Artificial intelligence (AI) Books

Book SynopsisTable of ContentsPart 1. Symmetry cognition 1. Rotational and reflectional transformations 2. Goodness and simplicity of dot patterns in a regular hexagonal grid 3. Cognitive judgments and group theoretical model for dot patterns in a square grid 4. A three-stage model with group theory and a spatial filter for cognitive judgments 5. Cognitive judgments for repetitive patterns 6. Cognitive judgments for one-dimensional black-and-white filled patterns Part 2. Contour perception and brightness illusion 7. Mathematical models of an antagonistic process of excitation and inhibition 8. The brightness illusions and a five-level qualitative model based on the decrease in brightness levels 9. A five-level qualitative model for various aspects of brightness contrast 10. A three-level qualitative model for the Ehrenstein illusions Part 3. Size of the circle in a geometrical illusion 11. The Ebbinghaus illusion as a circle size contrast 12. The Delboeuf illusion by comparative judgment 13. Concentric circle illusion and judgment-order effect by absolute judgments Part 4. Negative time-order effect on weight sensation 14. Excitation and inhibition in negative time-order effect

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Book SynopsisBig Data is everywhere. It shapes our lives in more ways than we know and understand. This comprehensive introduction unravels the complex terabytes that will continue to shape our lives in ways imagined and unimagined.Drawing on case studies like Amazon, Facebook, the FIFA World Cup and the Aadhaar scheme, this book looks at how Big Data is changing the way we behave, consume and respond to situations in the digital age. It looks at how Big Data has the potential to transform disaster management and healthcare, as well as prove to be authoritarian and exploitative in the wrong hands.The latest offering from the authors of Artificial Intelligence: Evolution, Ethics and Public Policy, this accessibly written volume is essential for the researcher in science and technology studies, media and culture studies, public policy and digital humanities, as well as being a beacon for the general reader to make sense of the digital age.Table of Contents1. Big Data: What, Why and How? 2. Big Data and AI 3. What Big Data is not? 4. How Data Analytics works? 5. Big Data: The Applications 6. Why Big Data Matters? 7. The challenges with Big Data 8. Big Data: the Key Questions 9. Big Data: Is there a question mark on ethics? 10. The Future of Big Data.

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Book SynopsisArtificial intelligence (AI) has revolutionized many areas of medicine and is increasingly being embraced. This book focuses on the integral role of AI in radiology, shedding light on how this technology can enhance patient care and streamline professional workflows.This book reviews, explains, and contextualizes some of the most current, practical, and relevant developments in artificial intelligence and deep learning in radiology and medical image analysis. AI for Radiology presents a balanced viewpoint of the impact of AI in these fields, underscoring that AI technologies are not intended to replace radiologists but rather to augment their capabilities, freeing professionals to focus on more complex cases. This book guides readers from the basic principles of AI to their practical applications in radiology, moving from the role of data in AI to the ethical and regulatory considerations of using AI in radiology and concluding with a selection of resources for furtherTrade Review“The book is not just about the present state of affairs. It offers a vision, exploring the future trajectories of AI in radiology, addressing challenges, controversies, and the endless possibilities on the horizon.Having witnessed Oge’s dedication and forward-thinking approach firsthand, I am confident that this book will serve as an invaluable resource. For those stepping into the realm of AI in radiology or seeking to deepen their knowledge, this book provides a holistic, scientifically rigorous, and practical guide…I wholeheartedly believe that it will stand as a cornerstone for all enthusiasts eager to delve into the world of AI in Radiology.”--Felipe Kitamura, MD, PhDDirector of Applied Innovation and AI at DasaAffiliated Professor of Radiology at Universidade Federal de São PauloTable of Contents1 Artificial Intelligence and Medicine: The Big Picture2 AI in Radiology: From Fear to Leadership3 Fundamentals of Machine Learning and Deep Learning4 Fundamentals of Medical Image Analysis5 Data: The Essential Ingredient in AI Solutions6 Clinical Applications of AI in Radiology7 Harnessing AI in Radiology Education and Training8 Getting Started with Deep Learning in Medical Imaging9 The Future of AI in Radiology10 Resources for Further Learning

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Book SynopsisThis book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, and how all countries around the world are feeling this impact. The book aims at low-cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other. The audience is graduate and postgraduate students, researchers, academicians, institutions, and professionals who are interested in exploring Table of ContentsPart 1: Healthcare. 1. Deep Learning Approaches for Better Medical Imaging. 2. Use of Artificial Intelligence (AI) in Healthcare. 3. Computer Vision and Biomedical Imaging. 4. Disease Diagnosis and Prediction Using Machine Learning. 5. AI and ML Based Mathematical Models for Better Healthcare. Part 2: Human and Society. 6. Internet of Things (IoT) Applications for Safety and Security. 7. Use of AI and Data Science Tools for Fraud and Risk Detection. 8. Probabilistic Predictive Mathematical Tools for Data Science. 9. Natural Language Processing for Human Assistant Systems. 10. Energy Efficient Green Cities. 11. Futuristic Networks. Part 3: Industry and Business Applications. 12. Technology Driven Strategic Business Plans. 13. Data Science Applications for Decision-Making. 14. IOT Based Recommender Systems. 15. Optimization Techniques for Strategic Decision-Making. 16. Technology for Robust Cyber Security.

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Book SynopsisTwenty-five of the most important scientific minds gather for an unparalleled round-table examination about AI and what it means for our future.

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Book SynopsisWhat is artificial intelligence? Can I realistically use it in my school? Whatâs best done by human intelligence vs. artificial intelligence, and how do I bring these strengths together? What would it look like for me, and my school, to be AI Ready?AI for School Teachers will help teachers and headteachers understand enough about AI to build a strategy for how it can be used in their school. Examining the needs of schools to ensure they are ready to leverage the power of AI and drawing examples from early years to high school students, this book outlines the educational implications and benefits that AI brings to school education in practical ways. It develops an understanding of what AI is and isn't and how we define and measure what we value and provides a framework which supports a step-by-step approach to developing an AI mindset, focusing on ways to improve educational opportunities for students with evidence-informed interventions.Trade Review"This book is designed to engage members of the teaching profession directly and in an accessible way. It is starting with teachers not technology. It then places the importance of AI to teachers in their practice and follows by guiding them through how to make AI work for them. I am excited by the potential of this book to empower the teaching profession. Too many feel hard pressed by accountability systems and the pressures of the pandemic. New AI technology offers the possibility of relieving the pressure and liberating teachers to re-connect with their vocation and realise the potential of their pupils." -- Lord Jim Knight, Chair of E-ACT multi academy trust and CAST "Importantly, Rose and Karine assure us we don’t need to be experts or data scientists to be substantively engaged with AI. And with this book, they have provided teachers an all-access pass to one of the most important public conversations of our era. Jump right in, enjoy the ride and then take everyone on it with you!" --Tabitha Goldstaub, Co-Founder of Cognition X, Chair of the UK Government's AI Council, from the Foreword"The COVID-19 pandemic has further highlighted the importance of digital systems and data in education, but while there are many questions, credible answers are still few and far between. This book will help educationalists understand what AI is, what questions and dilemmas it raises, and how to respond." --Timo Hannay, Founder, SchoolDash LimitedTable of ContentsForeword. Introduction: Understanding the ingredients. 1 What is AI and why might AI be useful in education? 2 Educational challenges and AI. 3 Data, data everywhere. 4 Looking at data differently. 5 Applying AI to understand data. 6 Learning from AI. 7 Ethical questions and what is next? Index.

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Book SynopsisArtificial Intelligence for Business Creativity provides an in-depth examination of the integration of Artificial Intelligence (AI) into the business sector to foster creativity. The book explores the interplay between micro-level individual creativity and macro-level organizational innovation through the lens of AI. It delves into three crucial areas where AI can stimulate business creativity: product and service design, optimized processes, and enhanced organizational collaboration. The authors also highlight the versatility and capability of generative AI systems in promoting creativity and innovation.Intended for business leaders, managers, entrepreneurs, and those interested in AI and creativity, the book offers practical guidance and insightful recommendations on how organizations can effectively utilize AI to enhance their creative process. By offering a comprehensive understanding of the role of AI in fostering creativity, the book equips its readers with theTrade Review"Artificial Intelligence for Business Creativity" provides inspiration and practical guidance for business leaders to explore the power of AI to achieve greater creative success and improve business outcomes. This book is a critical contribution at a time of great innovation in the field" - Jerry Dischler, VP/GM, Ads, Google"In this new age of generative AI, understanding how AI can enhance creativity in business is a critical emerging topic whether for marketing, new business ventures, or designing new business models. Pagani & Champion have produced a valuable resource that shows how to manage AI-augmented work for creative business tasks".- Omar A. El Sawy, Kenneth King Stonier Chair in Business Administration, Professor of Information Systems, USC Marshall School of Business, Los Angeles, California"This leaves no doubts about whether AI can help evolve your business. It illuminates how AI can serve as both a catalyst and muse to anyone seeking new approaches to business problem solving. An excellent roadmap for professionals charged with creating meaningful business transformation at a velocity never before imaginable."- Alan Schulman, Managing Partner, UpperRight Former Chief Creative Officer, Deloitte DigitalTable of ContentsForewordIgor JablokovINTRODUCTION Margherita Pagani and Renaud ChampionPART ONE - Artificial Intelligence and Creativity1. Creativity and Innovation in the Age of AIYoram (Jerry) Wind, Margherita Pagani, and Stacey Lynn Schulman2. Could Artificial Intelligence make us humans more creative?Margherita Pagani, Renaud ChampionPART TWO - From Individual Creativity Towards Business Creativity When Artificial Intelligence Systems help to inspire creative new venture ideas Margherita Pagani, Nathan Sorin How AI can foster Business Creativity Margherita Pagani, Renaud Champion Artificial Intelligence and Creativity in Marketing: a proposed typology and new directions for academia-industry collaborations Nisreen Ameen, Gagan Deep Sharma, Shlomo Tarba Towards AI-enabled support for creative thinking about business models Mark Dowsett, Neil Maiden, Charles Baden-Fuller Conclusions and Future Directions Margherita Pagani and Renaud ChampionAPPENDIX – Think ForwardLegal Issues of AI for Business CreativityOlivier LasmolesIndex

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Book SynopsisThis book is a timely and critical introduction for those interested in what data science is (and isn't), and how it should be applied. The language is conversational and the content is accessible for readers without a quantitative or computational background; but, at the same time, it is also a practical overview of the field for the more technical readers. The overarching goal is to demystify the field and teach the reader how to develop an analytical mindset instead of following recipes. The book takes the scientist's approach of focusing on asking the right question at every step as this is the single most important factor contributing to the success of a data science project. Upon finishing this book, the reader should be asking more questions than I have answered. This book is, therefore, a practising scientist's approach to explaining data science through questions and examples.Trade Review"Data science is no longer the exclusive domain of computer scientists and engineers. The contributions of other stakeholders are required for taking a holistic approach to the problems that can be addressed by analysing a given dataset. Not only is this likely to lead to better solutions, but also a smoother journey to their implementation, validation and widespread adoption. However, in the same way that a computer scientist should at least gain an operational understanding of the tackled problem, the domain expert should also understand the foundations and correct use of the tools unveiling its solutions. In this context, How to Think about Data Science is an unusual book in that it provides an accessible introduction to this broad and booming discipline without sacrificing the understanding of key questions in data science. I can only recommend this book to those aspiring to acquire this knowledge and mindset."--Pedro J. Ballester, PhD, Senior Lecturer, Imperial College London; Wolfson Fellow, The Royal Society"What is the difference between a regular cook from a renowned chef? A regular cook may follow recipes and create edible dishes, but knowing which ingredients to use and how to combine them, how to cook each one and for how long, and how to finally present them is what makes all the difference. The tools and processes are important for sure, but what really provides value is being able to choose and integrate the right tools, ingredients and processes to create a terrific dish. In data science it is the same: anyone can execute a clustering or build a neural network with default parameters but what matters is to know, given a dataset, what questions can be answered, what algorithms we should use to answer each question and what ethical issues and privacy concerns should be considered; answering these questions would allow a data scientist not just to follow recipes, but to apply the right algorithms to answer the right questions while minimizing potentially discriminating outputs. This book focuses on these relevant questions. If you want to cook a terrific dish, this book will help you."--Jordi Conesa i Caralt, PhD, Associate Professor of Computer Science, Universitat Oberta de Catalunya"Today, big data influences nearly everything we do, and harnessing its enormous power remains a key driver of business analytics, research innovation, cultural revolution, and global politics. This book offers a great gateway to this broad and evolving subject by asking the right questions, introducing concepts clearly and succinctly, and making rational connections between computation and their wide ranging applications. The book also discusses important issues related to data bias, discrimination, data privacy, and security. The final chapter debates the limits of artificial intelligence and the computational, ethical, and philosophical conundrums it presents. Thought-provoking and refreshing – it is a must-read book!"-- Subhajyoti De, PhD, Associate Professor at the Center for Systems and Computational Biology, Rutgers, the State University of New JerseyTable of ContentsA bird’s-eye view and the art of asking questions. Descriptive Analytics. Predictive Analytics. How are predictive models trained and evaluated? Are our algorithms racist, sexist and discriminating? Personal data, privacy and cybersecurity. What are the limits of Artificial Intelligence?

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Book SynopsisThis book applies both industrial engineering and computational intelligence to demonstrate intelligent machines that solve real-world problems in various smart environments. It presents fundamental concepts and the latest advances in multi-criteria decision-making (MCDM) techniques and their application to smart environments. Though managers and engineers often use multi-criteria analysis in making complex decisions, many core problems are too difficult to model mathematically or have simply not yet been modeled.In response, as well as discussing AI-based approaches, Soft Computing for Smart Environments covers various optimization techniques, decision analytics, and data science in applying soft computing techniques to a defined set of smart environments, including smart and sustainable cities, disaster response systems, and smart campuses.This state-of-the-art book will be essential reading for both undergraduate and graduate students, researchers, practitioners, and decision-makers interested in advanced MCDM techniques for management and engineering in relation to smart environments.

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Book SynopsisTwo of the biggest design problems in Artificial Intelligenceare how to build robots that behave in line with human values and how to stop them ever going rogue. One under-explored solution to these alignment and control problems might be to examine how these are already addressed in the design of humans.Looking closely at the human blueprint, it contains a suite of capacities that are so clumsy they have generally been kept away from AI. It was assumed that robots with features like emotions and intuition, that made mistakes and looked for meaning and purpose, would not workas well as robots without this kind of code. But on considering why all these irrational properties are there, it seems that they emerge from the source code of soul. Because it is actually this junk' code that makes us human and promotes the kind of reciprocal altruism that keeps humanity alive and thriving.Robot Souls looks at developments in AI and reviews the emergence of ideas of consciTrade Review"Many people have a sense of unease about the direction in which AI is taking us. This is more than a worry about losing jobs or online content, although these are symptoms. This is a sense that something more fundamental is wrong—that the way programmers and designers understand ‘intelligence’ is itself awry.With her extraordinary ability to bridge the arts and sciences, Eve Poole not only diagnoses what is wrong, but offers an entirely novel suggestion about how to put it right. Rather than throwing up her hands in horror, Poole offers a way out of the nightmare: stop stripping out all that makes us most human—like emotions and mistakes—and put our ‘junk code’ into the programming. If it has been good enough for human survival, it is good enough for AI.Robot Souls is a brilliant book that wears its breadth of learning lightly and makes a complex subject seem simply. It is funny, readable, and important. It upends the fundamental presuppositions of AI and puts the enterprise on a new, more human, foundation."Linda Woodhead, F.D.Maurice Professor King’s College London, UK"In Robot Souls, Eve Poole advances what is a provocative—even heretical—idea: our AIs and robots not only can have souls; we need them to have souls. In developing this groundbreaking proposal, Poole not only provides a much-needed critical examination of human exceptionalism but uses this opportunity to develop an innovative conceptualization of soul as the messy but necessary “junk code” of consciousness. More than a report concerning the current and future state-of-the-art, this remarkable and thoroughly engaging book is a soul-searching meditation on the nature of the soul, the significance it has had for our own self-image as human beings, and the fact that we now are and must learn to be responsible for the souls of those artifacts that have been created in our image."David J. Gunkel, Northern Illinois University, USA"What does it mean that humans are endowed with souls? Could souls be the markers of our distinctiveness from intelligent machines, or might robots also acquire them? These questions are critical in the context of the ongoing artificial intelligence revolution, and Eve Poole's 'Robot Souls' engages them directly and skillfully at the interface between science and religion. Her 'junk code' proposal represents a bold and exciting hypothesis, making us rethink what we deem most important about being human."Marius Dorobantu, the Vrije Universiteit Amsterdam, the NetherlandsTable of Contents1. What is AI? 1.1. Is AI Conscious? 1.2. Robots 1.3. Inventing AI 1.4. Deep Learning 1.5. Reinforcement Learning 1.6. Bayesian AI 1.7. The Turing Test 2. How Should We Relate to AI? 2.1. How Should We Treat AI? 2.2. Regulation 2.3. Legal Status 2.4. Audit 2.5. Asimov 2.0 3. Will AI Replace Us? 3.1. Our Obsolescence Problem 3.2. The 12 Dooms 3.3. Distinctiveness 3.4 Materialism 3.5 Free Will and the Rule of Law 4. What Is Consciousness? 4.1. Mind 4.2. Consciousness 4.3. Qualia 5. How Do We Know? 5.1. How We Know Things 5.2. Thinking Styles 5.3 Types of Intelligence 6. The Soul 6.1. History of the Soul 6.2. Mapping Soul to Consciousness 7. Junk Code 7.1 Junk Code? 7.2. Emotions 7.3. Mistakes 7.4. Storytelling 7.5. Sixth Sense 7.6. Uncertainty 7.7. Free Will 7.8. Meaning 7.9. Community 8. Cultivating Soul 8.1. Cultivating Junk Code 8.2. Emotions 8.3. Mistakes 8.4. Storytelling 8.5. Sixth Sense 8.6. Uncertainty 8.7. Free Will 8.8. Meaning 9. Programming in Humanity 9.1. Why Bother? 9.2. Parenting 9.3. Gender 9.4 Coding Soul? 9.5. Emotions 9.6. Mistakes 9.7. Storytelling 9.8. Sixth Sense 9.9. Uncertainty 9.10. Free Will 9.11. Meaning 9.12. Robot Manifesto 10. Eucatastrophe 10.1. Changing Our Minds 10.2. Happily Ever After? Appendix. Glossary. References. Index.

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Book SynopsisArtificial Intelligence was meant to be the great social equalizer that helps promote fairness by removing human bias from the equation, but is this true? Given that the policing and judicial systems can display human bias, this book explores how the technology they use can also reflect these prejudices.From healthcare services to social scoring in exams, to applying for and getting loans, AI outcomes often restrict those most in need of these services. Through personal stories from an esteemed Black Data Scientist and AI expert, this book attempts to demystify the algorithmic black box.AI pervades all aspects of modern society and affects everyone within it, yet its internal biases are rarely confronted. This book advises readers on what they can do to fight against it, including the introduction of a proposed AI Bill of Rights, whilst also providing specific recommendations for AI developers and technologists. https://hiddeninwhitesight.com/ Trade Review"An excellent book…is every day life, practical, visionary, and opens unique thoughts and ways to solving pervasive daily problems."--Antonio Smith Sr., Technologist, Serial Entrepreneur, Leader, Author, Inventor, Mentor, Activist"This book a must read. It can be used to educate those in the impacted communities, the developers and companies on the issues, and any interested party. It emphasizes the urgent need to address them now. If not, this country – and our global society – may sustain some of our systemic racial structures. It is a call to action to address the issues and enable AI/ML to fulfill its true promise; becoming a major impetus to improving our global quality of life."—Sandra K. Johnson, Ph.D., CEO, SKJ Visioneering; Former CEO IBM Central, East and West Africa"As technology becomes a driver in delivering government services, it is essential that the technologists, policy makers and leaders understand the value and risks of this evolving world. Government technology leaders must be at the forefront of establishing the guideposts for the fair and equitable use of technology that impacts the citizens and businesses that they support. Hidden in White Sight provides valuable insights on the impact of technology decisions that are being made today but more importantly, what technology leaders must do in the future."--Teri Takai, Senior Vice President, Center for Digital Government; Former State and Federal CIO."As a white, middle-aged senior executive who has mentored Calvin on how to navigate the corporate world and was mentored by Calvin on how to better engage with the Black community, when Calvin first told me he wanted to write a book about AI from his perspective, that of a black man who grew up in urban America and Urban Atlanta, I thought it was a fabulous idea for so many reasons. We discussed making the book approachable even to people without a technical degree. Possibly even to uneducated populations. It is important that the less educated and non-technical population, especially from the black and brown community, understand how AI impacts their lives every day - in some ways innocuous, and in others that impact their health, their wealth, and their livelihood….After reading the book, I was pleasantly stunned by how he was able to convey highly technical challenges and opportunities of AI from real-world examples from his friends and community members. This is a must-read for any person of color, but perhaps more important for the white community to read to begin to empathize with the challenges of the black community…Remember AI is just math and math is not intrinsically biased or hurtful, the math learns from the data that enshrines all the bad, hateful, and harmful decisions of the white community in the past. This is a must-read for all."—Seth Dobrin, Ph.D., President, Responsible AI Institute, CEO, Trustwise AI"An excellent book…is every day life, practical, visionary, and opens unique thoughts and ways to solving pervasive daily problems."--Antonio Smith Sr., Technologist, Serial Entrepreneur, Leader, Author, Inventor, Mentor, Activist"An excellent book…is every day life, practical, visionary, and opens unique thoughts and ways to solving pervasive daily problems."--Antonio Smith Sr., Technologist, Serial Entrepreneur, Leader, Author, Inventor, Mentor, Activist"As a white, middle-aged senior executive who has mentored Calvin on how to navigate the corporate world and was mentored by Calvin on how to better engage with the Black community, when Calvin first told me he wanted to write a book about AI from his perspective, that of a black man who grew up in urban America and Urban Atlanta, I thought it was a fabulous idea for so many reasons. We discussed making the book approachable even to people without a technical degree. Possibly even to uneducated populations. It is important that the less educated and non-technical population, especially from the black and brown community, understand how AI impacts their lives every day - in some ways innocuous, and in others that impact their health, their wealth, and their livelihood. I was hopeful that he would be able to achieve these goals, but skeptical even Calvin could achieve this. After reading the book, I was pleasantly stunned by how he was able to convey highly technical challenges and opportunities of AI from real-world examples from his friends and community members. This is a must-read for any person of color, but perhaps more important for the white community to read to begin to empathize with the challenges of the black community, especially those that have risen from being very economically challenged to those who have spent their life better off, but are still impacted by the systemic and structural racism that is embedded in the data used to train past and the current version of AI. Remember AI is just math and math is not intrinsically biased or hurtful, the math learns from the data that enshrines all the bad, hateful, and harmful decisions of the white community in the past. This is a must-read for all."-- Dr Seth Dobrin, PhD, President, Responsible AI Institute, CEO, Trustwise AI"As technology becomes a driver in delivering government services, it is essential that the technologists, policy makers and leaders understand the value and risks of this evolving world. Government technology leaders must be at the forefront of establishing the guideposts for the fair and equitable use of technology that impacts the citizens and businesses that they support. Hidden in White Sight provides valuable insights on the impact of technology decisions that are being made today but more importantly, what technology leaders must do in the future."--Teri Takai, Senior Vice President, Center for Digital Government; Former State and Federal CIO.Table of ContentsChapter 1: Listening Ears, Chapter 2: The Racist Algorithm, Chapter 3: The American Dream, Chapter 4: AI Gone Wild, Chapter 5: An Enduring Legacy, Chapter 6: Our Authentic Selves, Chapter 7: Mass Unemployment, Chapter 8: Medically Induced Trauma, Chapter 9: Colored Ads, Chapter 10: Weapons of Mass Destruction, Chapter 11: Designers’ Dilemma, Chapter 12: Corporate Choice, Chapter 13: Algorithmic Justice

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Book SynopsisThe availability of machine learning algorithms, and the immense computational power required to develop robust models with high accuracy, has driven researchers to conduct extensive studies in forensic science, particularly in the identification and examination of evidence found at crime scenes. Machine Learning in Forensic Evidence Examination discusses methodologies for the application of machine learning to the field of forensic science.Evidence analysis is the cornerstone of forensic investigations, examined for either classification or individualization based on distinct characteristics. Artificial intelligence offers a powerful advantage by efficiently processing large datasets with multiple features, enhancing accuracy and speed in forensic analysis to potentially mitigate human errors. Algorithms have the potential to identify patterns and features in evidences such as firearms, explosives, trace evidences, narcotics, body fluids etc. and cataloged them in various databases. Additionally, they can be useful in reconstruction and detection of complex events, such as accidents and crimes, both during and after event. This book provides readers with consolidated research data on the potential applications and use of machine learning for analyzing various types of evidence. Chapters focus on different methodologies of machine learning applied in different domains of forensic sciences such as biology, serology, physical sciences, fingerprints, trace evidences, ballistics, anthropology, odontology, digital forensics, chemistry, toxicology as well as the potential use of big data analytics in forensic. Exploring recent advancements in machine learning, coverage also addresses the challenges faced by experts during routine examinations and how machine learning can help overcome these challenges, and explore recent advancements in machine learning.Machine Learning in Forensic Evidence Examination is a valuable resource for academics, forensic scientists, legal professionals, and those working on investigations and analysis within the law enforcement agencies.

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Book SynopsisCommon sense is the endless frontier in the development of artificial intelligence, but what exactly is common sense, can we replicate it in algorithmic form, and if we can should we?Bauer, Schiele and their contributors from a range of disciplines analyse the nature of common sense, and the consequent challenges of incorporating into artificial intelligence models. They look at different ways we might understand common sense and which of these ways are simulated within computer algorithms. These include sensory integration, self-evident truths, rhetorical common places, and mutuality and intentionality of actors within a moral community. How far are these possible features within and of machines? Approaching from a range of perspectives including Sociology, Political Science, Media and Culture, Psychology and Computer Science, the contributors lay out key questions, practical challenges and common sense concerns underlying the incorporation of common sense within machine le

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Book SynopsisThis timely book affirms that humans can flourish in the Age of AI by relying on their distinctive strengths, and explores the skills and knowledge that are required to interact effectively, efficiently, and responsibly with AIs, both today and in the future.In Part I, this book develops the Cognitive Amplifier Loop, which allows humans to use AI to build on their cognitive and emotional strengths and manage their limitations. Kosslyn discusses ways to employ this loop to offload tasks to AI and to utilize it to train us effectively and efficiently, as well as how to use it to both learn and engage in critical thinking, creative problem solving, and manage cognitive and emotional constraints. Part II establishes how to draw on the Cognitive Amplifier Loop to help us improve our human relationships, addressing emotional intelligence, effective communication, leadership, followership, and collaboration skills. Finally, Part III builds on previous chapters to consider how to int

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Book SynopsisWould you like to know how you can benefit from generative artificial intelligence (AI)? Then this book will be of great help to you. It shows you how AI can make your life easier, and it will teach you what added value the current application scenarios of ChatGPT, Midjourney and various other AI tools offer and where their limits lie. Whether you want to write text, conduct research, generate images or create your own program code, you can get started right away without any previous knowledge.Bolstered with many practical examples from the most diverse areas of application, this book presents ChatGPT as part of an ever-growing toolkit, and guides you on which tools to utilize and apply. This is a valuable workbook for those looking to harness and incorporate ChatGPT and generative AI into their work, studies or general life.Key Features: Demonstrates the profitable use of ChatGPT and other AI tools to make work easier at work and in everyday life Provides practical examples to help with perfect prompts Shows how to create impressive images with just a few words Provides programmers with powerful tools to make the creation of professional software a childâs play Dives deeper into the topic of text-generative AI for advanced users and provides valuable tips and tricks

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Book SynopsisThe integration of fifth generation (5G) wireless technologies with distributed artificial intelligence (AI) is transforming network operations. AI is increasingly embedded in all network elements, from cloud and edge to terminal devices, enabling AI to function as a networking system. This convergence facilitates AI-based applications across the global network, with notable successes in various domains such as computer vision, natural language processing, and healthcare. Networked Artificial Intelligence: AI-Enabled 5G Networking a comprehensive framework for the deep integration of computing and communications, optimizing networks and applications as a unified system using AI.The book covers topics ranging from networked AI fundamentals to AI-enabled 5G networks, including agent modeling, machine learning (ML) algorithms, and network protocol architectures. It discusses how network service providers can leverage AI and ML techniques to customize network baseli

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

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

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

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

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

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Book SynopsisThis book shows you how to combine the practicality of early AI with deep learning capabilities and industrial control technologies to make robust decisions in the real world.

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Book SynopsisProvides an up-to-date analysis of big data and multi-agent systems The term Big Data refers to the cases, where data sets are too large or too complex for traditional data-processing software. With the spread of new concepts such as Edge Computing or the Internet of Things, production, processing and consumption of this data becomes more and more distributed. As a result, applications increasingly require multiple agents that can work together. A multi-agent system (MAS) is a self-organized computer system that comprises multiple intelligent agents interacting to solve problems that are beyond the capacities of individual agents. Modern Big Data Architectures examines modern concepts and architecture for Big Data processing and analytics. This unique, up-to-date volume provides joint analysis of big data and multi-agent systems, with emphasis on distributed, intelligent processing of very large data sets. Each chapter contains practical examples and detaTable of ContentsList of Figures ix List of Tables xi Preface xiii Acknowledgments xv Acronyms xvii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Assumptions 3 1.3 For Whom is This Book? 4 1.4 Book Structure 4 Chapter 2 Evolution of IT Architectures and Paradigms 7 2.1 Evolution of IT Architectures 7 2.1.1 Monolith 7 2.1.2 Service Oriented Architecture 9 2.1.3 Microservices 12 2.2 Actors and Agents 15 2.2.1 Actors 15 2.2.2 Agents 17 2.3 From ACID to BASE, CAP, and NoSQL – The Database (R)evolution 22 2.4 The Cloud 24 2.5 From Distributed Sensor Networks to the Internet of Things and Cyber-Physical Systems 27 2.6 The Rise of Big Data 28 Chapter 3 Sources of Data 31 3.1 The Internet 32 3.1.1 The Semantic Web 32 3.1.2 Linked Data 35 3.1.3 Knowledge Graphs 36 3.1.4 Social Media 38 3.1.5 Web Mining 38 3.2 Scientific Data 40 3.2.1 Biomedical Data 40 3.2.2 Physics and Astrophysics Data 41 3.2.3 Environmental Sciences 44 3.3 Industrial Data 45 3.3.1 Smart Factories 45 3.3.2 SmartGrid 47 3.3.3 Aviation 47 3.4 Internet of Things 48 Chapter 4 Big Data Tasks 51 4.1 Recommender Systems 51 4.2 Search 52 4.3 Ad-tech and RTB Algorithms 55 4.4 Cross-Device Graph Generation 57 4.5 Forecasting and Prediction Systems 58 4.6 Social Media Big Data 59 4.7 Anomaly and Fraud Detection 61 4.8 New Drug Discovery 63 4.9 Smart Grid Control and Monitoring 64 4.10 IoT and Big Data Applications 65 Chapter 5 Cloud Computing 67 5.1 Cloud Enabled Architectures 67 5.1.1 Cloud Management Platforms 67 5.1.2 Efficient Cloud Computing 73 5.1.3 Distributed Storage Systems 75 5.2 Agents and the Cloud 82 5.2.1 Multi-agent Versus Cloud Paradigms 83 5.2.2 Agents in the Cloud 83 Chapter 6 Big Data Architectures 87 6.1 Big Data Computation Models 87 6.1.1 MapReduce 87 6.1.2 Directed Acyclic Graph Models 89 6.1.3 All-Pairs 92 6.1.4 Very Large Bitmap Operations 93 6.1.5 Message Passing Interface 94 6.1.6 Graphical Processing Unit Computing 95 6.2 Publish-Subscribe Systems 97 6.3 Stream Processing 99 6.3.1 Information Flow Processing Concepts 99 6.3.2 Stream Processing Systems 101 6.4 Higer Level Big Data Architectures 110 6.4.1 Spark 110 6.4.2 Lambda 112 6.4.3 Multi-Agent View of the Lambda Architecture 113 6.4.4 Questioning the Lambda 115 6.5 Industry and Other Approaches 116 6.6 Actor and Agent-Based Big Data Architectures 118 Chapter 7 Big Data Analytics, Mining, and Machine Learning 121 7.1 To SQL or Not to SQL 122 7.1.1 SQL Hadoop Interfaces 123 7.1.2 From Shark to SparkSQL 125 7.2 Big Data Mining and Machine Learning 128 7.2.1 Graph Mining 133 7.2.2 Agent Based Machine Learning and Data Mining 134 Chapter 8 Physically Distributed Systems – Mobile Cloud, Internet of Things, Edge Computing 137 8.1 Mobile Cloud 138 8.2 Edge and Fog Computing 145 8.2.1 Business Case: Mobile Context Aware Recommender System 147 8.3 Internet of Things 148 8.3.1 IoT Fundamentals 148 8.3.2 IoT and the Cloud 151 8.3.3 MAS in IoT 156 Chapter 9 Summary 159 Bibliography 161 Index 179

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Book SynopsisTable of ContentsAbout the editors, ix List of Contributors, x Series Preface, xi Preface Book, xii Acknowledgements, xiii 1 Introduction, 1Zeno Geradts and Katrin Franke 2 AI-based Forensic Evaluation in Court: The Desirability of Explanation and the Necessity of Validation, 3Rolf J.F. Ypma, Daniel Ramos, and Didier Meuwly 2.1 Introduction, 3 2.1.1 AI for Forensic Evaluation, 6 2.2 The Desirability for Explanation and the Necessity of Validation, 7 2.3 Explainability (and its Validity), 8 2.3.1 Reasons to Pursue Explanations, 9 2.3.2 Types of Explanations, 9 2.3.3 Limitations of Explanations, 11 2.4 Validation (and its Explanation), 11 2.4.1 Measure the Method's Performance, 12 2.4.2 Approach in Four Steps, 12 2.4.3 Accountability, 16 2.5 Conclusion, 17 3 Machine Learning for Evidence in Criminal Proceedings: Techno-legal Challenges for Reliability Assurance, 21Radina Stoykova, Jeanne Mifsud Bonnici, and Katrin Franke 3.1 Introduction: AI in the Intersection of Criminal Procedure and Forensics, 21 3.1.1 Technical Fragmentation in Digital Investigations, 21 3.1.2 Legal and Methodological Fragmentation in Digital Investigations, 22 3.1.3 Specifics of ML-based Investigative Approach, 23 3.1.4 Scope and Definitions, 25 3.2 Legal Framework, 27 3.2.1 The Fair Trial Principle, 28 3.2.2 Necessity and Proportionality of Investigative Measures, 32 3.2.3 The AIA Proposal, 33 3.2.4 AI System Development and Legislative Contradictions, 35 3.3 Machine Learning Pipelines: Techno-legal Challenges, 44 3.3.1 Task + Purpose Limitation and Data Minimization, 44 3.3.2 Dataset Engineering and Data Governance, 48 3.3.3 Pre-processing for Input: Trade-offs between Accuracy and Computational Costs, 53 3.3.4 Modelling, 56 3.4 AI Use in Investigations: AI System Design + Data Protection = Fair Trial?, 63 3.5 Conclusion, 66 4 Formalising Representation and Interpretation of Digital Evidence to Reinforce Reasoning and Automated Analysis, 74Eoghan Casey and Timothy Bollé 4.1 Introduction, 74 4.2 Background and Related Work, 76 4.3 Method, 77 4.4 Representing Digital Traces, 79 4.5 Representing Computed Similarity, 86 4.6 Representing ML Classification, 89 4.7 Representing Hypothesis Test Results (a.k.a. Inferences), 91 4.7.1 Location Example, 93 4.7.2 Identification Example, 95 4.8 Effective/Reliable/Responsible Automated Analysis, 99 4.9 Conclusion, 101 5 Servicing Digital Investigations with Artificial Intelligence, 103Harm van Beek and Hans Henseler 5.1 Introduction, 103 5.2 Introduction To Hansken, 104 5.2.1 Normalized Trace Model, 105 5.2.2 Forensic Tool Application, 106 5.2.3 Hansken's Application Programming Interfaces, 108 5.3 Large Scale Application of AI Techniques, 109 5.3.1 Rule-based AI Techniques Implemented in Hansken, 109 5.3.2 Deep-learning AI Techniques Currently Implemented in Hansken, 111 5.3.3 Deep-learning AI Techniques to be Implemented in Hansken, 115 5.3.4 The application of large language models in digital forensics, 118 5.4 Conclusions and Further Reading, 120 6 On the Feasibility of Social Network Analysis Methods for Investigating Large-scale Criminal Networks, 123Jan William Johnsen and Katrin Franke 6.1 Introduction, 123 6.2 Previous Work, 125 6.3 Material and Methods, 127 6.3.1 Real-world Underground Forum Database Dumps, 127 6.3.2 Network Centrality Measures, 129 6.3.3 Measuring Association Using Bi-variate Analysis, 129 6.3.4 Topic Modelling Algorithms, 130 6.4 Experimental Setup, 130 6.4.1 Evaluating Network Centrality Measures for Forensics, 130 6.4.2 Our Novel Approach for Analysing Cybercriminal's Technical Skills, 133 6.5 Experimental Results and Discussion, 137 6.5.1 Correlation Testing, 137 6.5.2 Our Newly Proposed Method, 142 6.6 Conclusion, 145 7 Mapping NLP Techniques to Investigations and Investigative Interviews, 149Kyle Porter and Bente Skattør 7.1 Introduction, 149 7.2 Criminal Investigation, 150 7.2.1 Investigative Interviews, 150 7.3 Assessing the Needs of Investigators in an NLP Context, 151 7.3.1 Mapping Interviewer Needs to Existing NLP Tasks, 151 7.4 Automatic Speech Recognition, 152 7.4.1 ASR Basics, 152 7.4.2 ASR, Digital Investigation, and the State of the Art, 153 7.5 NLP Basics, 154 7.5.1 Common Terminology, 154 7.5.2 Vector Space Models and Embeddings, 156 7.5.3 Modern NLP Models, 157 7.6 Text Extraction, 157 7.6.1 Entity Identification and Named Entity Recognition, 157 7.6.2 Named Entity Recognition Metrics, 158 7.6.3 NER Applied to Investigations, 159 7.6.4 Entity Linking, 159 7.6.5 Limitations of Using NER, 160 7.6.6 Extraction Methods outside NER, 161 7.7 Text Classification, 161 7.7.1 Classification Evaluation Metrics, 162 7.7.2 Text Classification and Digital Investigation, 162 7.7.3 Classification Limitations, 163 7.8 Text Reduction, 164 7.8.1 Thematic Extraction and Topic Modelling, 164 7.8.2 Topic Modelling and Digital Investigations, 165 7.8.3 Limitations of Topic Modelling, 166 7.8.4 Text Summarization, 166 7.8.5 Text Summarization and Digital Investigations, 167 7.8.6 Summarization Limitations, 167 7.9 Discussion and Conclusion, 167 7.9.1 Future Work, 169 8 The Influence of Compression on the Detection of Deepfake Videos, 174Meike Kombrink and Zeno Geradts 8.1 Introduction, 174 8.2 Method, 178 8.2.1 Dataset, 178 8.2.2 Deepfake Detection, 180 8.3 Results, 183 8.3.1 Compressed Dataset, 183 8.3.2 Algorithms, 184 8.4 Discussion, 190 8.4.1 Deepfake Detection, 190 8.4.2 Compression, 191 8.4.3 Future Work, 193 8.5 Conclusion, 193 9 Event Log Analysis and Correlation: A Digital Forensic Perspective, 195Neminath Hubballi and Pratibha Khandait 9.1 Introduction, 195 9.2 Sources of Logs, 197 9.2.1 End Host System Logs, 198 9.2.2 Networking Devices and Security Applications, 203 9.2.3 Application Logs, 207 9.3 Need for Correlation, 208 9.4 Correlation Techniques, 210 9.5 Conclusions, 214 10 (Hyper-)graph Analysis and its Application in Forensics, 216Marcel Worring 10.1 Introduction, 216 10.2 Survey of Methods, 218 10.2.1 Preliminaries, 218 10.2.2 Tasks, 219 10.2.3 Graph Neural Networks, 220 10.3 Explainability and Visualization, 224 10.4 Conclusion, 227 11 Conclusion, 230Zeno Geradts and Katrin Franke Index, 232

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Book SynopsisTable of ContentsIntroduction xxi Assessment Testxxxii Chapter 1 Framing ML Problems 1 Translating Business Use Cases 3 Machine Learning Approaches 5 Supervised, Unsupervised, and Semi- supervised Learning 5 Classification, Regression, Forecasting, and Clustering 7 ML Success Metrics 8 Regression 12 Responsible AI Practices 13 Summary 14 Exam Essentials 14 Review Questions 15 Chapter 2 Exploring Data and Building Data Pipelines 19 Visualization 20 Box Plot 20 Line Plot 21 Bar Plot 21 Scatterplot 22 Statistics Fundamentals 22 Mean 22 Median 22 Mode 23 Outlier Detection 23 Standard Deviation 23 Correlation 24 Data Quality and Reliability 24 Data Skew 25 Data Cleaning 25 Scaling 25 Log Scaling 26 Z-score 26 Clipping 26 Handling Outliers 26 Establishing Data Constraints 27 Exploration and Validation at Big- Data Scale 27 Running TFDV on Google Cloud Platform 28 Organizing and Optimizing Training Datasets 29 Imbalanced Data 29 Data Splitting 31 Data Splitting Strategy for Online Systems 31 Handling Missing Data 32 Data Leakage 33 Summary 34 Exam Essentials 34 Review Questions 36 Chapter 3 Feature Engineering 39 Consistent Data Preprocessing 40 Encoding Structured Data Types 41 Mapping Numeric Values 42 Mapping Categorical Values 42 Feature Selection 44 Class Imbalance 44 Classification Threshold with Precision and Recall 45 Area under the Curve (AUC) 46 Feature Crosses 46 TensorFlow Transform 49 TensorFlow Data API (tf.data) 49 TensorFlow Transform 49 GCP Data and ETL Tools 51 Summary 51 Exam Essentials 52 Review Questions 53 Chapter 4 Choosing the Right ML Infrastructure 57 Pretrained vs. AutoML vs. Custom Models 58 Pretrained Models 60 Vision AI 61 Video AI 62 Natural Language AI 62 Translation AI 63 Speech- to- Text 63 Text- to- Speech 64 AutoML 64 AutoML for Tables or Structured Data 64 AutoML for Images and Video 66 AutoML for Text 67 Recommendations AI/Retail AI 68 Document AI 69 Dialogflow and Contact Center AI 69 Custom Training 70 How a CPU Works 71 GPU 71 TPU 72 Provisioning for Predictions 74 Scaling Behavior 75 Finding the Ideal Machine Type 75 Edge TPU 76 Deploy to Android or iOS Device 76 Summary 77 Exam Essentials 77 Review Questions 78 Chapter 5 Architecting ML Solutions 83 Designing Reliable, Scalable, and Highly Available ml Solutions 84 Choosing an Appropriate ML Service 86 Data Collection and Data Management 87 Google Cloud Storage (GCS) 88 BigQuery 88 Vertex AI Managed Datasets 89 Vertex AI Feature Store 89 NoSQL Data Store 90 Automation and Orchestration 91 Use Vertex AI Pipelines to Orchestrate the ML Workflow 92 Use Kubeflow Pipelines for Flexible Pipeline Construction 92 Use TensorFlow Extended SDK to Leverage Pre-built Components for Common Steps 93 When to Use Which Pipeline 93 Serving 94 Offline or Batch Prediction 94 Online Prediction 95 Summary 97 Exam Essentials 97 Review Questions 98 Chapter 6 Building Secure ML Pipelines 103 Building Secure ML Systems 104 Encryption at Rest 104 Encryption in Transit 105 Encryption in Use 105 Identity and Access Management 105 IAM Permissions for Vertex AI Workbench 106 Securing a Network with Vertex AI 109 Privacy Implications of Data Usage and Collection 113 Google Cloud Data Loss Prevention 114 Google Cloud Healthcare API for PHI Identification 115 Best Practices for Removing Sensitive Data 116 Summary 117 Exam Essentials 118 Review Questions 119 Chapter 7 Model Building 121 Choice of Framework and Model Parallelism 122 Data Parallelism 122 Model Parallelism 123 Modeling Techniques 125 Artificial Neural Network 126 Deep Neural Network (DNN) 126 Convolutional Neural Network 126 Recurrent Neural Network 127 What Loss Function to Use 127 Gradient Descent 128 Learning Rate 129 Batch 129 Batch Size 129 Epoch 129 Hyperparameters 129 Transfer Learning 130 Semi-supervised Learning 131 When You Need Semi-supervised Learning 131 Limitations of SSL 131 Data Augmentation 132 Offline Augmentation 132 Online Augmentation 132 Model Generalization and Strategies to Handle Overfitting and Underfitting 133 Bias Variance Trade- Off 133 Underfitting 133 Overfitting 134 Regularization 134 Summary 136 Exam Essentials 137 Review Questions 138 Chapter 8 Model Training and Hyperparameter Tuning 143 Ingestion of Various File Types into Training 145 Collect 146 Process 147 Store and Analyze 150 Developing Models in Vertex AI Workbench by Using Common Frameworks 151 Creating a Managed Notebook 153 Exploring Managed JupyterLab Features 154 Data Integration 155 BigQuery Integration 155 Ability to Scale the Compute Up or Down 156 Git Integration for Team Collaboration 156 Schedule or Execute a Notebook Code 158 Creating a User-Managed Notebook 159 Training a Model as a Job in Different Environments 161 Training Workflow with Vertex AI 162 Training Dataset Options in Vertex AI 163 Pre-built Containers 163 Custom Containers 166 Distributed Training 168 Hyperparameter Tuning 169 Why Hyperparameters Are Important 170 Techniques to Speed Up Hyperparameter Optimization 171 How Vertex AI Hyperparameter Tuning Works 171 Vertex AI Vizier 174 Tracking Metrics During Training 175 Interactive Shell 175 TensorFlow Profiler 177 What-If Tool 177 Retraining/Redeployment Evaluation 178 Data Drift 178 Concept Drift 178 When Should a Model Be Retrained? 178 Unit Testing for Model Training and Serving 179 Testing for Updates in API Calls 180 Testing for Algorithmic Correctness 180 Summary 180 Exam Essentials 181 Review Questions 182 Chapter 9 Model Explainability on Vertex AI 187 Model Explainability on Vertex AI 188 Explainable AI 188 Interpretability and Explainability 189 Feature Importance 189 Vertex Explainable AI 189 Data Bias and Fairness 193 ML Solution Readiness 194 How to Set Up Explanations in the Vertex AI 195 Summary 196 Exam Essentials 196 Review Questions 197 Chapter 10 Scaling Models in Production 199 Scaling Prediction Service 200 TensorFlow Serving 201 Serving (Online, Batch, and Caching) 203 Real- Time Static and Dynamic Reference Features 203 Pre-computing and Caching Prediction 206 Google Cloud Serving Options 207 Online Predictions 207 Batch Predictions 212 Hosting Third- Party Pipelines (MLFlow) on Google Cloud 213 Testing for Target Performance 214 Configuring Triggers and Pipeline Schedules 215 Summary 216 Exam Essentials 217 Review Questions 218 Chapter 11 Designing ML Training Pipelines 221 Orchestration Frameworks 223 Kubeflow Pipelines 224 Vertex AI Pipelines 225 Apache Airflow 228 Cloud Composer 229 Comparison of Tools 229 Identification of Components, Parameters, Triggers, and Compute Needs 230 Schedule the Workflows with Kubeflow Pipelines 230 Schedule Vertex AI Pipelines 232 System Design with Kubeflow/TFX 232 System Design with Kubeflow DSL 232 System Design with TFX 234 Hybrid or Multicloud Strategies 235 Summary 236 Exam Essentials 237 Review Questions 238 Chapter 12 Model Monitoring, Tracking, and Auditing Metadata 241 Model Monitoring 242 Concept Drift 242 Data Drift 243 Model Monitoring on Vertex AI 243 Drift and Skew Calculation 244 Input Schemas 245 Logging Strategy 247 Types of Prediction Logs 247 Log Settings 248 Model Monitoring and Logging 248 Model and Dataset Lineage 249 Vertex ML Metadata 249 Vertex AI Experiments 252 Vertex AI Debugging 253 Summary 253 Exam Essentials 254 Review Questions 255 Chapter 13 Maintaining ML Solutions 259 MLOps Maturity 260 MLOps Level 0: Manual/Tactical Phase 261 MLOps Level 1: Strategic Automation Phase 263 MLOps Level 2: CI/CD Automation, Transformational Phase 264 Retraining and Versioning Models 266 Triggers for Retraining 267 Versioning Models 267 Feature Store 268 Solution 268 Data Model 269 Ingestion and Serving 269 Vertex AI Permissions Model 270 Custom Service Account 270 Access Transparency in Vertex AI 271 Common Training and Serving Errors 271 Training Time Errors 271 Serving Time Errors 271 TensorFlow Data Validation 272 Vertex AI Debugging Shell 272 Summary 272 Exam Essentials 273 Review Questions 274 Chapter 14 BigQuery ML 279 BigQuery – Data Access 280 BigQuery ML Algorithms 282 Model Training 282 Model Evaluation 284 Prediction 285 Explainability in BigQuery ML 286 BigQuery ML vs. Vertex AI Tables 289 Interoperability with Vertex AI 289 Access BigQuery Public Dataset 289 Import BigQuery Data into Vertex AI 290 Access BigQuery Data from Vertex AI Workbench Notebooks 290 Analyze Test Prediction Data in BigQuery 290 Export Vertex AI Batch Prediction Results 290 Export BigQuery Models into Vertex AI 291 BigQuery Design Patterns 291 Hashed Feature 291 Transforms 291 Summary 292 Exam Essentials 293 Review Questions 294 Appendix Answers to Review Questions 299 Chapter 1: Framing ML Problems 300 Chapter 2: Exploring Data and Building Data Pipelines 301 Chapter 3: Feature Engineering 302 Chapter 4: Choosing the Right ML Infrastructure 302 Chapter 5: Architecting ML Solutions 304 Chapter 6: Building Secure ML Pipelines 305 Chapter 7: Model Building 306 Chapter 8: Model Training and Hyperparameter Tuning 307 Chapter 9: Model Explainability on Vertex AI 308 Chapter 10: Scaling Models in Production 308 Chapter 11: Designing ML Training Pipelines 309 Chapter 12: Model Monitoring, Tracking, and Auditing Metadata 310 Chapter 13: Maintaining ML Solutions 311 Chapter 14: BigQuery ML 313 Index 315

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Book SynopsisDiscover how to achieve business goals by relying on high-quality, robust data In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you'll learn techniques to define and assess data quality, discover how to ensure that your firm's data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications. The author shows you how to: Profile for data quality, including the appropriate techniques, criteria, and KPIs Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization. Formulate the reference architecture for data quality, inTable of ContentsForeword by Bill Inmon Preface About the Book Quality Principles Applied in This Book Organization of the Book Who Should Read This Book? References Acknowledgments Define Phase Chapter 1: Introduction Introduction Data, Analytics, AI, and Business Performance Data as a Business Asset or Liability Data Governance, Data Management, and Data Quality Leadership Commitment to Data Quality Key Takeaways Conclusion References Chapter 2: Business Data Introduction Data in Business Telemetry Data Purpose of Data in Business Business Data Views Key Characteristics of Business Data Critical Data Elements (CDE) Key Takeaways Conclusion References Chapter 3: Data Quality in Business Introduction Data Quality Dimensions Context in Data Quality Consequences and Costs of Poor Data Quality Data Depreciation and Its Factors Data in IT Systems Data Quality and Trusted Information Key Takeaways Conclusion References Analyze Phase Chapter 4: Causes for Poor Data Quality Introduction Data Quality RCA Techniques Typical Causes of Poor Data Quality Key Takeaways Conclusion References Chapter 5: Data Lifecycle and Lineage Introduction Business-Enabled DLC Stages IT Business-Enabled DLC Stages Data Lineage Key Takeaways Conclusion References Chapter 6: Profiling for Data Quality Introduction Criteria for Data Profiling Data Profiling Techniques for Measures of Centrality Data Profiling Techniques for Measures of Variation Integrating Centrality and Variation KPIs Key Takeaways Conclusion References Realize Phase Chapter 7: Reference Architecture for Data Quality Introduction Options to Remediate Data Quality DataOps Data Product Data Fabric and Data Mesh Data Enrichment Key Takeaways Conclusion References Chapter 8: Best Practices to Realize Data Quality Introduction Overview of Best Practices BP 1: Identify the Business KPIs and the Ownership of These KPIs and the Pertinent Data BP 2: Build and Improve the Data Culture and Literacy in the Organization BP 3: Define the Current and Desired state of Data Quality BP 4: Follow the Minimalistic Approach to Data Capture BP 5: Select and Define the Data Attributes for Data Quality BP 6: Capture and Manage Critical Data with Data Standards in MDM Systems Key Takeaways Conclusion References Chapter 9: Best Practices to Realize Data Quality Introduction BP 7: Automate the Integration of Critical Data Elements BP 8: Define the SoR and Securely Capture Transactional Data in the SoR/OLTP System BP 9: Build and Manage Robust Data Integration Capabilities BP 10: Distribute Data Sourcing and Insight Consumption Key Takeaways Conclusion References Sustain Phase Chapter 10: Data Governance Introduction Data Governance Principles Data Governance Design Components Implementing the Data Governance Program Data Observability Data Compliance – ISO 27001 and SOC2 Key Takeaways Conclusion References Chapter 11: Protecting Data Introduction Data Classification Data Safety Data Security Key Takeaways Conclusion References Chapter 12: Data Ethics Introduction Data Ethics Importance of Data Ethics Principles of Data Ethics Model Drift in Data Ethics Data Privacy Managing Data Ethically Key Takeaways Conclusion References Appendix 1: Abbreviations and Acronyms Appendix 2: Glossary Appendix 3: Data Literacy Competencies About the Author Index

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Book SynopsisA hands-on guide to evolving your company with ethical AI along with thought-provoking insights and predictions from a variety of well-known industry leaders In Our Planet Powered by AI, renowned AI strategist and pioneer Mark Minevich delivers an exciting and practical discussion of how to implement groundbreaking artificial intelligence technologies at every level of your organization. You'll learn to create sustainable, effective competitive advantage by introducing previously unheard-of levels of adaptability, resilience, and innovation into your company. Using real-world case studies from a variety of well-known industry leaders, the author explains the strategic archetypes, technological infrastructures, and cultures of sustainability you'll need to ensure your firm's next-level digital transformation takes root. You'll also discover: How AI can enable new business strategies, models, and ecosystems of innovation and growth HowTable of ContentsForeword ix Introduction xiii Prologue xxv Part I AI’s Impact on Modern Work and Life 1 Chapter 1 Current Challenges and Issues with AI 5 Chapter 2 AI for Social Good 15 Part II Digital Transformation and Society 31 Chapter 3 The Future of Work 33 Chapter 4 Digital Transformation Models and Digital Disruption 57 Chapter 5 Technologies Driving Society’s Digital Transformation 79 Part III Social Impacts of Digital Inclusion 101 Chapter 6 Ethics, Safety, and Security Concerns 103 Chapter 7 An Ecosystem and Investment Approach to AI Adoption 129 Chapter 8 AI Culture and Society 135 Chapter 9 Social and Financial Inclusion 147 Part IV Foreseeing the Future 165 Chapter 10 Future Cities and Societies 167 Chapter 11 The Next Generation 173 Chapter 12 The Future I Envision When I Dream 187 Notes 193 List of Contributors 203 Acknowledgments 207 About the Author 209 Index 213

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Book SynopsisTable of ContentsAbout the Author xi Foreword xiii Preface xix Acknowledgments xxiii Part I Roadmap of AI in Healthcare 1 1 History of AI and Its Promise in Healthcare 3 1.1 What is AI? 5 1.2 A Classification System for Underlying AI/ML Algorithms 14 1.3 AI and Deep Learning in Medicine 17 1.4 The Emergence of Multimodal and Multipurpose Models in Healthcare 20 References 23 2 Building Robust Medical Algorithms 27 2.1 Obtaining Datasets That are Big Enough and Detailed Enough for Training 30 2.2 Data Access Laws and Regulatory Issues 33 2.3 Data Standardization and Its Integration into Clinical Workflows 34 2.4 Federated AI as a Possible Solution 36 2.5 Synthetic Data 40 2.6 Data Labeling and Transparency 43 2.7 Model Explainability 45 2.8 Model Performance in the Real World 50 2.9 Training on Local Data 52 2.10 Bias in Algorithms 53 2.11 Responsible AI 60 References 62 3 Barriers to AI Adoption in Healthcare 67 3.1 Evidence Generation 71 3.2 Regulatory Issues 74 3.3 Reimbursement 76 3.4 Workflow Issues with Providers and Payers 78 3.5 Medical- Legal Barriers 81 3.6 Governance 83 3.7 Cost and Scale of Implementation 85 3.8 Shortage of Talent 86 References 86 4 Drivers of AI Adoption in Healthcare 91 4.1 Availability of Data 92 4.2 Powerful Computers, Cloud Computing, and Open Source Infrastructure 93 4.3 Increase in Investments 94 4.4 Improvements in Methodology 95 4.5 Policy and Regulatory 95 4.5.1 Fda 95 4.5.2 Other Bodies 100 4.6 Reimbursement 102 4.7 Shortage of Healthcare Resources 105 4.8 Issues with Mistakes, Inefficient Care Pathways, and Non- personalized Care 106 References 110 Part II Applications of AI in Healthcare 113 5 Diagnostics 115 5.1 Radiology 115 5.2 Pathology 122 5.3 Dermatology 124 5.4 Ophthalmology 125 5.5 Cardiology 127 5.6 Neurology 132 5.7 Musculoskeletal 133 5.8 Oncology 134 5.8.1 Diagnosis and Treatment of Cancer 136 5.8.2 Histopathological Cancer Diagnosis 136 5.8.3 Tracking Tumor Development 136 5.8.4 Prognosis Detection 137 5.9 Gi 139 5.10 Covid- 19 139 5.11 Genomics 140 5.12 Mental Health 141 5.13 Diagnostic Bots 142 5.14 At Home Diagnostics/Remote Monitoring 144 5.15 Sound AI 148 5.16 AI in Democratizing Care 149 References 150 6 Therapeutics 157 6.1 Robotics 158 6.2 Mental Health 159 6.3 Precision Medicine 161 6.4 Chronic Disease Management 164 6.5 Medication Supply and Adherence 167 6.6 Vr 168 References 169 7 Clinical Decision Support 171 7.1 AI in Decision Support 176 7.2 Initial Use Cases 180 7.3 Primary Care 182 7.4 Specialty Care 185 7.4.1 Cancer Care 185 7.4.2 Neurology 185 7.4.3 Cardiology 186 7.4.4 Infectious Diseases 187 7.4.5 Covid- 19 187 7.5 Devices 188 7.6 End- of- Life AI 189 7.7 Patient Decision Support 190 References 191 8 Population Health and Wellness 195 8.1 Nutrition 196 8.2 Fitness 200 8.3 Stress and Sleep 201 8.4 Population Health and Management 204 8.5 Risk Assessment 206 8.6 Use of Real World Data 208 8.7 Medication Adherence 208 8.8 Remote Engagement and Automation 209 8.9 Sdoh 211 8.10 Aging in Place 212 References 214 9 Clinical Workflows 217 9.1 Documentation Assistants 218 9.2 Quality Measurement 225 9.3 Nursing and Clinical Assistants 225 9.4 Virtual Assistants 227 References 230 10 Administration and Operations 233 10.1 Providers 234 10.1.1 Documentation, Coding, and Billing 234 10.1.2 Practice Management and Operations 238 10.1.3 Hospital Operations 240 10.2 Payers 243 10.2.1 Payer Administrative Functions 244 10.2.2 Fraud 246 10.2.3 Personalized Communications 247 References 248 11 AI Applications in Life Sciences 251 11.1 Drug Discovery 252 11.2 Clinical Trials 261 11.2.1 Information Engines 264 11.2.2 Patient Stratification 267 11.2.3 Clinical Trial Operations 268 11.3 Medical Affairs and Commercial 271 References 272 Part III the Business Case for Ai in Healthcare 275 12 Which Health AI Applications Are Ready for Their Moment? 277 12.1 Methodology 278 12.2 Clinical Care 281 12.3 Administrative and Operations 289 12.4 Life Sciences 291 References 293 13 The Business Model for Buyers of Health AI Solutions 295 13.1 Clinical Care 298 13.2 Administrative and Operations 305 13.3 Life Sciences 309 13.4 Guide for Buyer Assessment of Health AI Solutions 312 References 313 14 How to Build and Invest in the Best Health AI Companies 315 14.1 Barriers to Entry and Intellectual Property (IP) 316 14.1.1 Creating Defensible Products 318 14.2 Startups Versus Large Companies 319 14.3 Sales and Marketing 321 14.4 Initial Customers 324 14.5 Direct- to- Consumer (D2C) 325 14.6 Planning Your Entrepreneurial Health AI Journey 327 14.7 Assessment of Companies by Investors 329 14.7.1 Key Areas to Explore for a Health AI Company for Investment 329 References 330 Index 333

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Book Synopsis

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Book Synopsis*FRANKIE BOYLE''S GRIPPING CRIME NOVEL: AN INSTANT SUNDAY TIMES BESTSELLER * ''A dark and entertaining tranche of Glasgow noir . . . [A] deft, engaging thriller'' Observer ''Full of scintillating sentences and perfect lines of dialogue'' Sunday Times _______________________________________________________________Glasgow, 2015. When Valium addict Felix McAveety''s best friend Marina is found murdered in the local park, he goes looking for answers to questions that he quickly forgets. Felix enlists the help of a brilliant but mercurial GP; a bright young trade unionist; a failing screenwriter; semi-celebrity crime novelist Jane Pickford; and his crisis fuelled downstairs neighbour Donnie.Their investigation sends them on a bewildering expedition that takes in Scottish radical politics, Artificial Intelligence, cults, secret agents, smugglers and vegan record shops.Trade ReviewA darkest noir, unputdownable crime novel that swerves and surprises, with a gut-punch ending. I loved it! * Denise Mina *A gloriously funny mystery that bucks the "cosy crime' trend" . . . Peppered with one-liners, it reads Raymond Chandler in Glasgow . . . Boyle regularly deploys the beautifully offbeat imagery that characterises the best of his stand-up * Daily Telegraph *Starts off the funniest noir - like a Glaswegian Big Lebowski - then takes you somewhere suddenly heartbreaking . . . A debut novel that makes me absolutely INSIST there is more to come * Marina Hyde *The gags are so good that the book doesn't outstay its welcome [...] anybody who loves jet-black humour is in for a treat * Daily Express *Reads like a twisted Caledonian take on Robert Altman's The Long Goodbye. Inherent vices and scalpel-sharp jokes vie with a very human concern for those least garlanded in the rat race of life * Ian Rankin *Part whodunnit, part social safari, part extended stand-up monologue . . . the novel is full of scintillating sentences and perfect lines of dialogue * Sunday Times *An enjoyably dark and entertaining tranche of Glasgow noir . . . Imagine Withnail and I stumbling into a Bond movie co-written by William McIlvanney and Mick Herron . . . [A] deft, engaging thriller * Observer *A surprisingly moving and beautiful journey through one man's sh*tshow of a friend's death/hangover * Lucy Prebble, screenwriter for Succession *A fine slice of contemporary noir, full of acute insight into the way we live now -- John Williams * Mail on Sunday *Just finished this and f*ck me, it's total class!! I'm sure all the expletive swears are coming out for this one - and so they should. Ace -- Helen Stanton, Forum BooksMeantime is a tremendous book: a detective story full of twists and turns that is as beautifully written as it is darkly comic. . . Not surprisingly, Meantime is very, very funny. But it is also a gripping work of stylised crime fiction that marks, I suspect, a new and exciting chapter in Boyle's multi-faceted career -- Matthew D’ancona * Tortoise *Remarkably moving * Scotsman *A sharply written, memorable read * Scottish Field *Meantime flies along, filled with laughs and moments that pull you up sharp * Sun (Scotland) *Boyle's key strength is in creating such a believable milieu, even when events are exaggerated, underpinned but never derailed by a tongue-in-cheek humour * Chortle *Frankie Boyle's fiction debut is genuinely engrossing * Independent *It's both funny and moving * Guardian *[Frankie Boyle] has graduated into an extremely fine author with his first novel . . . The book lays bare the various worlds of Glasgow . . . and it slowly becomes that awful word, unputdownable, as the fascinating mixture of violence, drugs and unexpected humour surround the reader * On Yorkshire Magazine *Boyle's darkly comic debut unfolds amid vivid scenes of the seamy Glasgow underworld, its hard-bitten humour offset by an unexpectedly tender conclusion * Daily Mail *Word-perfect dialogue and wild imagination * Strathspey & Badenoch Herald *

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Book Synopsis Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications.  Deep learning is quickly integrating itself into the technology landscape. Its applications range from applicable data science to deep fakes and so much more. It is crucial for aspiring data scientists or those who want to enter the field of AI to understand deep learning concepts.  The best way to learn is by doing. You''ll develop a working knowledge of not only TensorFlow, but also related technologies such as Python and Keras. You''ll also work with Neural Networks and other deep learning concepts. By the end of the book, you''ll have a collection of unique projects that you can add to your GitHub profiles and expand on for professional application.  Table of ContentsChapter 1— Perceptrons • Introduction to Perceptrons• Working of a Perceptron• Program to understand the working of a Perceptron Chapter 2: Neural Networks • Introduction to Neural Networks• Types of Neural Networks• How each neural network works• Program to understand the working of Neural Networks Chapter 3: Project 1- DJ Neuron • About the Project: Creating Music Using Neural Networks • Requirements• Explanation of concepts used• Architecture of the Neural Network • Source code with line by line instructions Chapter 4: Project 2- Artistic Neurons • About the Project: Adding colour to black and white images • Requirements• Explanation of concepts used• Architecture of the Neural Network • Source code with line by line instructions Chapter 5: Project 3- Go HD • About the Project: Restoration of images for better quality • Requirements • Explanation of concepts used• Architecture of the Neural Network • Source code with line by line instructions Chapter 6: Project 4- Voice Experiments • About the Project: Voice Manipulation • Requirements • Explanation of concepts used• Architecture of the Neural Network • Source code with line by line instructions Chapter 7: Project 5- Imposters • About the Project: Fake Image Recognition • Requirements• Explanation of concepts used• Architecture of the Neural Network • Source code with line by line instructions Chapter 8: Project 6 - Gaming is Fun • About the Project: MI-agent training using Unity. Learn to create Artificially Intelligent Characters. Requirements Explanation of concepts used Architecture of the Neural Network Source code with line by line instructions

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Book SynopsisUser experience (UX) design practices have seen a fundamental shift as more and more software products incorporate machine learning (ML) components and artificial intelligence (AI) algorithms at their core. This book will probe into UX design''s role in making technologies inclusive and enabling user collaboration with AI. AI/ML-based systems have changed the way of traditional UX design. Instead of programming a method to do a specific action, creators of these systems provide data and nurture them to curate outcomes based on inputs. These systems are dynamic and while AI systems change over time, their user experience, in many cases, does not adapt to this dynamic nature. Applied UX Design for Artificial Intelligence will explore this problem, addressing the challenges and opportunities in UX design for AI/ML systems, look at best practices for designers, managers, and product creators and showcase how indiviTable of ContentsPart 1: Intelligence.- Chapter 1: On intelligence.- Chapter 2: Intelligent Agents.- Chapter 3: Incorporating Artificial Intelligence.- Part 2: Decisions.- Chapter 4: Building Trust.- Chapter 5: Designing Feedback.- Chapter 6: Handling Errors.- Part 3: Design.- Chapter 7: IE Ethics.- Chapter 8: Prototyping AI Products.- Part 4: Teamwork .- Chapter 9: Understanding AI Terminology.- Chapter 10: Working Effectively with AI Tech Teams.- Epilogue.

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Book SynopsisArtificial intelligence (AI) is presented as a solution to the greatest challenges of our time, from global pandemics and chronic diseases to cybersecurity threats and the climate crisis. But AI also contributes to the climate crisis by running on technology that depletes scarce resources and by relying on data centres that demand excessive energy use. Is AI Good for the Planet? brings the climate crisis to the centre of debates around AI, exposing its environmental costs and forcing us to reconsider our understanding of the technology. It reveals why we should no longer ignore the environmental problems generated by AI. Embracing a green agenda for AI that puts the climate crisis at centre stage is our urgent priority. Engaging and passionately written, this book is essential reading for scholars and students of AI, environmental studies, politics, and media studies and for anyone interested in the connections between technology and the environment.Trade Review"Benedetta Brevini lays out the risks posed to our planet by a headlong and indiscriminate embrace of AI, and what we can do to manage them. I urge you to read this essential book."—Michael E. Mann, author of The New Climate War "Bold, perceptive, and urgently topical, Is AI Good for the Planet? both informs and inspires."—Frank Pasquale, author of New Laws of Robotics "[T]he book may serve as a spark for public discourse and an urgent call to action for more research, policy action and public advocacy on this subject. Given its brevity and its non-technical, opinionated and engaging writing style, it is well-positioned to achieve this aim."—PrometheusTable of ContentsAcknowledgements Introduction Chapter One: Defining AI: beyond the Hype Chapter Two: Controlling AI: understanding data capitalism Chapter Three: Why AI worsens the Climate Crisis Conclusion: AI and the Climate Crisis: what we can do about it Notes References Index

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Book SynopsisThis volume offers an innovative and counter-intuitive study of how and why artificial intelligence-infused weapon systems will affect the strategic stability between nuclear-armed states. Johnson demystifies the hype surrounding artificial intelligence (AI) in the context of nuclear weapons and, more broadly, future warfare. The book highlights the potential, multifaceted intersections of this and other disruptive technology – robotics and autonomy, cyber, drone swarming, big data analytics, and quantum communications – with nuclear stability. Anticipating and preparing for the consequences of the AI-empowered weapon systems are fast becoming a critical task for national security and statecraft. Johnson considers the impact of these trends on deterrence, military escalation, and strategic stability between nuclear-armed states – especially China and the United States. The book draws on a wealth of political and cognitive science, strategic studies, and technical analysis to shed light on the coalescence of developments in AI and other disruptive emerging technologies.Artificial intelligence and the future of warfare sketches a clear picture of the potential impact of AI on the digitized battlefield and broadens our understanding of critical questions for international affairs. AI will profoundly change how wars are fought, and how decision-makers think about nuclear deterrence, escalation management, and strategic stability – but not for the reasons you might think.Trade Review'This superb book is a technical tour de force with a timely approach to emerging challenges to nuclear deterrence. In this way, it has significant potential to inform current policy among states as they contemplate – for example – the growth of Chinese nuclear forces.'Rose Gottemoeller, Payne Distinguished Lecturer at the Center for International Security and Cooperation, Stanford University'Johnson has crafted a superb narrative that examines the strategic risks of artificial intelligence and its potential impact on deterrence, stability and the maintenance of nuclear weapon capabilities. His evidence-based recommendations on the responsible application of artificial intelligence will provide policy makers and strategists with a range of ethical responses to the dilemmas of using non-human cognitive functions in national security.'Major General Mick Ryan'Artificial intelligence and the future of warfare present the reader with a clear and elegant understanding of artificial intelligence as it provides a robust technical foundation concerning key technological advances in the evolution of AI for a non-technical audience.'Augusto C. Dall’Agnol, Journal of Strategic Studies 'With Artificial Intelligence and the Future of Warfare, James Johnson truly advances the conversation around intelligence, strategic stability, and technology. Johnson is undoubtedly well-positioned to write this book given his education as a Political Scientist, lengthy career working in China, and intellectual interest in emerging technology, especially Artificial Intelligence (AI). While there are similar books on the intersection of emerging technology and geopolitics, Johnson’s work stands out since it addresses how AI influences intelligence collection and analysis which in turn shapes a state’s perception and actions. This approach is novel making Johnson’s contribution well-worth the time for strategic strategists.'Mark Grzegorzewski, Intelligence and National Security'A significant contribution to the wave of recent scholarship on the potential implications of Artificial Intelligence (AI) for strategic warfare and great power competition. It is a must-read for strategic studies scholars, decision-makers, and military leaders.'Reuben Steff, Defense & Security Analysis'The book provides a good and comprehensive perspective on the nu­clear power play and strategic stability and how it is shaped by new technological developments.'Thomas Reinhold and Christian Reuter, Z AuBen Sicherheitspolit (2022) -- .Table of ContentsIntroduction: Opening the AI Pandora’s boxPart I: Destabilizing AI renaissance1 Military AI primer2 Artificial intelligence in the 2nd nuclear age Part II: Military AI superpowers3 New challenges to military-techno Pax Americana4 U.S.-China crisis stability under the AI nuclear shadowPart III: Nuclear instability redux?5 Hunting for nuclear weapons in the digital age6 Fast and the furious: Drone swarming and hypersonic weapons7 The AI-cyber security nexus8 Delegating strategic decisions to intelligent machinesConclusion: Managing an AI futureIndex

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Book Synopsis'The best up-to-date, go-to book on the social and economic implications of artificial intelligence - Tyler Cowen'Rule of the Robots explores the future implications of artificial intelligence as a uniquely scalable and potentially disruptive technology.In this sequel to his prescient New York Times bestseller Rise of the Robots, Martin Ford presents us with a striking vision of the very near future. He argues that AI is a uniquely powerful technology, a kind of "electricity of intelligence" that is altering every dimension of human life, often for the better with advanced science being done by machines who can solve problems humans can not. AI has the potential to help us fight climate change or the next pandemic, but it also has a capacity for profound harm. Deep fakes-AI-generated audio or video of events that never happened-are poised to cause havoc throughout society. AI empowers authoritarian regimes like China with unprecedented mechanisms for social control. And AI can be deeply biased, learning bigoted attitudes from the data used to train algorithms and perpetuating them. Hard-hitting and thought-provoking, covering everything from self-driving cars to the history of deep learning to apps for diagnosing skin cancer, Rule of the Robots challenges our fears and preconceptions about artificial intelligence. Ford argues that AI is here to stay and the real question is not how to stop it, but how to control its negative potential and harness its power for good as AI transforms our economy, our politics, and our lives.Trade ReviewProbably the most compelling single-volume book so far on AI's advance and the opportunities and challenges associated with its multi-faceted impact on the world. Those in AI and those outside it will get a lot out of his clear-eyed and critical perspective. I highly recommend it -- James Manyika, Chairman and Director of the McKinsey Global InstituteThere is no technology more important today than AI. Martin Ford continues his tradition of clear insights and observations about this important topic in a well-researched page-turner. A delightful book! -- Erik Brynjolfsson, Director of the Stanford Digital Economy Lab and co-author of The Second Machine AgeThe best up-to-date, go-to book on the social and economic implications of artificial intelligence -- Tyler Cowen, Professor of Economics at George Mason UniversityAn incisive, balanced, and well-informed discussion of where AI stands today, how it may evolve, and the risks it poses to human society -- Stuart Russell, Professor of Computer Science at the University of California, Berkeley and co-author of Artificial Intelligence: A Modern ApproachWriting about the future of robotics is a dangerous endeavor, since it illuminates every aspect of our lives with a startling, uncharted perspective. Ford navigates this challenge admirably, with an exceptional blend of depth, rigor and clarity -- Judea Pearl, winner of the A.M. Turing Award and co-author of The Book of WhyRule of the Robots is subtle and clever, a book that will challenge readers to reassess their position on AI -- Engineering And TechnologyAccording to software developer Martin Ford, artificial intelligence (AI) will probably transform society faster than electricity did, but with unpredictable effects. His well-informed study notes a 2016 forecast that within five years, radiologists would be overtaken by advances in AI. But that hasn't happened: AI cannot currently integrate key information from sources such as clinical notes. AI's future, Ford concludes, lies somewhere between the science-fictional extremes of utopian Star Trek and dystopian The Matrix -- NatureA good read and . . . a balanced perspective on both the benefits and costs of increasingly widespread use of AI -- Diane Coyle, Enlightenment Economics

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Book SynopsisFor readers of Sapiens and Homo Deus and viewers of The Social Dilemma, psychologist Tomas Chamorro-Premuzic tackles one of the biggest questions facing our species: Will we use artificial intelligence to improve the way we work and live, or will we allow it to alienate us?It's no secret that AI is changing the way we live, work, love, and entertain ourselves. Dating apps are using AI to pick our potential partners. Retailers are using AI to predict our behavior and desires. Rogue actors are using AI to persuade us with bots and misinformation. Companies are using AI to hire us—or not.In I, Human psychologist Tomas Chamorro-Premuzic takes readers on an enthralling and eye-opening journey across the AI landscape. Though AI has the potential to change our lives for the better, he argues, AI is also worsening our bad tendencies, making us more distracted, selfish, biased, narcissistic, entitled, predictable, and impatient.It doesn't have to be this way. Filled with fascinating insights about human behavior and our complicated relationship with technology, I, Human will help us stand out and thrive when many of our decisions are being made for us. To do so, we'll need to double down on our curiosity, adaptability, and emotional intelligence while relying on the lost virtues of empathy, humility, and self-control.This is just the beginning. As AI becomes smarter and more humanlike, our societies, our economies, and our humanity will undergo the most dramatic changes we've seen since the Industrial Revolution. Some of these changes will enhance our species. Others may dehumanize us and make us more machinelike in our interactions with people. It's up to us to adapt and determine how we want to live and work.The choice is ours.What will we decide?Trade ReviewNamed one of the best management books of 2023 by Børsen."I, Human argues compellingly that artificial intelligence is altering human intelligence—fuelling narcissism, diluting self-control, reinforcing prejudice—and reveals how human learning can still counteract the malign effects of machine learning. Tomas's easy style and dry humour belie the seriousness with which he tackles this vital issue of our time. Take note before the robots take over how you think." — City A.M."Fascinating, original and thought-provoking." — E&T magazine"The book's final chapter, How to Be Human, is headed with a quote from Maya Angelou: "You may not control all the events that happen to you, but you can decide not to be reduced by them." This encapsulates the purpose of this unique book: to explain how AI is changing our lives, values, and ways of being—right now, never mind what this implies for the future—and to propose the means by which AI should and can enhance and enrich human experience rather than reduce it." — Developing Leaders magazine"…this is not an AI book like others. It does not try to predict the future or bamboozle readers with technological geekery. Instead, it assesses where this technology has brought us thus far and what we can do with it to retain what is most important to us as people." — Financial Times"…a shrewd, insightful take on the dangers of AI." — Publisher's WeeklyAdvance Praise for I, Human: "A compelling read about how AI is shaping us—and how we should shape it. Tomas Chamorro-Premuzic examines how technology can augment our intelligence and reminds us to invest in the human skills that robots can't replace." — Adam Grant, #1 New York Times bestselling author, Think Again; host, TED podcast Re:Thinking"A must-read for anyone who has wondered how we can maintain our humanity amid the superpowerful prediction machines we've created." — Angela Duckworth, author, New York Times bestselling Grit"Techno-zealots and doomsayers dominate the debate about artificial intelligence, which is why this unique book is such a breath of fresh air. I, Human is a strikingly clear-eyed account of the fraught but fertile relationship we already have with AI—and an inspiring argument for how, in the future, it can help us maintain and enhance rather than degrade what makes us essentially human." — Oliver Burkeman, New York Times bestselling author, Four Thousand Weeks"If you want to understand how we can best thrive in a world that is rapidly changing because of AI, and feel hopeful and confident about the role you can play, you'll find this book to be both brilliant and essential. Full of insights and practical tips, I, Human will prepare you for the future by focusing your attention on the very traits that make human nature unique." — Francesca Gino, professor, Harvard Business School; author, Rebel Talent"I, Human argues compellingly that artificial intelligence is altering human intelligence—fueling narcissism, diluting self-control, reinforcing prejudice—and reveals how human learning can still counteract the malign effects of machine learning. Tomas's easy style and dry humor bely the seriousness with which he tackles this vital issue of our time. Take note before the robots take over how you think." — Octavius Black, founder and CEO, MindGym"At last, a book on AI that focuses on humans rather than machines. A powerful case for reclaiming some of our most valuable neglected virtues." — Dorie Clark, Wall Street Journal bestselling author, The Long Game; executive education faculty, Duke University Fuqua School of Business

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Book SynopsisWhat will you do when your AI misbehaves?The promise of artificial intelligence is automated decision-making at scale, but that means AI also automates risk at scale. Are you prepared for that risk?Already, many companies have suffered real damage when their algorithms led to discriminatory, privacy-invading, and even deadly outcomes. Self-driving cars have hit pedestrians; HR algorithms have precluded women from job searches; mortgage systems have denied loans to qualified minorities. And often the companies who deployed the AI couldn't explain why the black box made the decision it did.In this environment, AI ethics isn't merely an academic curiosity, it's a business necessity. In Ethical Machines, Reid Blackman gives you all you need to understand AI ethics as a risk management challenge. He'll help you build, procure, and deploy AI in a way that's not only ethical but also safe in terms of your organization's reputation, regulatory compliance, and legal standing—and do it at scale.And don't worry—the book's purpose is to get work done, not to ponder deep and existential questions about ethics and technology. Blackman's clear and accessible writing helps make a complex and often misunderstood concept like ethics easy to grasp. Most importantly, Blackman makes ethics actionable by tackling the big three ethical risks with AI—bias, explainability, and privacy—and tells you what to do (and what not to do) to mitigate them.With practical approaches to everything from writing a strong statement of AI ethics principles to creating teams that effectively evaluate ethical risks, Ethical Machines is the one guide you need to ensure your AI advances your company's objectives instead of undermining them.Trade ReviewAdvance Praise for Ethical Machines:"Finally! A book that demystifies how to responsibly manage AI and does it in an engaging, succinct, and practical way. This is a must-read for anyone embarking on a machine learning journey. Blackman is a true expert in AI ethics, and this book is an indispensable resource." — Siobhan Hanna, Managing Director, AI Data Solutions, TELUS International Artificial Intelligence"An excellent read that turns a complex topic into understandable and actionable components. Business leaders need to understand that AI ethics isn't just hand-waving. It poses real risks to brand and profit. Fortunately, Blackman is outstanding at laying out how you should think about AI ethics and the steps you should take." — Joel Shapiro, Professor, Kellogg School of Management, Northwestern University"Reid Blackman's new book, Ethical Machines, is a fresh contribution to the complex challenge of putting AI ethics into practice. Using vivid examples and accessible storytelling, Blackman introduces an actionable framework for comprehensive ethical risk management that will help companies avoid critical pitfalls as they incorporate AI into their products and processes." — Cara LaPointe, PhD, Codirector, Johns Hopkins Institute for Assured Autonomy; author, The Blockchain Ethical Design Framework"Companies of all stripes are increasingly looking to use AI and hoping to build it ethically and responsibly. In this charming book, Reid Blackman provides practical guidance to turn these hopes into a reality." — David Danks, Professor, Data Science & Philosophy, University of California, San Diego

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Book SynopsisFrom finance to artificial intelligence, genetic algorithms are a powerful tool with a wide array of applications. But you don't need an exotic new language or framework to get started; you can learn about genetic algorithms in a language you're already familiar with. Join us for an in-depth look at the algorithms, techniques, and methods that go into writing a genetic algorithm. From introductory problems to real-world applications, you'll learn the underlying principles of problem solving using genetic algorithms. Evolutionary algorithms are a unique and often overlooked subset of machine learning and artificial intelligence. Because of this, most of the available resources are outdated or too academic in nature, and none of them are made with Elixir programmers in mind. Start from the ground up with genetic algorithms in a language you are familiar with. Discover the power of genetic algorithms through simple solutions to challenging problems. Use Elixir features to write genetic algorithms that are concise and idiomatic. Learn the complete life cycle of solving a problem using genetic algorithms. Understand the different techniques and fine-tuning required to solve a wide array of problems. Plan, test, analyze, and visualize your genetic algorithms with real-world applications. Open your eyes to a unique and powerful field - without having to learn a new language or framework. What You Need: You'll need a macOS, Windows, or Linux distribution with an up-to-date Elixir installation.

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Book SynopsisWINNER: Independent Press Awards 2023 - Category: Technology AI presents a new paradigm in software development, representing the biggest change to how we think about quality and testing in decades. Many of the well known issues around AI, such as bias, manifest themselves as quality management problems. This book, aimed at testing and quality management practitioners who want to understand more, covers trustworthiness of AI and the complexities of testing machine learning systems, before pivoting to how AI can be used itself in software test automation.Trade ReviewA brilliant reference with a focus on introducing the reader to new AI ideas and challenges. The danger with AI and software testing is the mistaken belief that people understand it all. This book addresses this issue by opening the reader up to a rich source of references & useful concepts using use cases, models and references, to both stimulate and challenge the reader's own knowledge of this broad subject. Highly recommended. -- Paul Mowat MBCS CITP, BCS SiGIST Social Media Secretary & Committee Member, Quality Test Engineer Director, Deloitte UKAI-based systems conquer more and more areas of our daily life. People are concerned whether these systems are trustworthy. 'Artificial Intelligence and Software Testing' tackles this issue and provides an insight into AI quality and how it differs from conventional software quality, and where the difficulties and challenges are in testing machine learning systems. A great introduction into this topic and must read for all interested in building AI-based systems that you can trust. -- Klaudia Dussa-Zieger, Chair GTB & Vice President ISTQB®, Head of ISTQB® Certified Tester AI Testing (CT-AI) taskforceIn the ever expansive and evolving virtual domain, the prominence of AI is becoming more and more prolific, and this evolution will not be without its challenges. This title provides an excellent resource into the potential dilemmas faced in this evolutionary field as the virtual, cognitive, and physical spaces become more interlinked with the dawn of the metaverse. The part that humans play in the growth, development and testing of AI is discussed. Supported by a wealth of experience, research, and evidence from the authors, the title provides a great introduction to and understanding of AI and software testing. Highly recommended for all with an interest in this area. -- Jonathan Miles MBA BSc(Hons) FCMI, Head of Strategic Intelligence, MimecastShift Right! A concept you won’t find in ‘The Seven Principles of Testing’. 'Artificial Intelligence and Software Testing' puts the principles into perspective. Not only does it explore early testing, but it also looks at the concept of exhaustive testing thoroughly and effectively. As a trainer of software testing I will definitely use all this book has to offer. Guiding the next generation of testers to question the intricacies of machine learning. A must for anyone in tech, not just software testers. -- Rachel Hurley MBCS TAP.dip, Technical Trainer (Software Testing)As the title describes, this book is a robust AI and ML testing exploration that also dives into the juxtaposition of the trustworthiness and bias in AI systems. It touches on the basis of ontologies and how to enable the considerable impact of testing and monitoring of AI-based systems. After reading this you would be able to answer an important challenge: how to determine that your AI system has been extensively tested? -- Dina Dede, AI/ML and Cloud Architect Lead, UKThis book beautifully captures the game-changing complexity of artificial intelligence (AI) and the traditional discipline of software quality management. It is a comprehensive manual addressing the conundrum and tantalizing promise of both disciplines with good pace and a distinct future-present context. Forget waterfall and DevOps, we’re right shifting into OpsDev, AIOps and digital twins in the metaverse, so things are about to get a whole lot more interesting. Excellent effort, and a much-needed treatment of this topic by true experts. -- Jude Umeh FBCS CITP, Senior Program Architect, Salesforce'Artificial Intelligence and Software Testing' is a great read. The vast experience of the authors is evident as they comprehensively explain the challenges and benefits of not only applying AI to testing, but also testing the AI software itself. I found the insight into the shift-right approach and its application during the development of the test and trace application fascinating. A must read for any testing/QA professional plus any C-suite looking to rapidly increase their ROI on testing. -- Anil Pande, Managing Partner, TestPro Consulting LtdThis book is a very good introduction to using AI in software testing as well as testing AI systems, covering several relevant topics like societal risk, bias, ethical behavior, quality, trustworthiness, and the problems associated with AI/ML systems. I specifically liked the section that details on the problems associated with AI/ML systems. I would recommend this book to anyone who is starting their study on software testing vis-a-vis AI/ML systems. -- Venkat Ramakrishnan, Software Quality Leader And Software Testing Technologist'Artificial Intelligence and Software Testing' is a valuable resource for anyone curious in how to approach testing AI models as they expand into our daily lives. This is a clear, informative read which discusses within each chapter different testing challenges with AI software and advice on how to handle them effectively. I can highly recommend this to testers and students alike. -- Katy Hannath BSc(Hons), MSc in Artificial Intelligence and Data Science student, & Quality Assurance Tester, VISR DynamicsThis book is an exceptionally practical resource which is a remarkable reference guide to understanding the foundations of AI & ML for anyone wishing to build a career in AI or define a test approach. It has a clear, direct, and concise explanation of AI, ML, ethics, ontology, quality, bias, challenges, test automation, and the significance of ‘shift-right’ testing. It offers thorough, data-driven and real-world examples that bring together the rich wealth of experience from these expert authors and authorities in this area. -- Boby Jose BSc MBA MBCS, Author of BCS publication ‘Test Automation: A manager’s guide’What an exciting and relevant publication! Beyond the positive game-changing societal benefits delivered by AI, it has proven equally disruptive to all aspects of software engineering including software testing. This book provides great insight into new build and test design techniques to augment our traditional thinking. An essential guide for technology leaders and test professionals alike, looking to understand how to approach the critical problem of building and testing today’s complex and often unpredictable AI systems. -- Jack Mortassagne, Director at Cigniti Technologies and TMMI Accredited AssessorThis is a great book for those who want to gain more insight into how AI will affect the software testing profession. The writers introduce the challenges in AI in an easy-to-understand manner, while the case studies showcased are extremely interesting and contemporary, clearly exemplifying the topics presented. Brilliant read and highly recommended! -- Dr Diana Hintea BEng(Hons) PhD SFHEA, Associate Head of School (School of Computing, Electronics and Mathematics), Coventry UniversityIn a time the promised paradigm shift of Artificial intelligence is starting to have a real-world impact, this is a vitally important book. It explains the social, ethical, and technical concerns around AI in an easy to understand way, making a complex subject easily accessible. Everyone involved in IT is likely to be impacted by AI whether from a business, technical, ethical, or quality point of view and so this book will be an invaluable resource for everyone in IT. As a Testing and Quality specialist, this is going to have pride of place on my bookshelf as a practical, real-world reference for helping me navigate testing and quality in the emerging world of AI. -- Bryan Jones MBCS, Director of Testing Practice, Sopra Steria Private SectorThis book is a must-read for anyone in software testing with responsibility for quality assuring AI technology that must engender public trust. With topics that feel both familiar and challenging, the authors confidently explore a range of subjects to broaden and deepen the reader’s understanding of the intersection of AI and testing. -- Bronia Anderson-Kelly, IT change consultant, Sabiduria LtdThis book addresses an often ignored but critically important aspect of AI implementation: how to ensure that AI's are producing, and continue to produce, acceptable output. As AI is non-deterministic and complex, and dataset quality can be highly variable, it is notoriously difficult to determine suitable test cases for modern systems. In this book, the authors provide practical methods and examples that can be used to ensure AI quality, and as such is an extremely useful resource for anyone implementing systems involving AI and machine learning. -- Peter Brightwell MSc, Intelligent Automation Architect, NDL SoftwareTable of Contents Introduction AI Trustworthiness and Quality Quality and Bias Testing Machine Learning Systems AI-based Test Automation Ontologies for Software Testing Shifting Right into the Metaverse with Digital Twin Testing

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Book SynopsisWhy aren't the most powerful new technologies being used to solve the world's most important problems: hunger, poverty, conflict, inequality, employment, disease? What's missing? From a pioneer in Artificial Intelligence and Machine Learning comes a thought-provoking book that answers these questions. In Link: How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World, Dr. Lorien Pratt explores the solution that is emerging worldwide to take Artificial Intelligence to the next level: Decision Intelligence. Decision Intelligence (DI) goes beyond AI as well, connecting human decision makers in multiple areas like economics, optimization, big data, analytics, psychology, simulation, game theory, and more. Yet despite the sophistication of these approaches, Link shows how they can be used by you and me: connecting us in a way that supercharges our ability to meet the interconnected challenges of our age. Pratt tells the stories of decision intelligence pioneers worldwide, along with examples of their work in areas that include government budgeting, space exploration, emerging democracy conflict resolution, banking, leadership, and much more. Link delivers practical examples of how DI connects people to computers and to each other to help us solve complex interconnected problems. Link explores a variety of scenarios that show readers how to design solutions that change the way problems are considered, data is analyzed, and technologies work together with people. Technology and academics has accelerated beyond our ability to understand or effectively control them. Link brings technology down to earth and connects it to our more natural ways of thinking. It offers a roadmap to the future, empowering us all to make practical steps and take the best actions to solve the hardest problems.Trade ReviewLink is an exercise in abstraction, causality, and modeling. It is about discovering and making visible interdependencies in complex systems. The author distills what she has learned in pithy insights. It takes discipline. You won't regret reading this book. -- Vint Cerf, Internet PioneerLink is the missing link to our understanding of unintended consequences of many of our decisions and actions. This is a book for the ages, moving both technology (like AI) and human decision making to the next level. Must read. -- V R Ferose, SVP and Head of SAP Engineering AcademyThere is an explosion today in the impacts, risks, and opportunities of many decisions by private or governmental entities intended to impact future events. Link is about understanding these decisions and causal relationships. Societal transactions are accelerating: many more than ever are intangible, and there are substantial complexities created by newly discovered information as well as the resulting increase of global interdependencies. Surveillance capitalism, especially as enhanced by AI, is also a substantial risk today. Link is part of the solution: a crucial resource to understand causal chains, especially with the goal of avoiding unintended consequences of decisions involving data and technology. -- Bill Fenwick, Partner Emeritus, Fenwick & West LLPPratt, a former computer science professor who works in artificial intelligence and machine learning, discusses decision intelligence and how it can help solve complex problems in areas like business, finance, and economics by connecting humans with technology and data to think about decisions in a new way. She describes how decisions lead to actions that lead to outcomes in the future and how to use a causal decision diagram to make decisions using advanced technologies to solve difficult problems in organizations. She discusses the concept of decisions as artifacts that can be designed and engineered; the role of artificial intelligence, machine learning, data science, and other technologies; how to model complex decisions; how to shift to a decision-centric view; and the future of decision intelligence. -- Annotation ©2019 * (protoview.com) *Table of ContentsIntroduction 1. Getting Serious about Decisions 2. Breaking through the Complexity Ceiling 3. Technologies, Disciplines, and Other Puzzle Pieces of the Solutions Renaissance 4. How to Build Decision Models 5. The Power of the Decision Model Framework 6. Looking to the Future Conclusion

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Book SynopsisArtificial Intelligence Applications in Human Pathology deals with the latest topics in biomedical research and clinical cancer diagnostics. With chapters provided by true international experts in the field, this book gives real examples of the implementation of AI and machine learning in human pathology.Advances in machine learning and AI in general have propelled computational and general pathology research. Today, computer systems approach the diagnostic levels achieved by humans for certain well-defined tasks in pathology. At the same time, pathologists are faced with an increased workload both quantitatively (numbers of cases) and qualitatively (the amount of work per case, with increasing treatment options and the type of data delivered by pathologists also expected to become more fine-grained). AI will support and leverage mathematical tools and implement data-driven methods as a center for data interpretation in modern tissue diagnosis and pathology. Digital or computational pathology will also foster the training of future computational pathologists, those with both pathology and non-pathology backgrounds, who will eventually decide that AI-based pathology will serve as an indispensable hub for data-related research in a global health care system.Some of the specific topics explored within include an introduction to DL as applied to Pathology, Standardized Tissue Sampling for Automated Analysis, integrating Computational Pathology into Histopathology workflows. Readers will also find examples of specific techniques applied to specific diseases that will aid their research and treatments including but not limited to; Tissue Cartography for Colorectal Cancer, Ki-67 Measurements in Breast Cancer, and Light-Sheet Microscopy as applied to Virtual Histology.The key role for pathologists in tissue diagnostics will prevail and even expand through interdisciplinary work and the intuitive use of an advanced and interoperating (AI-supported) pathology workflow delivering novel and complex features that will serve the understanding of individual diseases and of course the patient.

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Book SynopsisWith surveillance at work extending into the home and the deployment of AI in the workplace already rapidly expanding, concerns have been raised about the ramifications of these developments. Blurring the boundaries between public and private spheres, digital workplace monitoring and digital activity tracking seem set to raise stress levels and undermine trust between employers and employees as they threaten to further infiltrate the world of work. Proposing a clear list of policy options, Ethical AI Surveillance in the Workplace tackles the structural challenges associated with ‘wiring the labour market’, including issues of control, autonomy and voice. From Data Protection Impact Assessments to regulatory sandboxes, and from establishing the right to disconnect to setting up a Code of Ethical Workplace Monitoring, the proposed paths aim to safeguard a responsible deployment of AI-powered monitoring tools within the workplace and protect employees as data subjects whose digital footprints are under constant scrutiny. Wielding the legal, regulatory and institutional tools available, this uniquely structured analysis acts as a comprehensive starting point for discussing these ever-evolving challenges and how they may shape the future of the workplace.Table of ContentsIntroduction; Ron Iphofen Section I. Legal Options Chapter 1. Putting the Employee in the Driving Seat via Reinforced Collective Agreements Chapter 2. Review and Update the Current Legal Framework Chapter 3. Introduce Legislation on Workplace Monitoring Chapter 4. Introduce a Penalty System and Strict Conditionalities Chapter 5. Introduce the Right to obtain an Explanation of the Decision and Challenge It Chapter 6. Introduce the Right of Transparency in Workplace Monitoring Chapter 7. Introduce the Right to Disconnect as a Fundamental Right Chapter 8. Introduce the Right to Health and Safety at Work in the Digital Space Chapter 9. Establish Legislation for the Protection of Whistleblowers and the Development of Effective Communication Corridors in the Workplace Chapter 10. Introduce Review Clauses on Delegated/Implementing acts in Existing Labour Legislation and in any upcoming AI Legislative Proposals Chapter 11. Develop a Workplace/Employee-Specific Data Governance Strategy that Strengthens the Rights to Privacy and Data Protection in the Digital Workplace Chapter 12. Ensure Compliance with a Mandatory Data Protection Impact Assessment for Workplace Monitoring Chapter 13. Redefine the Concept of Consent in the Frame of Workplace Monitoring Chapter 14. Address Psychosocial Risks as an Essential Part of the Occupational Health and Safety (OHS) Legal Framework Section II. Governance Options Chapter 15. Develop a Code of Ethical Workplace Monitoring Chapter 16. Introduce Certification Mechanisms and Standards for Ethical Compliance Chapter 17. Set up an Oversight Body for Workplace Monitoring and other Workplace Governance/Oversight Structures Chapter 18. Establish an Ombudsman for Employment Affairs and Workplace Monitoring Chapter 19. Develop and Establish Procedural Frameworks to Effectively Audit Workplace Algorithmic Systems Chapter 20. Set-up Risk Governance Structures and Algorithmic Risk Assessment Methodologies for Workplace Monitoring Chapter 21. Consider the Particular Risks Related to Keylogging, Workplace Biometrics, Bring Your Own Device (BYOD) Practices Chapter 22. Set up Bodies for Workplace Monitoring Including a High Level Expert Group on Workplace Monitoring Chapter 23. Update any Existing Digital Strategy on Preventing Potential Misuse of AI-Enabled Monitoring Tools Chapter 24. Develop Specialised Training Opportunities for Employers and Employees Alike Section III. Ethics Options Chapter 25. Publicly-Funded Research on Workplace Monitoring should be Subject to a Thorough Ethics Appraisal Chapter 26. Consider Imposing a Moratorium on Certain Workplace Monitoring Tools Chapter 27. A Human in the Loop Approach/Human Oversight Chapter 28. The Explainability and Transparency of AI Monitoring Tools Chapter 29. Draft and Develop an Ethical Charter/Governance Framework Chapter 30. Revisit the Concepts of Worker and of Surveillance Chapter 31. Acknowledge and Address the High Informational Asymmetries Chapter 32. Workplace Monitoring Should Be Considered as High-Risk Chapter 33. Adopt a Broad Definition of AI and Algorithmic Management Chapter 34. Sandboxing Chapter 35. Ethics by Design Chapter 36. Security by design Chapter 37. Misuse/Dual Use Chapter 38. Ethical Leadership Chapter 39. Appeal and Redress Chapter 40. Use ‘Smart’ tools to Handle AI-Related Ethical Risks

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Book Synopsis"Nina Schick is alerting us to a danger from the future that is already here." - Adam Boulton, Editor at Large, Sky News"Deep Fakes and the Infocalypse is an urgent, thoughtful and thoroughly-researched book that raises uncomfortable questions about the way that information is being distorted by states and individuals... A must-read." - Greg Williams, Editor in Chief of WIRED UK"Essential reading for any one interested about the shocking way information is and will be manipulated." - Lord Edward Vaizey"Schick's Deep Fakes and the Infocalypse is a short, sharp book that hits you like a punch in the stomach." - Nick Cohen, The Observer"Deep Fakes is an uncomfortable but gripping read, probing the way in which the internet has been flooded with disinformation and dark arts propaganda." - Jim Pickard, Chief Political Correspondent, Financial Times"A searing insight into a world so many of us find difficult to understand. I was gripped from the first page." - Iain Dale, Broadcaster"With this powerful book, Nina Schick has done us all a great public service...It's your civic duty to read it." - Jamie Susskind, author of Future Politics"Gripping, alarming and morally vital." - Ian Dunt, Host of Remainiacs PodcastDeep Fakes are coming, and we are not ready. Advanced AI technology is now able to create video of people doing things they never did, in places they have never been, saying things they never said. In the hands of rogue states, terrorists, criminals or crazed individuals, they represent a disturbing new threat to democracy and personal liberty. Deep Fakes can be misused to shift public opinion, swing Presidential elections, or blackmail, coerce, and silence individuals. And when combined with the destabilising overload of disinformation that has been dubbed 'the Infocalypse', we are potentially facing a danger of world-changing proportions.Deep Fakes and the Infocalypse is International Political Technology Advisor Nina Schick's stark warning about a future we all need to understand before it's too late. Deep Fake technology at its most insidious can currently be seen in the BBC drama series The Capture. Trade ReviewNina Schick is alerting us to a danger from the future that is already here. Deepfakes mean that we can't trust our eyes and ears. Listen to this vital warning. -- Adam Boulton * Sky News *Schick's Deep Fakes and the Infocalypse is a short, sharp book that hits you like a punch in the stomach. -- Nick Cohen * The Observer *A searing insight into a world so many of us find difficult to understand. I was gripped from the first page and read the book in one sitting. The lessons I learned from it will stay with me for a long time. * Iain Dale, Broadcaster, LBC *With this powerful book, Nina Schick has done us all a great public service. It's a brilliant guide to the challenges facing our information ecosystem. It's your civic duty to read it. * Jamie Susskind, Author of Future Politics *Deep Fakes is an uncomfortable but gripping read, probing the way in which the internet has been flooded with disinformation and dark arts propaganda dubbed by Schick as "the Infocalypse" - and how that has undermined democracies in the world. The book sketches out an alarming future where convincing fakes will make it even harder for citizens to disentangle truth from lies, and I would advise policy-makers to read it. * Jim Pickard, Chief Political Correspondent, The Financial Times *Gripping, alarming and morally vital. Reading this book is like being ushered into a terrifying new world where nothing can be trusted. Thankfully Nina Schick guides us through it in a reassuringly old-fashioned way: with diligence, breezy storytelling, expert insight and a tried-and-tested commitment to accuracy. * Ian Dunt, Author: Brexit What the Hell Happens Next? *Those concerned with the criminal side of technology will learn from Schick's well-mounted argument. * Kirkus Reviews *Deep Fakes and the Infocalypse is an urgent, thoughtful and thoroughly-researched book that raises uncomfortable questions about the way that information is being distorted by states and individuals. Schick expertly outlines the key challenges that liberal democracies face to ensure the primacy of veracity and trust in public discourse. A must-read for anyone who cares about how the world is represented and an important addition to the discussion around the polarisation of contemporary politics. * Greg Williams, Editor in Chief of WIRED UK *This is essential reading for any one interested about the shocking way information is and will be manipulated - essential not just for policy makers but also CEOs and corporations having to navigate this new landscape. * Lord Edward Vaizey *Schick is a world-leading authority on the ways that warped information flows have broken our shared sense of reality. Deep Fakes is an urgent, illuminating, fast-paced read, making clear that if we break reality, we break democracy too. * Brian Klaas, Author, How to Rig an Election *If you are freaked out by this stuff and you want to see what the background is, and what the future can look like, this is the book for you. I learned a lot from it. * Andrew Yang, Yang Speaks Podcast *Disinformation is no longer a small element of the political landscape. In an era when political campaigns can be based on entirely false versions of reality, it is the political landscape. Schick's book helps explain how this has happened, and warns that it will get worse. * Anne Applebaum, Author, The Twilight of Democracy *This thought-provoking, well-written book finishes with a rallying call: we need to understand, defend and fight back, and Schick tells us how. -- Brian Maye * The Irish Times *

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