsmakers explores human-machine collaboration in the future of news. Marconi offers a practical perspective of how journalists can be directly involved in training, testing and deploying algorithms. A valuable guide for journalists who want to stay in the drivers’ seat of making news and leverage emerging AI-powered tools to unlock new storytelling opportunities. -- Deb Roy, Massachusetts Institute of TechnologyMarconi’s book will help journalists start thinking about some of the exciting ways AI can improve and streamline their work and how to implement these new technologies in the newsroom. If we are going to create a sustainable future for journalism, this is exactly what we need to be thinking about: putting audience needs and rapid iteration at the center of everything we do. -- Carrie Brown, City University of New YorkIn an era when machine learning is being applied to optimize decisions in countless other industries, Newsmakers examines the richness and complexity of using these tools to make dynamic, personalized, and impactful choices about the stories we offer our readers. For Marconi, it is not journalism automated by computation, but rather journalism augmented. Machine learning changes the ways a reporter sees the world around her, pieces a story together, and builds an audience in a complex information ecosystem. Newsmakers presents a view of journalism that is explicitly iterative, experimental and collaborative. -- Mark Hansen, Columbia UniversityA must read for all journalists and media scholars, this book provides a clear pathway for understanding AI in the newsmaking process. * Journalism and Mass Communication Quarterly *Table of ContentsPrefaceWhat Is This Book About, According to AI? Introduction: Technology Moves Faster Than Journalistic Standards1. The Problem: A Journalistic Model in Transition2. Enablers: The AI Technologies Driving Journalistic Change3. Workflow: A Scalable Process for Newsroom TransformationConclusionAcknowledgmentsNotesBibliographyIndex

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Book SynopsisLos modelos de inteligencia artificial generativa (IAG) y de lenguajes de inteligencia artificial son cada vez más conocidos por sus incomparables capacidades. Este volumen ofrece información sobre el funcionamiento interno de los LLM o grandes modelos de lenguaje y una guía para la creación de modelos de lenguaje propios. Comienza con una introducción al campo de la IAG que le permitirá comprender cómo se entrenan estos modelos para generar nuevos datos. Tendrá además la oportunidad de explorar casos prácticos en los que ChatGPT ha logrado mejorar la productividad y fomentar la creatividad. Aprenderá cómo sacar el máximo partido de sus interacciones con ChatGPT enriqueciendo el diseño de prompts y aprovechando las capacidades de aprendizaje zero-shot, one-shot y few-shot, es decir, con cero, uno y pocos ejemplos por clase. Los casos prácticos están agrupados por los ámbitos de técnicos de marketing, investigadores y desarrolladores o científicos de datos, lo que le permitirá ap

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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

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Book SynopsisThe past decade has witnessed extraordinary advances in artificial intelligence. But what precisely is it and where does its future lie?In this brilliant, one-stop guide WIRED journalist Matt Burgess explains everything you need to know about AI. He describes how it works. He looks at the ways in which it has already brought us everything from voice recognition software to self-driving cars, and explores its potential for further revolutionary change in almost every area of our daily lives. He examines the darker side of machine learning: its susceptibility to hacking; its tendency to discriminate against particular groups; and its potential misuse by governments. And he addresses the fundamental question: can machines become as intelligent as human beings?Trade ReviewIn this book Burgess manages to cover all the key AI trends and developments over the last 60 years . . . delivers an informative and readable guide to all the main events that have taken place to date . . . We found this one helpful for a new or general reader and would recommend it to those looking for a good place to start in this field. * Irish Tech News *

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Book SynopsisArtificial Intelligence Applications in K- 12 offers authentic instances of how AI systems can be integrated into K- 12 education today. As AI technologies rapidly evolve and become more accessible to primary, middle, and high schools worldwide, there is a pressing need for new demonstrations that highlight the challenges, opportunities, and ethical considerations associated with these powerful tools. This book explores the various roles of AI within pedagogy and assessment, school administration, student data management, and beyond. Its collected case studies present practical ideas for enhancing educational institutions and offer replicable approaches across a range of learning priorities, from fostering motivation and engagement to improving feedback and achieving educational goals. Researchers, faculty members of teacher and leadership preparation programs, curriculum and instruction specialists, school- based instructional designers, technology coaches, and other readers

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Book SynopsisXAI in Computer Vision.- Comparing XAI Explanations and Synthetic Data Augmentation Strategies in Neuroimaging AI.- Superpixel Correlation for Explainable Image Classification.- On Background Bias of Post-Hoc Concept Embeddings in Computer Vision DNNs.- Explaining Vision GNNs: A Semantic and Visual Analysis of Graph-based Image Classification.- Counterfactuals in XAI.- HalCECE: A Framework for Explainable Hallucination Detection through Conceptual Counterfactuals in Image Captioning.- Diffusion Counterfactuals for Image Regressors.- Mitigating Text Toxicity with Counterfactual Generation.- Guiding LLMs to Generate High-Fidelity and High-Quality Counterfactual Explanations for Text Classification.- Exploring Ensemble Strategies for Graph Counterfactual Explanations.- Explainable Sequential Decision Making.- Leveraging XAI Techniques for Context-Aware Energy Consumption Forecasting.- ConformaSegment: A Conformal Prediction-Based, Uncertainty-Aware, and Model-Agnostic Explainability Framework for Time-Series Forecasting.- FLEXtime: Filterbank Learning to Explain Time Series.- From Text to Space: Mapping Abstract Spatial Models in LLMs during a Grid-World Navigation Task.- Class-Dependent Perturbation Effects in Evaluating Time Series Attributions.- Explainable AI in Finance & Legal Frameworks for XAI Technologies.- XAI In Fraud Detection: A Causal Perspective.- Detecting Fraud in Financial Networks: A Semi-Supervised GNN Approach with Granger-Causal Explanations.- Legal Requirements, Trust Issues and Engineering Challenges - a Multi-Disciplinary Case for User-Specific Explainability.- Explainable Fairness in Mortgage Lending.- Cyber Risk Management with Time Varying Artificial Intelligence Models.

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Book SynopsisARTIFICAL INTELLIGENCE for SUSTAINABLE APPLICATIONS The objective of this book is to leverage the significance of artificial intelligence in achieving sustainable solutions using interdisciplinary research through innovative ideas. With the advent of recent technologies, the demand for Information and Communication Technology (ICT)-based applications such as artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), health care, data analytics, augmented reality/virtual reality, cyber-physical systems, and future generation networks, has increased drastically. In recent years, artificial intelligence has played a more significant role in everyday activities. While AI creates opportunities, it also presents greater challenges in the sustainable development of engineering applications. Therefore, the association between AI and sustainable applications is an essential field of research. Moreover, the applications of sustainable products have Table of ContentsPreface xv Part I: Medical Applications 1 1 Predictive Models of Alzheimer's Disease Using Machine Learning Algorithms -- An Analysis 3Karpagam G. R., Swathipriya M., Charanya A. G. and Murali Murugan 1.1 Introduction 3 1.2 Prediction of Diseases Using Machine Learning 4 1.3 Materials and Methods 5 1.4 Methods 6 1.5 ML Algorithm and Their Results 7 1.6 Support Vector Machine (SVM) 11 1.7 Logistic Regression 11 1.8 K Nearest Neighbor Algorithm (KNN) 12 1.9 Naive Bayes 15 1.10 Finding the Best Algorithm Using Experimenter Application 17 1.11 Conclusion 18 1.12 Future Scope 19 2 Bounding Box Region-Based Segmentation of COVID-19 X-Ray Images by Thresholding and Clustering 23Kavitha S. and Hannah Inbarani 2.1 Introduction 23 2.2 Literature Review 24 2.3 Dataset Used 26 2.4 Proposed Method 26 2.5 Experimental Analysis 29 2.6 Conclusion 33 3 Steering Angle Prediction for Autonomous Vehicles Using Deep Learning Model with Optimized Hyperparameters 37Bineeshia J., Vinoth Kumar B., Karthikeyan T. and Syed Khaja Mohideen 3.1 Introduction 38 3.2 Literature Review 39 3.3 Methodology 41 3.4 Experiment and Results 46 3.5 Conclusion 51 4 Review of Classification and Feature Selection Methods for Genome-Wide Association SNP for Breast Cancer 55L.R. Sujithra and A. Kuntha 4.1 Introduction 56 4.2 Literature Analysis 58 4.3 Comparison Analysis 66 4.4 Issues of the Existing Works 70 4.5 Experimental Results 70 4.6 Conclusion and Future Work 73 5 COVID-19 Data Analysis Using the Trend Check Data Analysis Approaches 79Alamelu M., M. Naveena, Rakshitha M. and M. Hari Prasanth 5.1 Introduction 79 5.2 Literature Survey 80 5.3 COVID-19 Data Segregation Analysis Using the Trend Check Approaches 81 5.4 Results and Discussion 83 5.5 Conclusion 86 6 Analyzing Statewise COVID-19 Lockdowns Using Support Vector Regression 89Karpagam G. R., Keerthna M., Naresh K., Sairam Vaidya M., Karthikeyan T. and Syed Khaja Mohideen 6.1 Introduction 90 6.2 Background 91 6.3 Proposed Work 98 6.4 Experimental Results 104 6.5 Discussion and Conclusion 110 7 A Systematic Review for Medical Data Fusion Over Wireless Multimedia Sensor Networks 117John Nisha Anita and Sujatha Kumaran 7.1 Introduction 118 7.2 Literature Survey Based on Brain Tumor Detection Methods 118 7.3 Literature Survey Based on WMSN 122 7.4 Literature Survey Based on Data Fusion 123 7.5 Conclusions 125 Part II: Data Analytics Applications 127 8 An Experimental Comparison on Machine Learning Ensemble Stacking-Based Air Quality Prediction System 129P. Vasantha Kumari and G. Sujatha 8.1 Introduction 130 8.2 Related Work 133 8.3 Proposed Architecture for Air Quality Prediction System 134 8.4 Results and Discussion 140 8.5 Conclusion 145 9 An Enhanced K-Means Algorithm for Large Data Clustering in Social Media Networks 147R. Tamilselvan, A. Prabhu and R. Rajagopal 9.1 Introduction 148 9.2 Related Work 149 9.3 K-Means Algorithm 151 9.4 Data Partitioning 152 9.5 Experimental Results 154 9.6 Conclusion 159 10 An Analysis on Detection and Visualization of Code Smells 163Prabhu J., Thejineaswar Guhan, M. A. Rahul, Pritish Gupta and Sandeep Kumar M. 10.1 Introduction 164 10.2 Literature Survey 165 10.3 Code Smells 168 10.4 Comparative Analysis 170 10.5 Conclusion 174 11 Leveraging Classification Through AutoML and Microservices 177M. Keerthivasan and V. Krishnaveni 11.1 Introduction 178 11.2 Related Work 179 11.3 Observations 181 11.4 Conceptual Architecture 181 11.5 Analysis of Results 190 11.6 Results and Discussion 193 Part III: E-Learning Applications 197 12 Virtual Teaching Activity Monitor 199Sakthivel S. and Akash Ram R.K. 12.1 Introduction 199 12.2 Related Works 203 12.3 Methodology 206 12.4 Results and Discussion 213 12.5 Conclusions 215 13 AI-Based Development of Student E-Learning Framework 219S. Jeyanthi, C. Sathya, N. Uma Maheswari, R. Venkatesh and V. Ganapathy Subramanian 13.1 Introduction 220 13.2 Objective 220 13.3 Literature Survey 221 13.4 Proposed Student E-Learning Framework 222 13.5 System Architecture 223 13.6 Working Module Description 224 13.7 Conclusion 228 13.8 Future Enhancements 228 Part IV: Networks Application 231 14 A Comparison of Selective Machine Learning Algorithms for Anomaly Detection in Wireless Sensor Networks 233Arul Jothi S. and Venkatesan R. 14.1 Introduction 234 14.2 Anomaly Detection in WSN 236 14.3 Summary of Anomaly Detections Techniques Using Machine Learning Algorithms 237 14.4 Experimental Results and Challenges of Machine Learning Approaches 238 14.5 Performance Evaluation 244 14.6 Conclusion 246 15 Unique and Random Key Generation Using Deep Convolutional Neural Network and Genetic Algorithm for Secure Data Communication Over Wireless Network 249S. Venkatesan, M. Ramakrishnan and M. Archana 15.1 Introduction 250 15.2 Literature Survey 252 15.3 Proposed Work 253 15.4 Genetic Algorithm (GA) 253 15.5 Conclusion 261 Part V: Automotive Applications 265 16 Review of Non-Recurrent Neural Networks for State of Charge Estimation of Batteries of Electric Vehicles 267R. Arun Chendhuran and J. Senthil Kumar 16.1 Introduction 267 16.2 Battery State of Charge Prediction Using Non -Recurrent Neural Networks 268 16.3 Evaluation of Charge Prediction Techniques 272 16.3 Conclusion 273 17 Driver Drowsiness Detection System 275G. Lavanya, N. Sunand, S. Gokulraj and T.G. Chakaravarthi 17.1 Introduction 275 17.2 Literature Survey 276 17.3 Components and Methodology 277 17.4 Conclusion 281 Part VI: Security Applications 283 18 An Extensive Study to Devise a Smart Solution for Healthcare IoT Security Using Deep Learning 285Arul Treesa Mathew and Prasanna Mani 18.1 Introduction 285 18.2 Related Literature 286 18.3 Proposed Model 291 18.4 Conclusions and Future Works 292 19 A Research on Lattice-Based Homomorphic Encryption Schemes 295Anitha Kumari K., Prakaashini S. and Suresh Shanmugasundaram 19.1 Introduction 295 19.2 Overview of Lattice-Based HE 296 19.3 Applications of Lattice HE 299 19.4 NTRU Scheme 301 19.5 GGH Signature Scheme 303 19.6 Related Work 304 19.5 Conclusion 308 20 Biometrics with Blockchain: A Better Secure Solution for Template Protection 311P. Jayapriya, K. Umamaheswari and S. Sathish Kumar 20.1 Introduction 311 20.2 Blockchain Technology 313 20.3 Biometric Architecture 317 20.4 Blockchain in Biometrics 320 20.4.1 Template Storage Techniques 322 20.5 Conclusion 324 References 324 Index 329

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Book SynopsisThe Black Box of Computational Imaging.- Algorithmic Mediascapes.- Algorithms and the Arts.- Cultural Critique of Algorithmic Media.- Posthuman/Non-Human Imagery.

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Book SynopsisFurthermore, the book will show how to build Machine Learning models for a variety of use cases in image recognition, video object recognition, and data prediction.

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Book SynopsisDiscover the next major revolution in data science and AI and how it applies to your organization In Causal Artificial Intelligence: The Next Step in Effective, Efficient, and Practical AI, a team of dedicated tech executives delivers a business-focused approach based on a deep and engaging exploration of the models and data used in causal AI. The book's discussions include both accessible and understandable technical detail and business context and concepts that frame causal AI in familiar business settings. Useful for both data scientists and business-side professionals, the book offers: Clear and compelling descriptions of the concept of causality and how it can benefit your organization Detailed use cases and examples that vividly demonstrate the value of causality for solving business problems Useful strategies for deciding when to use correlation-based approaches and when to use causal inference An enlightening and easTable of ContentsForeword xix Preface xxiii Introduction xxix Chapter 1 Setting the Stage for Causal AI 1 Why Causality Is a Game Changer 2 Causal AI in Perspective with Analytics 7 Analytical Sophistication Model 8 Analytics Enablers 10 Analytics 10 Advanced Analytics 11 Scope of Services to Support Causal AI 11 The Value of the Hybrid Team 13 The Promise of AI 14 Understanding the Core Concepts of Causal AI 15 Explainability and Bias Detection 15 Explainability 17 Detecting Bias in a Model 17 Directed Acyclic Graphs 18 Structural Causal Model 19 Observed and Unobserved Variables 20 Counterfactuals 21 Confounders 21 Colliders 22 Front- Door and Backdoor Paths 23 Correlation 24 Causal Libraries and Tools 25 Propensity Score 25 Augmented Intelligence and Causal AI 26 Summary 27 Note 27 Chapter 2 Understanding the Value of Causal AI 29 Defining Causal AI 30 The Origins of Causal AI 33 Why Causality? 34 Expressing Relationships 37 The Ladder of Causation 38 Rung 1: Association, or Passive Observation 40 Rung 2: Intervention, or Taking Action 40 Rung 3: Counterfactuals, or Imagining What If 42 Why Causal AI Is the Next Generation of AI 43 Deep Learning and Neural Networks 43 Neural Networks 44 Establishing Ground Truth 45 The Business Imperative of a Causal Model 46 The Importance of Augmented Intelligence 51 The Importance of Data, Visualization, and Frameworks 52 Getting the Appropriate Data 52 Applying Data and Model Visualization 55 Applying Frameworks After Creating a Model 56 Getting Started with Causal AI 57 Summary 58 Notes 59 Chapter 3 Elements of Causal AI 61 Conceptual Models 62 Correlation vs. Causal Models 63 Correlation- Based AI 63 Causal AI 63 Understanding the Relationship Between Correlation and Causality 64 Process Models 66 Correlation- Based AI Process Model 67 Causal- Based AI Process Model 69 Collaboration Between Business and Analytics Professionals 72 The Fundamental Building Blocks of Causal AI Models 75 The Relations Between DAGs and SCMs 76 Explaining DAGs 76 Causal Notation: The Language of DAGs 78 Operationalizing a DAG with an SCM 79 The Elements of Visual Modeling 81 Nodes 83 Variables 83 Endogenous and Exogenous Variables 83 Observed and Unobserved Variables 84 Paths/Relationships 84 Weights 86 Summary 88 Notes 89 Chapter 4 Creating Practical Causal AI Models and Systems 91 Understanding Complex Models 92 Causal Modeling Process: Part 1 94 Step 1: What Are the Intended Outcomes? 95 Step 2: What Are the Proposed Interventions? 97 Step 3: What Are the Confounding Factors? 99 Step 4: What Are the Factors Creating the Effects and Changes? 102 Common/Universal Effects in a Causal Model 102 Refined Effects in a Causal Model 103 Step 5: Creating a Directed Acyclic Graph 105 Step 6: Paths and Relationships 105 Types of Paths 106 Path Connecting an Unobserved Variable 107 Front- Door Paths 108 Backdoor Paths 108 Modeling for Simplicity to Understand Complexity 109 Step 7: Data Acquisition 110 Causal- Based Approach: Part 2 112 Step 8: Data Integration 113 Step 9: Model Modification 114 Step 10: Data Transformation 115 Step 11: Preparing for Deployment in Business 118 Summary 121 Notes 122 Chapter 5 Creating a Model with a Hybrid Team 125 The Hybrid Team 126 Why a Hybrid Team? 127 The Benefits of a Hybrid Team 128 Establishing the Hybrid Team as a Center of Excellence 129 How Teams Collaborate 131 But Why? 132 Defining Roles 134 Leaders and Business Strategists 137 Subject- Matter Experts 138 Data Experts 140 Software Developers 142 Business Process Analysts 143 Information Technology Expertise 143 Project Manager(s) 144 The Basics Steps for a Hybrid Team Project 145 An Overview of Model Creation 146 It Depends on Your Destination 150 Understanding the Root Cause of a Problem 151 Understanding What Happened and Why 153 The Importance of the Iterative Process 154 Summary 155 Chapter 6 Explainability, Bias Detection, and AI Responsibility in Causal AI 157 Explainability 158 The Ramifications of the Lack of Explainability 159 What Is Explainable AI in Causal AI Models? 161 Black Boxes 162 Internal Workings of Black-Box Models 162 Deep Learning at the Heart of Black Boxes 163 Is Code Understandable? 163 The Value of White-Box Models 166 Understanding Causal AI Code 167 Techniques for Achieving Explainability 169 Challenges of Complex Causal Models 169 Methods for Understanding and Explaining Complex Causal AI Models 171 The Importance of the SHAP Explainability Method 172 Detecting Bias and Ensuring Responsible AI 175 Bias in Causal AI Systems 176 Responsible AI: Trust and Fairness 178 How Causal AI Addresses Bias Detection 180 Tools for Assessing Fairness and Bias 182 The Human Factor in Bias Detection and Responsible AI 183 Summary 184 Note 184 Chapter 7 Tools, Practices, and Techniques to Enable Causal AI 185 The Causal AI Pipeline 187 Define Business Objectives 190 Model Development 193 Data Identification and Collection 195 Data Privacy, Governance, and Security 197 Synthetic Data 198 Model Validation 199 Deployment/Production 201 Monitor and Evaluate 203 Update and Iterate 205 Continuous Learning 208 The Importance of Synthetic Data 210 Why Create Synthetic Data? 210 Overcoming Data Limitations 211 Enhancing Data Privacy and Security 211 Model Validation and Testing 211 Expanding the Range of Possible Scenarios 212 Reducing the Cost of Data Collection 212 Improving Data Imbalance 213 Encouraging Collaboration and Openness 213 Streamlining Data Preprocessing 213 Supporting Counterfactual Analysis 213 Fostering Innovation and Experimentation 214 Creating Synthetic Data 214 Generative Models 214 Agent-Based Modeling 215 Data Augmentation 215 Data Synthesis Tools and Platforms 215 Conditional Synthetic Data Generation 216 Synthetic Data from Text 216 The Limitations of Synthetic Data 217 Current State of Tools and Software in Causal AI 218 The Role of Open Source in Causal AI 218 Commercial Causal AI Software 221 CausaLens 221 Geminos Software 223 Summary 223 Chapter 8 Causal AI in Action 225 Enterprise Marketing in a Business- to- Consumer Scenario 226 DDCo Marketing Causal Model: Annual Pricing Review and Update Cycle 228 Incorporating Internal and External Factors in the Model and DAG 230 Easily Enabling Iterating 231 End-User-Driven Exploration 232 Bench Testing 234 DDCo Marketing Causal Model: Semiannual Product Planning Cycle 236 Always Consider Model Reuse 237 Give and Take in Building a New Model 239 Typical Model and Process Operation: Iterating 239 Keeping the Process/Model Scope Manageable and Understandable 240 Moving from Strategy to Building and Implementing Causal AI Solutions 241 Agriculture: Enhancing Crop Yield 242 Key Causal Variables 244 Creating the DAG 246 Moving from the DAG to Implementing the Causal AI Model 247 Commercial Real Estate: Valuing Warehouse Space 250 Key Causal Variables 251 Implementing the Causal AI Model 253 Video Streaming: Enhancing Content Recommendations 254 Key Causal Variables 255 Implementing the Causal AI Model 256 Healthcare: Reducing Infant Mortality 258 Key Causal Variables 259 Implementing the Causal AI Model 261 Retail: Providing Executives Actionable Information 263 Key Causal Variables 264 Implementing the Causal Model 265 Summary 267 Chapter 9 The Future of Causal AI 271 Where We Stand Today 271 Foundations of Causal AI 273 The Causal AI Journey 274 Causal AI Today 274 What’s Next for Causal AI 276 Integrating Causal AI and Traditional AI 278 The Imperative for Managing Data 279 Ensembles of Data 279 Generative AI Is Emerging as a Game Changer for Causal AI 281 The Future of Causal Discovery 282 The Emergence of Causal AI Reinforcement Learning Will Accelerate Model Training 284 Causal AI as a Common Language Between Business Leaders and Data Scientists 284 The Emergence of Probabilistic Programming Languages 286 The Predictable Model Evolution Cycle 286 The Emergence of the Digital Twin 287 Improving the Ability to Understand Ground Truth 289 The Development of More Sophisticated DAGs 289 Visualizing Complex Relationships in the DAGs 290 The Merging of Causal and Traditional AI Models 291 The Future of Explainability 291 The Evolution of Responsible AI 292 Advances in Data Security and Privacy 293 Integration Will Be Between Models and Business Applications 294 Summary 295 Glossary 299 Appendix 313 Selected Resources 329 Acknowledgments 331 About the authors 335 About the contributor 339 Index 341

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Book SynopsisFuzzy Low-Pass Filtering Approach for Mammogram Image Enhancement in Breast Cancer Identi?cation.- Systematic Review on Machine Learning Algorithms in Image Forgery Detection.- Enhanced Watermark Text Classi?cation Using Extended MobileNetV2 Model.- Feature Descriptors and Selection Techniques Analysis for Signature Veri?cation using Recommender Systems.- Survey Paper on Text Echo: Personalized TTS System.

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Book SynopsisWhy are we here? What does it mean to love? How do we overcome suffering? Why do we feel so alone? Is happiness truly possible? For thousands of years, humanity has turned to the same sacred texts to explore these universal questions — from the Bible and the Tao Te Ching, to the poetry of Rumi and Sappho, to modern-day mystics. What if you could take all of the wisdom contained within these collective pages and, using the world’s most advanced artificial intelligence, receive the answers? Thanks to OpenAI, a nonprofit research lab cofounded by Elon Musk and other tech luminaries, this is now possible. OpenAI’s mission is to ensure that artificial intelligence serves the betterment of humanity — and What Makes Us Human? offers an innovative exploration of AI’s potential in this realm. To create this first-of-its-kind book, international bestselling poet Iain S. Thomas and prodigious researcher and innovator Jasmine Wang collaborated with OpenAI’s GPT-3, an advanced artificial intelligence. Wang and Thomas prompted GPT-3 with thousands of humanity’s greatest texts, and then asked GPT-3 our most pressing questions. Contained in this book are the conversations and exchanges that followed. A bold, daring experiment, What Makes Us Human? is a contemporary exploration of spirituality that will inspire a new understanding of what makes us humans, humans.

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

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Book SynopsisAn essential companion for understanding the decision theory underlying artificial intelligence, how to model the impacts of artificial intelligence on economic growth, productivity, labour markets and inequality, and how to make sense of the debate whether AI poses an existential risk.

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Book SynopsisApplications of XAI.- Global Explanations of Expected Goal Models in Football.- Comprehensive Explanations Using Natural Language Queries.- A Human-in-the-Loop Approach to Learning Social Norms as Defeasible Logical Constraints.- A Cautionary Tale About ''Neutrally'' Informative AI Tools Ahead of the 2025 Federal Elections in Germany.- Human-Centered XAI & Argumentation.- Evaluating Argumentation Graphs as Global Explainable Surrogate Models for Dense Neural Networks and their Comparison with Decision Trees.- Mind the XAI Gap: A Human-Centered LLM Framework for Democratizing Explainable AI.- Explanations for Medical Diagnosis Predictions Based on Argumentation Schemes.- Spectral Occlusion - Attribution Beyond Spatial Relevance Heatmaps.- Non-experts' Trust in XAI is Unreasonably High.- Explainable and Interactive Hybrid Decision Making.- Exploring Annotator Disagreement in Sexism Detection: Insights from Explainable AI.- Can You Regulate Your Emotions? An Empirical Investigation of the Influence of AI Explanations and Emotion Regulation on Human Decision-Making Factors.- When Bias Backfires: The Modulatory Role of Counterfactual Explanations on the Adoption of Algorithmic Bias in XAI-Supported Human Decision-Making.- Understanding Disagreement Between Humans and Machines in XAI: Robustness, Fidelity, and Region-Based Explanations in Automatic Neonatal Pain Assessment.- On Combining Embeddings, Ontology and LLM to Retrieve Semantically Similar Quranic Verses and Generate their Explanations.- Uncertainty in Explainable AI.- Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification.- Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning Models.- Explaining Low Perception Model Competency with High-Competency Counterfactuals.- Uncertainty Propagation in XAI: A Comparison of Analytical and Empirical Estimators.

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Book SynopsisThis book introduces the subject of Artificial Intelligence (AI), its structures, its uses, its application to the Artificial Neural Network, its technology applications, and the advantages and disadvantages of its use, including its potential weaknesses and drawbacks. Artificial Intelligence, in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. Such machines may be called AIs. AI technology is widely used throughout industry, government, and science.Some high-profile applications include advanced web search engines (e.g., Google Search); recommendation systems (used by facilities such as YouTube, Amazon, and Netflix); interacting via human speech (e.g., Google Assistant, Siri, and Alexa); autonomous vehicles (e.g. Waymo); generative and creative tools (e.g., ChatGPT, Apple Intelligence, and AI art); and superhuman play and analysis in strategy games (e.g., chess and Go). However, many AI applications are not perceived as AI: For example, a lot of forefronts and cutting-edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough, it is no longer labeled as AI anymore.The growing use of artificial intelligence in the 21st century is influencing a societal and economic shift towards increased automation, data-driven decision-making, and the integration of AI systems into various economic and commercial sectors and areas of everyday life, impacting job markets, healthcare, government, industry, education, propaganda, and disinformation and deception, whether deliberate of otherwise. This raises questions about the long-term effects, ethical implications, and risks of AI, prompting discussions about regulatory policies to ensure the safety and benefits of the technology.

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Book SynopsisThis book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems.Table of Contents1. An Artificial Intelligence System Based Power Estimation Method for CMOS VLSI Circuits 2. Awareness Alert and Information Analysis in Social Media Networking Using Usage Analysis and Negotiable Approach 3. Object Detection and Tracking in Video Using Deep Learning Techniques: A Review 4. Fuzzy MCDM: Application in Disease Risk and Prediction 5. Deep Learning Approach to Predict and Grade Glaucoma from Fundus Images through Constitutional Neural Networks 6. A Novel Method for Securing Cognitive Radio Communication Network Using the Machine Learning Schemes and a Rule Based Approaches 7. Detection of Retinopathy of Prematurity Using Convolution Neural Network 8. Impact of Technology on Human Resource Information System and Achieving Business Intelligence in Organizations 9. Proficient Prediction of Acute Lymphoblastic Leukemia Using Machine Learning Algorithm 10. Role of Machine Learning in Social Area Networks 11. Breast Cancer and Machine Learning: Interactive Breast Cancer Prediction Using Naïve Bayes Algorithm 12. Deep Networks and Deep Learning Algorithms 13. Machine Learning for Big Data Analytics, Interactive and Reinforcement 14. Fish Farm Monitoring System Using IoT and Machine Learning

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Book SynopsisA concise but informative overview of AI ethics and policy.Artificial intelligence, or AI for short, has generated a staggering amount of hype in the past several years. Is it the game-changer it's been cracked up to be? If so, how is it changing the game? How is it likely to affect us as customers, tenants, aspiring homeowners, students, educators, patients, clients, prison inmates, members of ethnic and sexual minorities, and voters in liberal democracies? Authored by experts in fields ranging from computer science and law to philosophy and cognitive science, this book offers a concise overview of moral, political, legal and economic implications of AI. It covers the basics of AI's latest permutation, machine learning, and considers issues such as transparency, bias, liability, privacy, and regulation.Both business and government have integrated algorithmic decision support systems into their daily operations, and the book explores the implications for our lives as citizens. For example, do we take it on faith that a machine knows best in approving a patient's health insurance claim or a defendant's request for bail? What is the potential for manipulation by targeted political ads? How can the processes behind these technically sophisticated tools ever be transparent? The book discusses such issues as statistical definitions of fairness, legal and moral responsibility, the role of humans in machine learning decision systems, “nudging” algorithms and anonymized data, the effect of automation on the workplace, and AI as both regulatory tool and target.

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Book SynopsisArtificial Intelligence Applications in Higher Education offers direct examples of how artificial intelligence systems can be applied in today's higher education contexts. As the use of AI rapidly advances within colleges and universities worldwide, there is a pressing need to showcase the challenges, opportunities, and ethical considerations that are inherent in deploying these advanced computational tools. This book highlights the multifaceted roles of AI across teaching and learning, institutional administration, student data management, and beyond. Its collected case studies furnish actionable insights into enhancing academic institutions and addressing diverse learning priorities, such as motivation, engagement, feedback, and achievement goals. This valuable reference for researchers, designers, administrators, teaching faculty, and graduate students across various university programs offers fresh perspectives on generative AI, adaptive learning, intelligent tutoring sys

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Book SynopsisIntroduction to Artificial Intelligence in Education.- Instructional design for integrating AI into higher education.- AI and Competency-Based Education: Moving Beyond Traditional Approaches.- AI and Accessibility: Making Education More Inclusive.- Intelligent Tutoring Systems: Providing Personalized Support to Students.- Smart Content: Creating Interactive and Dynamic Learning Materials.- AI and Gamification: Adding Fun to Learning.- Natural Language Processing in Education: Enhancing Communication and Interaction.- Collaborative learning by using AI in Education.- Self-directed learning and problem-solving-based learning by using Ai in education.- Personalized Learning with AI: Adapting to the Needs of Each Student.- Self-directed learning and problem-solving-based learning by using Ai in education.- AI and Workforce Development: Preparing Students for the Future of Work.- Best Practices for Implementing AI in Higher Education: Lessons Learned and Future Directions.- Virtual and Augmented Reality in Education: A New Dimension of Learning.- The Future of AI in Higher Education: Trends and Predictions.- AI-Powered Adaptive Assessment: Measuring Learning More Accurately.- Chatbots in Higher Education: Supporting Student Engagement and Retention.- AI-Enhanced Teacher Support: Empowering Educators to Achieve More.- AI-Powered Student Support Services: Creating a More Responsive Campus Environment.- AI-Driven Learning Analytics.- Ethics and Privacy in AI-Enabled Education: Balancing Opportunities and Risks.- Ethical Guidelines for Artificial Intelligence-Based Learning.- Equity and Artificial Intelligence in Education.- Practical implications of different approaches to AI ethics in education.

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Book SynopsisProfessor Ivan Bratko leads the AI Lab in the Faculty of Computer and Information Science at Ljubljana University. He has taught Prolog world-wide as well as applying Prolog in medical expert systems, robot programming, qualitative modelling and computer chess research.Table of ContentsPart i The Prolog Language 1 Introduction to Prolog 2 Syntax and Meaning of Prolog Programs 3 Lists, Operators, Arithmetic 4 Programming Examples 5 Controlling Backtracking 6 Built-in Predicates 7 Constraint Logic Programming 8 Programming Style and Technique 9 Operations on Data Structures 10 Balanced Trees Part ii Prolog in Artificial Intelligence 11 Problem-Solving as Search 12 Heuristic Search and A* Algorithm 13 Best-First Search: Minimising Time and Space 14 Problem Decomposition and AND/OR Graphs 15 Knowledge Representation and Expert Systems 16 Probabilistic Reasoning with Bayesian Networks 17 Planning 18 Partial order planning and GRAPHPLAN 19 Scheduling, Simulation and Control with CLP 20 Machine Learning 21 Inductive Logic Programming 22 Qualitative Reasoning 23 Language Processing with Grammar Rules 24 Game Playing 25 Meta-Programming Appendix A: Some Differences Between Prolog Implementations Appendix B: Some Frequently Used Predicates Solutions to Selected Exercises Index

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Book SynopsisAbout our authors Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor and former chair of computer science, director of the Center for Human-Compatible AI, and holder of the SmithZadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was co-winner of the Computers and Thought Award. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science, and Honorary Fellow of Wadham College, Oxford, and an Andrew Carnegie Fellow. He held the Chaire Blaise Pascal in Paris from 2012 to 2014. He has published over 300 papers on a widTable of ContentsBrief Table of Contents Introduction Intelligent Agents Solving Problems by Searching Search in Complex Environments Adversarial Search and Games Constraint Satisfaction Problems Logical Agents First-Order Logic Inference in First-Order Logic Knowledge Representation Automated Planning Quantifying Uncertainty Probabilistic Reasoning Probabilistic Reasoning over Time Probabilistic Programming Making Simple Decisions Making Complex Decisions Multiagent Decision Making Learning from Examples Learning Probabilistic Models Deep Learning Reinforcement Learning Natural Language Processing Deep Learning for Natural Language Processing Robotics Philosophy and Ethics of AI The Future of AI

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