Artificial intelligence (AI) Books

Book SynopsisIn alignment with BCS AI Foundation and Essentials certificates, this introductory guide provides the understanding you need to start building artificial intelligence (AI) capability into your organisation. You will learn how AI is being utilised today and how it is likely to be used in the future to balance the talents of humans and machines. You will explore robotics and machine learning within the context of AI, and discover how the challenges AI presents are being addressed.

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Book SynopsisIs Apple conscious? Could a cyber–human system sense a potential terrorist attack? Or make diagnosing a rare and little-known disease routine? Computers are not replacing us: they are enhancing us. Different intelligences are joining together to do things we thought were impossible. Whether it’s devising innovations to tackle climate change, helping job seekers and employers find one another, or identifying the outbreak of a serious disease, groups of humans and machines are already working together to solve all sorts of problems. And they will do a lot more. The future will be like another world – a place where we’ll think differently. In many ways, we are already there.Trade Review‘Malone does a terrific job of explaining how collective intelligence has evolved.’ * City A.M. *‘Superminds is the first book I have seen that deeply explores the power of information technology to enable truly new forms of human organization. I really love the premise and thoughtfulness of the book, and I highly recommend it if you want to understand and make sense of what we are likely to see in the next few years!’ -- Jimmy Wales, Wikipedia founder‘Superminds offers a fascinating deep dive into the science of collective human intelligence, and how communities of minds may ultimately be integrated with AI to produce a new, composite super-intelligence that might soon be leveraged to help solve some of humanity’s most pressing problems.’ -- Martin Ford, author of the bestselling The Rise of the Robots‘From the father of collective intelligence…a refreshingly realistic view of how computers will supercharge collective intelligence and how these superminds can help us tackle the most complex problems that face the world today.’ -- Joi Ito, Director, MIT Media Lab, and author of Whiplash‘Thomas Malone was a decade ahead of most of the rest of us in thinking about the future of work. Now – in this fascinating book – he has done it again, looking ahead to a hyper-connected world and introducing us to new vistas of human capability and creativity achievable through collective intelligence. By thinking imaginatively about our future, Malone helps us think differently about the present.’ -- Anne Marie-Slaughter, CEO of New America and author of Unfinished Business‘Malone takes us on an intentional journey into thinking about thought, intelligence, reasoning, and consciousness. He sees these notions in extremely broad terms that have changed my views of what it means to “think” – a property that emerges from aggregations and organized structures. I may never see a four-legged table the same way again!’ -- Vint Cerf, Vice President and Chief Internet Evangelist, Google‘Forget artificial intelligence. Instead, think collective intelligence, putting “AI in combination with humans who provide whatever skills and general intelligence the machines don’t yet have themselves.” The possibilities are endless. A book rich in speculation about how collective thinking might solve big problems such as climate change; of interests to fans of Daniel Dennett, Steven Pinker, and other big-picture thinkers.’ * Kirkus *‘The story of human civilization has fundamentally been the story of coordination: in families, tribes, markets, nations. The challenge we now face is learning how to collaborate at an unprecedented scale, with both human and nonhuman partners alike – be they institutions, decentralized networks or intelligent machines. Superminds opens a window into what may be the defining question of the coming century.’ -- Brian Christian, author of Algorithms to Live By‘An energizing book, filling the reader with a sense of wonder and hope for the future.’ * Booklist *‘After helping introduce the phrase “Future of Work” in his 2004 book with that name, Tom Malone is back to remind us that the real impact of technology will come not only from AI but also from harnessing human minds at hyperscale. In this well-researched and highly readable book, he explores provocatively and practically the opportunities and challenges that superminds will help us address in businesses and society. Leaders who care about the power of human minds in a world enabled by digital technologies must read this book.’ -- James Manyika, Chairman, McKinsey Global Institute

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Book SynopsisArtificial intelligence is headline news with the launch of the latest ChatGPT and Google Gemini. But when did we start making computers mimic the human mind? And what is the reality of the capabilities of AI now, and in the future?AI has always stirred emotions, and caused great excitement as well as great concern. Since the launch of large language models such as ChatGPT, the scope and capabilities of AI look set to transform our technology, in both good and bad ways. AI can help teach us how to write better or help us generate amazing artworks. But in the wrong hands, AI can create fake images and fake information that can be used to damage our societies. A new addition to the popular Bite-sized Chunks series, this expert-led book will explore how AI has developed from humble beginnings in the 1950s to today’s extraordinary AIs that have more neurons than the human brain. Focusing on specific AIs and their creators over the years, it expl

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Book SynopsisThe rise of artificial intelligence and its countless branches have caused many professional industries to rethink their traditional methods of practice and develop new techniques to keep pace with technological advancement. The continued use of intelligent technologies in the professional world has propelled researchers to contemplate future opportunities and challenges that artificial intelligence may withhold. Significant research is a necessity for understanding future trends of artificial intelligence and the preparation of prospective issues. Analyzing Future Applications of AI, Sensors, and Robotics in Society provides emerging research exploring the potential uses and future challenges of intelligent technological advancements and their impact in education, finance, politics, business, healthcare, and engineering. Featuring coverage on a broad range of topics such as neuronal networks, cognitive computing, and e-health, this book is ideally designed for practitioners, researchers, scientists, executives, strategists, policymakers, academicians, government officials, developers, and students seeking current research on future societal uses of intelligent technology.

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Book SynopsisHumanity has always craved, and feared, information. Alicia Wanless offers a fresh understanding of the relationship between people, technology and knowledge, today and throughout history.

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Book SynopsisLogic and its components (propositional, first-order, non-classical) play a key role in Computer Science and Artificial Intelligence. While a large amount of information exists scattered throughout various media (books, journal articles, webpages, etc.), the diffuse nature of these sources is problematic and logic as a topic benefits from a unified approach. Logic for Computer Science and Artificial Intelligence utilizes this format, surveying the tableaux, resolution, Davis and Putnam methods, logic programming, as well as for example unification and subsumption. For non-classical logics, the translation method is detailed. Logic for Computer Science and Artificial Intelligence is the classroom-tested result of several years of teaching at Grenoble INP (Ensimag). It is conceived to allow self-instruction for a beginner with basic knowledge in Mathematics and Computer Science, but is also highly suitable for use in traditional courses. The reader is guided by clearly motivated concepts, introductions, historical remarks, side notes concerning connections with other disciplines, and numerous exercises, complete with detailed solutions, The title provides the reader with the tools needed to arrive naturally at practical implementations of the concepts and techniques discussed, allowing for the design of algorithms to solve problems.Table of ContentsPreface xi Chapter 1. Introduction 1 1.1. Logic, foundations of computer science, and applications of logic to computer science 1 1.2. On the utility of logic for computer engineers 3 Chapter 2. A Few Thoughts Before the Formalization 7 2.1. What is logic? 7 2.2. Somehistoric landmarks 32 Chapter 3. Propositional Logic 39 3.1. Syntaxand semantics 40 3.2. Themethodof semantic tableaux 54 3.3. Formal systems 64 3.4. Aformal systemforPL(PC) 78 3.5. ThemethodofDavis andPutnam 92 3.6. Semantic trees inPL 96 3.7. The resolutionmethodinPL 101 3.8. Problems, strategies, andstatements 109 3.9. Hornclauses 113 3.10. Algebraic point of view of propositional logic 114 Chapter 4. First-order Terms 121 4.1. Matchingandunification 121 4.2. First-order terms, substitutions, unification 125 Chapter 5. First-Order Logic (FOL) or Predicate Logic (PL1, PC1) 131 5.1. Syntax 133 5.2. Semantics 137 5.3. Semantic tableauxin FOL 154 5.4. Unification in the method of semantic tableaux 166 5.5. Toward a semi-decision procedure for FOL 169 5.6. Semantic trees inFOL 186 5.7. The resolutionmethodinFOL 190 5.8. Adecidable class: themonadic class 202 5.9. Limits: Godel’s (first) incompleteness theorem 206 Chapter 6. Foundations of Logic Programming 213 6.1. Specifications and programming 213 6.2. Toward a logic programming language 219 6.3. Logicprogramming: examples 222 6.4. Computability and Horn clauses 241 Chapter 7. Artificial Intelligence 245 7.1. Intelligent systems: AI 245 7.2. What approaches to studyAI? 249 7.3. Toward an operational definition of intelligence 249 7.4. Can we identify human intelligence with mechanicalintelligence? 251 7.5. Somehistory 254 7.6. Some undisputed themes in AI 256 Chapter 8. Inference 259 8.1. Deductiveinference 260 8.2. An important concept: clause subsumption 266 8.3. Abduction 273 8.4. Inductive inference 278 8.5. Generalization: the generation of inductive hypotheses 284 Chapter 9. Problem Specification in Logical Languages 291 9.1. Equality 291 9.2. Constraints 309 9.3. Second Order Logic (SOL): a few notions 319 Chapter 10. Non-classical Logics 327 10.1. Many-valuedlogics 327 10.2. Inaccurate concepts: fuzzy logic 337 10.3. Modal logics 353 10.4. Some elements of temporal logic 371 Chapter 11. Knowledge and Logic: Some Notions 385 11.1. What is knowledge? 386 11.2. Knowledge and modal logic 389 Chapter 12. Solutions to the Exercises 395 Bibliography 515 Index 517

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Book SynopsisAlthough still in its earliest stages, artificial intelligence (AI) is radically transforming all aspects of society. With the immanent emergence of Artificial Super Intelligence (ASI) and the illusory temptations of `transhumanism’, mankind stands at a crossroads. In Humanity’s Last Stand, Nicanor Perlas makes an urgent plea. It is imperative, he says, that we take immediate steps to ensure that digitized technology is aligned to human values and priorities. Otherwise, ASI will kill the essence of our humanity. Further, if we do not master it now, ASI will transform mankind into its own image. Ultimately, it will destroy the human race. AI experts have not offered a single cogent solution to this existential threat. Rudolf Steiner, however, not only foresaw these developments, but gave clear alternatives. Steiner, the founder of a contemporary, scientific approach to spirituality, provided philosophical, ontological and social innovations to save humanity from the abyss. It is the task of the global anthroposophical movement to pioneer this civilization-saving work: to establish spiritual-scientific ideas in mainstream culture that would allow AI to emerge in a healthier societal context. Perlas gives an overview of the phenomenon of AI, together with its related transhuman concepts of `perfecting humanity’, and outlines the critical internal and external responses required to meet them with consciousness. In particular, he addresses the movement connected to the work of Rudolf Steiner, indicating its all-important tasks: to cooperate with progressive individuals and movements, including scientists and civil society activists; to mobilize its `daughter’ movements for action; and, ultimately, to cooperate with the spiritual powers that have guided and served humanity since the dawn of time. This, says the author, is humanity’s last stand, and failure is not an option.Table of ContentsPreface – Part I BRAVE NEW WORLD OF ARTIFICIAL INTELLIGENCE (AI) – Chapter 1. The World is on Fire! – Chapter 2. Utopia or Extinction? – Chapter 3. Awakening to our True Humanity: The Way Out – Part II PREPARING FOR SPIRITUAL BATTLE – Chapter 4. Spiritual Opponents Fuelling the Potential for Technological Apocalypse – Chapter 5. Anthroposophy: In Defense of the Truly Human – Chapter 6. The Mission and Ways of Evil – Chapter 7. The Two Milestones of the Global Anthroposophical Movement – Part III SELF AND COLLECTIVE MASTERY TO SERVE THE WORLD – Chapter 8. Self Mastery: Preparing Our Self for Spiritual Battle – Chapter 9. Accessing the Support of the Keepers of Humanity – Chapter 10. Collective Human Intelligence (CHI) – PART IV ACTIVATING THE DAUGHTER MOVEMENTS – Chapter 11. Conditions for Decisive Action – Chapter 12. The Strategic Role of the Bio-Dynamic Agriculture Movement – Chapter 13. The Truth-Force of The Youth Movement – Chapter 14. The Original Daughter Movements: Dynamos to Awaken, Nurture and Defend the Truly Human – Chapter 15. The Second-Generation Daughter Movements: Widening the Horizon of Impact – PART V FORMING ALLIANCES WITH OTHER SPIRITUAL MOVEMENTS – Chapter 16. The Second and More Spiritual Scientific Revolution – Chapter 17. Answering the Four Grand Temptations of Artificial Intelligence – Chapter 18. The Sleeping Giant: Global Civil Society 2.0 – Part VI LEARNING FROM FAILURE: THE LAST STAND – Chapter 19. The Wisdom of Failure – Chapter 20. The Last Stand – Chapter 21. The Micha-elic Will and the Future of Humanity – Dedication – Appendix – For More Information – Notes

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Book SynopsisThis edited book explores the many interesting questions that lie at the intersection between AI and HCI. It covers a comprehensive set of perspectives, methods and projects that present the challenges and opportunities that modern AI methods bring to HCI researchers and practitioners. The chapters take a clear departure from traditional HCI methods and leverage data-driven and deep learning methods to tackle HCI problems that were previously challenging or impossible to address.It starts with addressing classic HCI topics, including human behaviour modeling and input, and then dedicates a section to data and tools, two technical pillars of modern AI methods. These chapters exemplify how state-of-the-art deep learning methods infuse new directions and allow researchers to tackle long standing and newly emerging HCI problems alike. Artificial Intelligence for Human Computer Interaction: A Modern Approach concludes with a section on Specific Domains which covers a set of emerging HCI areas where modern AI methods start to show real impact, such as personalized medical, design, and UI automation.Table of ContentsIntroduction.- Part 1: Modeling.- Human performance modeling with deep learning.- Optimal control to support high-level user goals in human-computer interaction.-Modeling UI tappability using deep learning and crowdsourcing.- Part 2: Input.- Eye gaze estimation and its applications.- AI-driven intelligent text correction techniques for mobile text entry.- Deep touch: Sensing press gestures from touch image sequences.- Deep learning-based hand posture recognition for pen interaction enhancement.- Part 3: Data and tools.- An early Rico retrospective: Three years of uses for a mobile app dataset.- Visual intelligence through human interaction.- ML tools for the web: A way for rapid prototyping and HCI research.- Interactive reinforcement learning for autonomous behavior design.- Part 4: Specific domains.- Sketch-based creativity support tools using deep learning.- Generative link: Data-driven computational models for digital ink.- Bridging natural language and graphical user interfaces.- Demonstration + natural language: Multimodal interfaces for GUI-based interactive task learning agents.- Human-centred AI for medical imaging.- 3D spatial sound individualization with perceptual feedback.

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Book SynopsisThis book introduces some fundamentals of information and communication technology (ICT) and other current and future technologies that are relevant to the field of cybersecurity. In a digitally connected world, cybersecurity is one of the most important issues today. We have witnessed tremendous advancements over the last two decades in various fields of networking, connectivity, electronics, and the technologies that make use of those platforms and devices. Many emerging technologies are also showing promise for future use in the cybersecurity area. Hence, it is important to understand some basics of the technologies and concepts that are making their impacts today and those which may show stronger influence in the near future. The book begins with an introduction to ICT and its advancements, then talks about Artificial Intelligence (AI), Machine Learning (ML), and Blockchain Technologies. It then goes on to cover wireless technology, Internet of Things (IoT), Distributed Cloud Computing, Quantum Computing, Virtual Reality, and other futuristic technologies that would be directly related to Cyberspace and Cybersecurity.This textbook is written in a step-by-step manner, with easily accessible information for both general readers and experts in the field. It is suitable to be used as a textbook for undergraduate and graduate courses like Computer Networks and Security, Information Security, etc.Table of ContentsChapter 01 – An Overview of ICT Technology Advancement ..................................................................... 16Introduction ............................................................................................................................................ 17An Overview of ICT Advanced Technologies ........................................................................................... 18Main Areas of ICT Technologies .............................................................................................................. 19Hardware Technologies .......................................................................................................................... 20Data Processing Hardware .................................................................................................................. 20Data Input Hardware .......................................................................................................................... 22Data Output Hardware ....................................................................................................................... 23Data Transmission Hardware .............................................................................................................. 23Data storage hardware ....................................................................................................................... 24Software Technologies ............................................................................................................................ 25Firmware 25Operating Systems (OSs) ..................................................................................................................... 26IT Protocols ......................................................................................................................................... 28Programming Languages ..................................................................................................................... 29Software Development Methodologies .............................................................................................. 30Evolution of Information Technology ..................................................................................................... 31Computer Generations ........................................................................................................................... 32Zero Generation (1642 – 1945) ........................................................................................................... 32First Generation (1945 – 1954) ........................................................................................................... 33Second Generation (1954 – 1963) ...................................................................................................... 33Third Generation (1963 – 1973) .......................................................................................................... 33Fourth Generation (1973 – 1985) ....................................................................................................... 33Fifth Generation (1985 – Present) ...................................................................................................... 33Operating System Generations ............................................................................................................... 34First Generation (1940 – 1950) ........................................................................................................... 34Second Generation (1955 – 1965) ...................................................................................................... 34Third Generation (1965 – 1980) .......................................................................................................... 35Fourth Generation (1980 – Present) ................................................................................................... 35Application Software Generations .......................................................................................................... 35First Generation .................................................................................................................................. 36Second Generation ............................................................................................................................. 36Third Generation ................................................................................................................................. 36Fourth Generation .............................................................................................................................. 37Fifth Generation .................................................................................................................................. 37Programming Language Generations ..................................................................................................... 37First Generation .................................................................................................................................. 38Second Generation ............................................................................................................................. 38Third Generation ................................................................................................................................. 38Fourth Generation .............................................................................................................................. 39Fifth Generation .................................................................................................................................. 39Wireless/Cellular Technology Generations............................................................................................. 39Zero Generation .................................................................................................................................. 40First Generation .................................................................................................................................. 40Second Generation ............................................................................................................................. 40Third Generation ................................................................................................................................. 41Fourth Generation .............................................................................................................................. 41Fifth Generation .................................................................................................................................. 41WWW Generations ................................................................................................................................. 42Web 1.0 42Web 2.0 42Web 3.0 43Web 4.0 43Evolution of Storage Technologies ......................................................................................................... 44Initial Storage Technologies ................................................................................................................ 44Magnetic Tape-Based Technologies ................................................................................................... 44Magnetic Disk-Based Technologies ..................................................................................................... 45Semiconductor-Based Storage Technologies ...................................................................................... 46Optical-Based Storage Technologies ................................................................................................... 48Advanced Storage Technologies ............................................................................................................. 48Direct Attached Storage (DAS) ............................................................................................................ 48Network Attached Storage (NAS) ....................................................................................................... 49Storage Area Network (SAN) ............................................................................................................... 49Futuristic Storage Technologies .............................................................................................................. 49Software Defined Storage (SDS) ......................................................................................................... 50Storage Virtualization ......................................................................................................................... 50Software Development Generations ...................................................................................................... 50Conventional Era – (1960-1970) ......................................................................................................... 51Transition Era – (1980 – 1990) ............................................................................................................ 51Modern Era – (2000 – Present) ........................................................................................................... 51Types of ICT Services ............................................................................................................................... 52Software Development ....................................................................................................................... 52Computer Networking ........................................................................................................................ 52IT Infrastructure Management ........................................................................................................... 52Telecommunication ............................................................................................................................ 53Data Storage Service ........................................................................................................................... 53Storage Transfer Service (STS) ............................................................................................................ 53Database Management ....................................................................................................................... 53Process Automation & Monitoring ..................................................................................................... 54Data Analytics ..................................................................................................................................... 54Cloud Computing Service .................................................................................................................... 55Application Programming Interface (API) Service ............................................................................... 55Cybersecurity Service .......................................................................................................................... 56Digital Entertainment Services ............................................................................................................ 56Content Delivery Network (CDN) ........................................................................................................ 57A Peep into Next Generation Technologies ............................................................................................ 58Chapter 02 – Artificial Intelligence Technology .......................................................................................... 61Introduction ............................................................................................................................................ 62What Is Artificial Intelligence (AI)?.......................................................................................................... 62What Is Neural Network?........................................................................................................................ 63Major Approaches Used in Artificial Intelligence Research .................................................................... 64Symbolic Approach ............................................................................................................................. 64Connectionist Approach ...................................................................................................................... 65Objectives of Artificial Intelligence ......................................................................................................... 66Reasoning ............................................................................................................................................ 67Problem Solving .................................................................................................................................. 67Natural Language Processing .............................................................................................................. 68Learning 68Planning 69Knowledge Representation ................................................................................................................. 69Motion and Manipulation ................................................................................................................... 69Artificial General Intelligence .............................................................................................................. 70Social Intelligence ............................................................................................................................... 70Business Intelligence ........................................................................................................................... 70Machine Perception ............................................................................................................................ 70An Overview of the History of AI ............................................................................................................ 71Main Areas of AI Application .................................................................................................................. 72Natural Language Processing .............................................................................................................. 73Computer Vision ................................................................................................................................. 76Expert Systems .................................................................................................................................... 77Speech Recognition ............................................................................................................................. 78Robotics 79Text Recognition ................................................................................................................................. 80Voice Recognition ............................................................................................................................... 80Voice-to-Text & Text-to-Voice Conversion ......................................................................................... 81Chatbot 82Types of Artificial Intelligence ................................................................................................................. 83Reactive Machines .............................................................................................................................. 84Limited Memory .................................................................................................................................. 84Theory of Mind.................................................................................................................................... 85Self-Awareness .................................................................................................................................... 85Artificial Narrow Intelligence (ANI) ..................................................................................................... 86Artificial General Intelligence (AGI) .................................................................................................... 86Artificial Super Intelligence (ASI)......................................................................................................... 87Intelligent Agent and Environment ......................................................................................................... 87Intelligent (or, Intelligence) Agent (IA) ............................................................................................... 88Artificial Intelligence Environments .................................................................................................... 90Future of Artificial Intelligence ............................................................................................................... 92Chapter 03 – Machine Learning Technology .............................................................................................. 98Introduction to Machine Learning .......................................................................................................... 99Importance of Machine Learning in Modern World ............................................................................. 100How Does Machine Learning Work? ..................................................................................................... 102Types of Machine Learning ................................................................................................................... 103Supervised Machine Learning ............................................................................................................... 103Unsupervised Machine Learning........................................................................................................... 104Semi-Supervised Machine Learning ...................................................................................................... 106Reinforcement Machine Learning ........................................................................................................ 107What Is Deep Machine Learning? ......................................................................................................... 110Artificial Neural Network .................................................................................................................. 110Major Methods/Techniques of Machine Learning ............................................................................... 110Regression Model ............................................................................................................................. 111Decision Trees ................................................................................................................................... 111Clustering .......................................................................................................................................... 111Classification ..................................................................................................................................... 111Anomaly Detection ........................................................................................................................... 111Neural Network Method ................................................................................................................... 112Dimensionality Reduction ................................................................................................................. 112Ensemble Methods ........................................................................................................................... 113Transfer Learning .............................................................................................................................. 113Natural Language Processing (NLP) ...................................................................................................... 113Word Embedding .............................................................................................................................. 115What Is a Machine Learning Algorithm? ............................................................................................... 116Common Categories of Machine Learning Algorithms ......................................................................... 116Classification Algorithms ....................................................................................................................... 117Naïve Bayes ....................................................................................................................................... 117Decision Tree ..................................................................................................................................... 118Random Forest .................................................................................................................................. 119Support Vector Machines ................................................................................................................. 119K Nearest Neighbors ......................................................................................................................... 121Clustering Algorithms ............................................................................................................................ 123K-Means Clustering ........................................................................................................................... 124Expectation Maximization (EM) Algorithm ....................................................................................... 124Agglomerative Hierarchical Clustering .............................................................................................. 124Fuzzy C-Means Algorithm ................................................................................................................. 124Regression Algorithms .......................................................................................................................... 125Linear Regression .............................................................................................................................. 125Multiple linear Regression ................................................................................................................ 126Multivariate Regression .................................................................................................................... 126Logistic Regression ............................................................................................................................ 127Lasso Regression ............................................................................................................................... 127Other Regression algorithms ............................................................................................................ 127What Is AI Training Data? ..................................................................................................................... 128Types of Training Data .......................................................................................................................... 129Text Training Data ............................................................................................................................. 129Audio Training Data .......................................................................................................................... 129Video Training Data ........................................................................................................................... 129Image Training Data .......................................................................................................................... 130Sensory Training Data ....................................................................................................................... 130What Is AI Training Dataset? ................................................................................................................. 130Major Processes Used in Building Training Datasets for AI Training .................................................... 130Data Collection .................................................................................................................................. 131Data Cleaning .................................................................................................................................... 131Data Classification ............................................................................................................................. 131Data Categorization .......................................................................................................................... 131Data Annotation & Labeling .............................................................................................................. 131What are the Major Categories of Data Annotation? ........................................................................... 132Image Data Annotation ......................................................................................................................... 132Bounding Box Annotation ................................................................................................................. 1323D Cuboids Annotation ..................................................................................................................... 133Polygon Annotation .......................................................................................................................... 133Lines & Splines .................................................................................................................................. 134Semantic Segmentation .................................................................................................................... 134Text Data Annotation ............................................................................................................................ 134Entity Annotation .............................................................................................................................. 134Entity Linking ..................................................................................................................................... 134Sentiment Annotation....................................................................................................................... 135Text Classification ............................................................................................................................. 135Audio Data Annotation ......................................................................................................................... 135Sound Labeling .................................................................................................................................. 135Event Tracking ................................................................................................................................... 135Speech to Text Transcription ............................................................................................................ 135Audio Classification ........................................................................................................................... 136Multi-labeling .................................................................................................................................... 136Video Data Annotation ......................................................................................................................... 136Key Points Annotation/Landmarks ................................................................................................... 137Object localization............................................................................................................................. 137Object Tracking ................................................................................................................................. 137Gradient Boosting ............................................................................................................................. 137Top Uses of Machine Learning in Today’s World.................................................................................. 138Big Data 139Data Analytics ................................................................................................................................... 139Cybersecurity .................................................................................................................................... 139Digital Marketing............................................................................................................................... 140Business Intelligence ......................................................................................................................... 140Process Automation .......................................................................................................................... 141Automobiles ...................................................................................................................................... 141e-Commerce ...................................................................................................................................... 142Impact of Machine Learning on Cybersecurity ..................................................................................... 142Positive Impact .................................................................................................................................. 142Negative Impact ................................................................................................................................ 143Chapter 04 – Blockchain Technology ........................................................................................................ 145Introduction to Blockchain Technology ................................................................................................ 146Top Features of Blockchain Technology ........................................................................................... 147History of Blockchain Technology ......................................................................................................... 149Major Terms Used in Blockchain Technology ....................................................................................... 150Cryptographic Hash ........................................................................................................................... 150Transaction ........................................................................................................................................ 151Proof of Work .................................................................................................................................... 151Block 152Mining 152Timestamp ........................................................................................................................................ 153Stack of Technologies Forming Blockchain ........................................................................................... 153Cryptographic Keys ........................................................................................................................... 153Peer-to-Peer Network with Shared Ledger ....................................................................................... 154Computing Resources to Store Transactions & Network Records.................................................... 155How Does Blockchain Technology Work? ............................................................................................. 155Node 155Block 155What Is Distributed Ledger Technology (DLT)? .................................................................................... 156Types of Blockchain Technology ........................................................................................................... 156Public Blockchain .............................................................................................................................. 158Private Blockchain ............................................................................................................................. 159Consortium Blockchain ..................................................................................................................... 159Hybrid Blockchains ............................................................................................................................ 159Typical Uses of Blockchain Technology ................................................................................................. 160Cryptocurrency.................................................................................................................................. 160Non-Fungible Token (NFT) ................................................................................................................ 161Smart Contracts ................................................................................................................................ 161Financial Markets .............................................................................................................................. 162Electronic Voting ............................................................................................................................... 162Record Maintenance ......................................................................................................................... 163Supply Chain ...................................................................................................................................... 163Government ...................................................................................................................................... 163Impact of Blockchain Technology on Cybersecurity ............................................................................. 164Chapter 05 – 5th Generation Wireless Technology ................................................................................... 168An Introduction to 5G Technology ........................................................................................................ 169Importance of 5G Technology .............................................................................................................. 170Evolution of Cellular Networks ............................................................................................................. 172First Generation (1G) ........................................................................................................................ 172Second Generation (2G) .................................................................................................................... 172Third Generation (3G) ....................................................................................................................... 173Fourth Generation (4G) ..................................................................................................................... 173Fifth Generation (5G) ........................................................................................................................ 174Sixth Generation (6G)........................................................................................................................ 174Key Features and Capabilities of 5G Technology .................................................................................. 174Architecture of 5G Network .................................................................................................................. 176Top Protocols Used in 5G Networks ..................................................................................................... 1793GPP 179New Radio (NR) ................................................................................................................................. 180NextGen Core .................................................................................................................................... 181LTE Advanced Pro .............................................................................................................................. 182EPC Evolution .................................................................................................................................... 183Impact of 5G Technology on Cybersecurity .......................................................................................... 183Chapter 06 – Internet of Things (IoT) ........................................................................................................ 188Introduction to Internet of Things (IoT) ................................................................................................ 189Importance of IoT.............................................................................................................................. 189Main Features of Internet of Things ................................................................................................. 190History of Internet of Things ................................................................................................................. 190What Is Ambient Intelligence in IoT? .................................................................................................... 191Autonomous Control in IoT ................................................................................................................... 191Range of Enabling Technologies Behind Internet of Things ................................................................. 191Low Power Sensors ........................................................................................................................... 192Cloud Computing .............................................................................................................................. 192Artificial Intelligence (AI) ................................................................................................................... 192Machine Learning .............................................................................................................................. 192Data Analytics ................................................................................................................................... 192Big Data 193Short Range Wireless Technologies .................................................................................................. 193Medium & Long-Range Wireless Technologies ................................................................................ 193Effective Communication Protocols .................................................................................................. 193Internet Protocol V6 ......................................................................................................................... 194Architecture of Internet of Things Ecosystem ...................................................................................... 194Three Layer Architecture .................................................................................................................. 194Four Layer Architecture .................................................................................................................... 194Five Layer Architecture ..................................................................................................................... 195What Is Decentralized Internet of Things Concept? ............................................................................. 195What Is Industrial Internet of Things? .................................................................................................. 196Industrial Internet of Things Standard Bodies ...................................................................................... 196Important Industrial Internet of Things IIoT Platforms......................................................................... 197Azure IoT 197Oracle IoT Cloud ................................................................................................................................ 198IBM Watson IoT................................................................................................................................. 198AWS IoT 198Siemens Mind Sphere ....................................................................................................................... 198Flutura Cerebra ................................................................................................................................. 198Thing Worx ........................................................................................................................................ 199GE Predix 199IIoT Use Cases in Different Industries ................................................................................................... 199Smart Cities ....................................................................................................................................... 200Smart Home ...................................................................................................................................... 200Manufacturing .................................................................................................................................. 200Process Automation .......................................................................................................................... 200Energy Management ......................................................................................................................... 200Supply Chain ...................................................................................................................................... 201Healthcare ......................................................................................................................................... 201Agriculture ........................................................................................................................................ 201Military 201Transportation .................................................................................................................................. 201Challenges Posed by Internet of Things ................................................................................................ 202Cybersecurity .................................................................................................................................... 202Privacy 202Complex Operations & Management ............................................................................................... 202Environment Impact ......................................................................................................................... 203Bulky Data ......................................................................................................................................... 203Impact of IoT on Cybersecurity ............................................................................................................. 203Chapter 07 – Distributed Cloud Computing .............................................................................................. 206An Introduction to Distributed Cloud Computing ................................................................................. 207What Is Edge Computing? ..................................................................................................................... 208Advantages of Distributed Cloud .......................................................................................................... 209Working Principle of Distributed Cloud ................................................................................................ 210Distributed Cloud Architecture ............................................................................................................. 210Top Use Cases of Distributed Cloud in Industries ................................................................................. 211Content Delivery Network (CDN) ...................................................................................................... 212Internet of Things (IoT) & Edge ......................................................................................................... 214Software Defined Infrastructure (SDI) .............................................................................................. 214Big Data Processing ........................................................................................................................... 215Multi-Cloud Unification ..................................................................................................................... 215Centralized Management ................................................................................................................. 216Challenges of Distributed Cloud Computing ......................................................................................... 216Impact of Distributed Cloud Computing on Cybersecurity ................................................................... 217Chapter 08 – Quantum Computing ........................................................................................................... 220An Introduction to Quantum Computing.............................................................................................. 221Salient Features of Quantum Computing ............................................................................................. 222Short History of Quantum Computing .................................................................................................. 223What Is Quantum Physics? ................................................................................................................... 224Theory of Quantum Computing ........................................................................................................ 224Working Principle of Quantum Computing........................................................................................... 225How Many States Are Used in Quantum Computing? .......................................................................... 225What Are Superimposition and Entanglement in Quantum Computing? ............................................ 225Difference Between Traditional Computing & Quantum Computing .................................................. 226Real-World Quantum Applications ....................................................................................................... 227Major Projects on Quantum Computing ............................................................................................... 229IBM 230Honeywell ......................................................................................................................................... 230Google 230Microsoft 231Main Terminologies Used in Quantum Computing .............................................................................. 232Superconductors ............................................................................................................................... 232Superfluid .......................................................................................................................................... 233Quantum Mechanics ......................................................................................................................... 234Qubits 234Quantum Logic Gate ......................................................................................................................... 235Quantum Counting ............................................................................................................................ 236Grover’s Algorithm ............................................................................................................................ 236Shor’s Algorithm ............................................................................................................................... 236Josephson Junction ........................................................................................................................... 237Chapter 09 – Tactile Virtual Reality .......................................................................................................... 240An Introduction to Tactile Virtual Reality ............................................................................................. 241Augmented Reality and Virtual Reality ................................................................................................. 242History & Evolution of Tactile Virtual Reality ........................................................................................ 242Types of Virtual Reality ......................................................................................................................... 244Non-Immersive VR ............................................................................................................................ 244Fully-Immersive VR ........................................................................................................................... 244Semi-Immersive VR ........................................................................................................................... 245Neurophysiological Tactile Measurement Techniques ......................................................................... 245Electroencephalography (EEG) ......................................................................................................... 246Magnetoencephalography (MEG)..................................................................................................... 246Functional Magnetic Resonance Imaging (fMRI) .............................................................................. 246Somatosensation and Its Types ............................................................................................................ 246Active Somatosensation.................................................................................................................... 247Passive Somatosensation .................................................................................................................. 247Major VR Terms with Definitions .......................................................................................................... 247Head Mounted Display (HMD) .......................................................................................................... 247Haptics 247360 Videos ......................................................................................................................................... 247Interactive VR .................................................................................................................................... 248Stereoscopy ....................................................................................................................................... 2484D Virtual Reality .............................................................................................................................. 248Field of View (FOV) ............................................................................................................................ 248Image/Video Stitching ....................................................................................................................... 248Simulator Sickness ............................................................................................................................ 248Cave Automatic Virtual Environment ............................................................................................... 249Mixed Reality .................................................................................................................................... 249Real-Word Applications of Tactile Virtual Reality ................................................................................. 249Video Games ..................................................................................................................................... 250Education & Training ......................................................................................................................... 251Product Development ....................................................................................................................... 252Chapter 10 – An Overview of Top Futuristic Technologies ....................................................................... 255What Is Futuristic Technology? ............................................................................................................. 256Top Futuristic Technologies .................................................................................................................. 2583D Printing Technology ..................................................................................................................... 2594D Printing ........................................................................................................................................ 2636G Technology .................................................................................................................................. 263Autonomous Robots ......................................................................................................................... 267Artificial Neurons .............................................................................................................................. 271Artificial General Intelligence (AGI) .................................................................................................. 273Artificial Super Intelligence (ASI)....................................................................................................... 274Mind Uploading................................................................................................................................. 276Driverless Vehicles ............................................................................................................................ 278Infrastructure Hacking ...................................................................................................................... 279Regenerative Medicine ..................................................................................................................... 279Digital Twin (DT) Technology ............................................................................................................ 280Programmable Living Robots ............................................................................................................ 282Human Augmentation ....................................................................................................................... 283Intelligent Process Automation (IPA) ................................................................................................ 283Space Elevator ................................................................................................................................... 284Rotating Skyhook .............................................................................................................................. 285Light Sail 285Chapter 11 – Impact of Advanced & Futuristic Technologies on Cybersecurity ...................................... 288Overview of Impact of Modern Technologies on Cybersecurity .......................................................... 288Major Cybersecurity Challenges Due to Advanced Technologies ........................................................ 291Risk to National Security ................................................................................................................... 292Breach of Privacy ............................................................................................................................... 293Increased Burden of Cybersecurity on Businesses ........................................................................... 294Shortage of Cybersecurity Specialists ............................................................................................... 294Risk of Extensive Data Exposure ....................................................................................................... 295Society & Business Manipulation ...................................................................................................... 295References ................................................................................................................................................ 298

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Book SynopsisIntroduction to Generative AI.- Evolution of Neural Networks to Large Language Models.- LLMs and Transformers.- The ChatGPT Architecture: An In-Depth Exploration of OpenAI.- Google Bard and Beyond.- Diffusion Model and Generative AI for Images.- Setting Up the Environment and Implementing LLMs.- ChatGPT Use Cases.

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Book SynopsisThis book proposes a comprehensive overview of the state-of-the-art research work on multimedia analysis in IoT applications. This is a third volume by editors which provides theoretical and practical approach in the area of multimedia and IOT applications and performance analysis. Further, multimedia communication, deep learning models to multimedia data, and the new (IOT) approaches are also covered. It addresses the complete functional framework in the area of multimedia data, IoT, and smart computing techniques. It bridges the gap between multimedia concepts and solutions by providing the current IOT frameworks, their applications in multimedia analysis, the strengths and limitations of the existing methods, and the future directions in multimedia IOT analytics.Table of ContentsQuantum Blockchain Approach for Security Enhancement in Cyber World.- Quantum Computing for Healthcare: A review on Implementation Trends and Recent Advances.- Towards Task Scheduling Approaches to Reduce Energy Consumption in Cloud Computing Environment.- An Efficient Data Transferring through Li-Fi Technology: A Smart Home Appliance.- Modeling of Fuzzy Logic Based Classification System Using the Gravitational Search Algorithm.- Big Data Based Image Handling – A Review of Implementation using Amazon Web Services.- Real Time System for Forecasting Natural Disasters using the Social Network.- Call Based Smart Transportation using Artificial Intelligence.- Design Issues for Developing Routing Protocols for Flying Adhoc Network.- Online Stream Processing and Multimedia Oriented IoT: Tools for Sustainable Development of Smart Cities.- Big Data Analytics and Data Mining for Healthcare Informatics (HCI).- Integration of Quantum Computing and Blockchain Technology: A Cryptographic Perspective.

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Book SynopsisARTIFICE is set in a near-future Singapore and takes on the challenge of what truly sentient AI might mean for humanity. It’s speculative fiction in the mould of Ishiguro’s Klara and the Sun or Le Tellier’s The Anomaly. This novel would strike a chord given ongoing uncertainty and anxiety about the role of AI. Humanity’s greatest invention could be our last. Archie’s involvement in the artificial intelligence project known as Janus was limited to routine diagnostics. But when she discovers that she and everyone else has been deceived by their creation, it launches her on a journey that will change her life — and humanity’s future.

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Book SynopsisThis book presents state-of-the-art blockchain and AI advances in health care. Healthcare service is increasingly creating the scope for blockchain and AI applications to enter the biomedical and healthcare world. Today, blockchain, AI, ML, and deep learning are affecting every domain. Through its cutting-edge applications, AI and ML are helping transform the healthcare industry for the better. Blockchain is a decentralization communication platform that has the potential to decentralize the way we store data and manage information. Blockchain technology has potential to reduce the role of middleman, one of the most important regulatory actors in our society. Transactions are simultaneously secure and trustworthy due to the use of cryptographic principles. In recent years, blockchain technology has become very trendy and has penetrated different domains, mostly due to the popularity of cryptocurrencies. One field where blockchain technology has tremendous potential is health care, due to the need for a more patient-centric approach in healthcare systems to connect disparate systems and to increase the accuracy of electronic healthcare records (EHRs).Table of ContentsMachine Learning for Drug Discovery and Manufacturing.- Knowledge Strategies Influencing on The Epidemiologists Performance of The Qeshm Island’s Health Centers.- Healthcare: In the Era of Blockchain.- Securing Healthcare records using Blockchain: Applications and Challenges.- Authentication Schemes For Healthcare Data Using Emerging Computing Technologies.- Biomedical data classification using fuzzy clustering.- Applications of Machine Learning in healthcare With a Case Study of Lung Cancer Detection Through Deep Learning Approach.- Fetal Health Status Prediction During Labor and Delivery Based on Cardiotocogram Data using Machine and Deep Learning.- Blockchain and AI: Disruptive Digital Technologies in Designing the Potential Growth of Healthcare Industries.- Recommendation Systems for Cancer Prognosis, Treatment and Wellness.- A Real Time Data Mining-Based Cancer Disease Classification Using KEGG Gene Dataset.- SOLUTION ARCHITECTING ON REMOTE MEDICAL MONITORING WITH AWS CLOA Domain Oriented Framework for Prediction of Diabetes Disease and Classification of Diet using Machine Learning TechniquesUD AND IOT.- An Accurate Swine Flu Prediction and Early Prediction Using Data Mining Technique

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Book SynopsisSometime in the future the intelligence of machines will exceed that of human brain power. So are we on the edge of an AI-pocalypse, with superintelligent devices superseding humanity, as predicted by Stephen Hawking? Or will this herald a kind of Utopia, with machines doing a far better job at complex tasks than us? You might not realise it, but you interact with AIs every day. They route your phone calls, approve your credit card transactions and help your doctor interpret results. Driverless cars will soon be on the roads with a decision-making computer in charge. But how do machines actually think and learn? In Machines That Think, AI experts and New Scientist explore how artificial intelligence helps us understand human intelligence, machines that compose music and write stories - and ask if AI is really a threat.ABOUT THE SERIESNew Scientist Instant Expert books are definitive and accessible entry points to the most important subjects in science; subjects that challenge, attract debate, invite controversy and engage the most enquiring minds. Designed for curious readers who want to know how things work and why, the Instant Expert series explores the topics that really matter and their impact on individuals, society, and the planet, translating the scientific complexities around us into language that's open to everyone, and putting new ideas and discoveries into perspective and context.

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Book SynopsisCognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. The integration of deep learning improves the performance of Cognitive computing systems in many applications, helping in utilizing heterogeneous data sets and generating meaningful insights.

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Book SynopsisArtificial intelligence is emerging as a game-changer in the business world, with impacts across all sectors. AI allows business to process massive amounts of data instantaneously, and to scale solutions at almost zero marginal cost, forcing companies to adapt and reimagine their business and operations. The Rise of AI-Powered Companies examines some of the most successful examples of companies using artificial intelligence to their advantage. From AI-enabled countries across the globe that stayed resilient and strong in the face of COVID-19, to Business-to-Consumer businesses that transformed their product development processes thanks to unprecedented amounts of consumer data, increasing their revenues manifold along the way. The book then delves into the critical enablers to becoming AI-powered and the critical steps to activate and integrate them within business organizations. Starting with data strategy, it examines new forms of data sharing and how companies should think about governance and privacy risks. It then focuses on human–AI collaboration and its role in building a stronger team culture. Finally, "Responsible AI" is discussed as well as the impact of AI-powered businesses on society at large. AI-powered companies will become the norm in the years to come. By unpacking and showcasing the major steps of a successful AI transformation, this book will help guide organizations in making the critical leap to become AI-powered—essential to survive and remain competitive in the near future.

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Book SynopsisProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.Trade ReviewFrom the reviews: "This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group." John Maindonald for the Journal of Statistical Software "In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … This book will serve as an excellent reference. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields." (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007) "This book appears in the Information Science and Statistics Series commissioned by the publishers. … The book appears to have been designed for course teaching, but obviously contains material that readers interested in self-study can use. It is certainly structured for easy use. … For course teachers there is ample backing which includes some 400 exercises. … it does contain important material which can be easily followed without the reader being confined to a pre-determined course of study." (W. R. Howard, Kybernetes, Vol. 36 (2), 2007) "Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra … . Summing Up: Highly recommended. Upper-division undergraduates through professionals." (C. Tappert, CHOICE, Vol. 44 (9), May, 2007) "The book is structured into 14 main parts and 5 appendices. … The book is aimed at PhD students, researchers and practitioners. It is well-suited for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bio-informatics. Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the book’s web site … ." (Ingmar Randvee, Zentralblatt MATH, Vol. 1107 (9), 2007) "This new textbook by C. M. Bishop is a brilliant extension of his former book ‘Neural Networks for Pattern Recognition’. It is written for graduate students or scientists doing interdisciplinary work in related fields. … In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the sense of parameter estimation). A large number of very instructive illustrations adds to this value." (H. G. Feichtinger, Monatshefte für Mathematik, Vol. 151 (3), 2007) "Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. … Pattern Recognition and Machine Learning provides excellent intuitive descriptions and appropriate-level technical details on modern pattern recognition and machine learning. It can be used to teach a course or for self-study, as well as for a reference. … I strongly recommend it for the intended audience and note that Neal (2007) also has given this text a strong review to complement its strong sales record." (Thomas Burr, Journal of the American Statistical Association, Vol. 103 (482), June, 2008) "This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning. It presents a unified treatment of well-known statistical pattern recognition techniques. … The book can be used by advanced undergraduates and graduate students … . The illustrative examples and exercises proposed at the end of each chapter are welcome … . The book, which provides several new views, developments and results, is appropriate for both researchers and students who work in machine learning … ." (L. State, ACM Computing Reviews, October, 2008) "Chris Bishop’s … technical exposition that is at once lucid and mathematically rigorous. … In more than 700 pages of clear, copiously illustrated text, he develops a common statistical framework that encompasses … machine learning. … it is a textbook, with a wide range of exercises, instructions to tutors on where to go for full solutions, and the color illustrations that have become obligatory in undergraduate texts. … its clarity and comprehensiveness will make it a favorite desktop companion for practicing data analysts." (H. Van Dyke Parunak, ACM Computing Reviews, Vol. 49 (3), March, 2008)Table of ContentsProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

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Book SynopsisOmar Santos is a cybersecurity thought leader with a passion for driving industry-wide initiatives to enhance the security of critical infrastructures. Omar is the lead of the DEF CON Red Team Village, chair of the Common Security Advisory Framework (CSAF) technical committee, and board member of the OASIS Open standards organization. Omar's collaborative efforts extend to numerous organizations, including the Forum of Incident Response and Security Teams (FIRST) and the Industry Consortium for Advancement of Security on the Internet (ICASI). Omar is a renowned expert in ethical hacking, vulnerability research, and incident response. He employs his deep understanding of these disciplines to help organizations stay ahead of emerging threats. His dedication to cybersecurity has made a significant impact on businesses, academic institutions, law enforcement agencies, and other entities striving to bolster their security measures. With over twenty books, video courses, Trade Review“As AI promises to revolutionize many aspects of work and society, there is a critical need for IT professionals to understand how AI can help them in practice. This book provides a highly accessible overview of how new and emerging AI capabilities can improve many key areas of IT. The authors were early industry pioneers in applying AI to improve networking, cybersecurity, and the design & operation of AI-based systems for large enterprises -- and their real-world AI experience is clearly shown throughout the book.”--John Apostolopoulos, Area Tech Lead Communication & Collaboration, Google, formerly VP/CTO Enterprise Networking Business, CiscoTable of ContentsPreface xix Chapter 1. Introducing the Age of AI: Emergence, Growth, and Impact on Technology 1 The End of Human Civilization 2 Significant Milestones in AI Development (This Book Is Already Obsolete) 2 The AI Black Box Problem and Explainable AI 5 What's the Difference Between Large Language Models and Traditional Machine Learning? 6 Hugging Face Hub: A Game-Changer in Collaborative Machine Learning 12 AI's Expansion Across Industries: Networking, Cloud Computing, Security, Collaboration, and IoT 14 AI's Impacts on the Job Market 15 AI's Impacts on Security, Ethics, and Privacy 17 Summary 30 References 31 Chapter 2. Connected Intelligence: AI in Computer Networking 33 The Role of AI in Computer Networking 34 AI for Network Management 37 AI for Network Optimization 45 AI for Network Security 49 AI for Traffic Classification and Prediction 52 AI in Network Digital Twins 54 Summary 55 References 56 Chapter 3. Securing the Digital Frontier: AI's Role in Cybersecurity 59 AI in Incident Response: Analyzing Potential Indicators to Determine the Type of Attack 59 AI in Vulnerability Management and Vulnerability Prioritization 71 AI in Security Governance, Policies, Processes, and Procedures 73 Using AI to Create Secure Network Designs 74 AI and Security Implications of IoT, OT, Embedded, and Specialized Systems 75 AI and Physical Security 76 AI in Security Assessments, Red Teaming, and Penetration Testing 77 AI in Identity and Account Management 80 Using AI for Fraud Detection and Prevention 86 AI and Cryptography 87 AI in Secure Application Development, Deployment, and Automation 90 Summary 93 References 94 Chapter 4. AI and Collaboration Building Bridges, Not Walls 95 Collaboration Tools and the Future of Work 96 AI for Collaboration 101 The Contact Center: A Bridge to Customers 109 AR/VR: A Closer Look 113 Affective Computing 116 Summary 116 References 117 Chapter 5. AI in the Internet of Things (AIoT) 119 Understanding the IoT Landscape 120 AI for Data Analytics and Decision Making 122 AI for IoT Resource Optimization 125 AI for IoT in Supply Chain 127 AI for IoT Security 130 AI for IoT in Sustainability 133 Summary 137 References 137 Chapter 6. Revolutionizing Cloud Computing with AI 139 Understanding the Cloud Computing Environment 139 AI in Cloud Infrastructure Management 145 AI for Cloud Security 147 AI for Cloud Optimization 151 AI and Machine Learning as a Service 153 Challenges of AI and Machine Learning in the Cloud 158 What Lies Ahead 158 References 159 Chapter 7. Impact of AI in Other Emerging Technologies 161 Executive Order on the Development and Use of Artificial Intelligence 162 AI in Quantum Computing 163 How AI Can Revolutionize Quantum Hardware Optimization 167 Data Analysis and Interpretation 168 AI in Blockchain Technologies 169 AI in Autonomous Vehicles and Drones 175 AI in Edge Computing 175 Summary 183 References 184 Index 185

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Book SynopsisPrologue – How to Read This Book.- Chapter 1 - The Value Compass - Navigating the AI Hype.- Chapter 2 - The GenAI Idea Maze - Finding Gold Among the Glitter.- Chapter 3 - Proof or Dare - Reimagining POCs in the GenAI Playground.- Chapter 4 - Code to Scale - Engineering GenAI for Production.- Chapter 5 - Guardrails - Building Habits for Governance, Security and Ethics.- Chapter 6 - The Era of AI agents - Understanding the Next Wave of AI.

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Book SynopsisCovid-19 has shown us the importance of mathematical and statistical models to interpret reality, provide forecasts, and explore future scenarios. Algorithms, artificial neural networks, and machine learning help us discover the opportunities and pitfalls of a world governed by mathematics and artificial intelligence.Trade Review“Alfio Quarteroni invites us to the stage of contemporary science and technology in which multidisciplinarity and transferability are combined to contribute to the construction of the wisdom of life … . A great master. Its access does not present difficulties beyond the decision to satisfy an intellectual and spiritual curiosity with a future edge: a book to be read with ease and understood with great quality.” (Melio Sáenz, ResearchGate, researchgate.net, June, 2023)Table of Contents1 Epidemic.- 2 Retrospective.- 3 Interlude: the revolution that did not happen and the revolution that was unforeseen.- 4 Artificial intelligence, learning computers, artificial neural networks.- 5 A bit of maths (behind artificial intelligence and machine learning).- 6 BIG DATA - BIG BROTHER (or, on the ethical and moral aspects of artificial intelligence).

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Book SynopsisAI is ready for business. Are you ready for AI?From financial modeling and product design to performance management and hiring decisions, AI and machine learning are becoming everyday tools for managers at businesses of all sizes. But AI systems come with benefits and downsides—and if you can't make sense of them, you're not going to make the right decisions.Whether you need to get up to speed quickly or need a refresher, or you're working with an AI expert for the first time, the HBR Guide to AI Basics for Managers will give you the information and skills you need to succeed.You'll learn how to: Understand key AI terms and concepts Recognize which of your projects would benefit from AI Work more effectively with your data team Hire the right AI vendors and consultants Deal with ethical risks before they arise Scale AI across your organization Arm yourself with the advice you need to succeed on the job, with the most trusted brand in business. Packed with how-to essentials from leading experts, the HBR Guides provide smart answers to your most pressing work challenges.

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Book SynopsisAI has unparalleled transformative potential to reshape society but without legal scrutiny, international oversight and public debate, we are sleepwalking into a future written by algorithms which encode regressive biases into our daily lives. As governments and corporations worldwide embrace AI technologies in pursuit of efficiency and profit, we are at risk of losing our common humanity: an attack that is as insidious as it is pervasive. Leading privacy expert Ivana Bartoletti exposes the reality behind the AI revolution, from the low-paid workers who train algorithms to recognise cancerous polyps, to the rise of data violence and the symbiotic relationship between AI and right-wing populism. Impassioned and timely, An Artificial Revolution is an essential primer to understand the intersection of technology and geopolitical forces shaping the future of civilisation, and the political response that will be required to ensure the protection of democracy and human rights.Trade ReviewReview: An Artificial Revolution ‘This is a great read, giving you enough information to perhaps inspire you to look into this further, or to just consider where your data is held and what it is being used for.’ http://independentbookreviews.co.uk/book/an-artificial-revolution/ -- Fiona Sharp * Independent Book Reviews *‘Books of the Year 2020’ ‘A great book for those interested in AI and power-dynamics.’ https://burleyfisherbooks.com/blogs/news/books-of-the-year-2020 -- Enya Nolan * Burley Fisher Books *‘An Interview with Ivana Bertoletti, Technical Director at Deloitte.’ ‘We cannot leave AI and its future to the technologists. AI is about power, and this is the time to ensure that power benefits us all. I wrote An Artificial Revolution because I wanted people to talk about AI at the kitchen table.’ https://www.trustinsoda.com/blog/an-interview-with-ivana-bartoletti-technical-director-at-deloitte--253495/ -- Alfie Rice * SODA *‘Modern democracy: Data, surveillance creep and more authoritarian regimes?’ ‘What are governments and corporations doing with the data they are collecting, and what is the ultimate end goal? As Ivana Bartoletti states in her recent book An Artificial Revolution On Power, Politics and AI, “Data is not neutral, and the fact that we collect a huge amount of it brings many challenges – not just from the standpoint of privacy but also from the standpoint of power dynamics”.’ https://www.orfonline.org/expert-speak/modern-democracy-data-surveillance-creep-and-more-authoritarian-regimes/ -- Oriana Medicott * Observer Research Foundation *

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Book SynopsisProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.Trade ReviewFrom the reviews: "This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group." John Maindonald for the Journal of Statistical Software "In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … This book will serve as an excellent reference. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields." (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007) "This book appears in the Information Science and Statistics Series commissioned by the publishers. … The book appears to have been designed for course teaching, but obviously contains material that readers interested in self-study can use. It is certainly structured for easy use. … For course teachers there is ample backing which includes some 400 exercises. … it does contain important material which can be easily followed without the reader being confined to a pre-determined course of study." (W. R. Howard, Kybernetes, Vol. 36 (2), 2007) "Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra … . Summing Up: Highly recommended. Upper-division undergraduates through professionals." (C. Tappert, CHOICE, Vol. 44 (9), May, 2007) "The book is structured into 14 main parts and 5 appendices. … The book is aimed at PhD students, researchers and practitioners. It is well-suited for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bio-informatics. Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the book’s web site … ." (Ingmar Randvee, Zentralblatt MATH, Vol. 1107 (9), 2007) "This new textbook by C. M. Bishop is a brilliant extension of his former book ‘Neural Networks for Pattern Recognition’. It is written for graduate students or scientists doing interdisciplinary work in related fields. … In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the sense of parameter estimation). A large number of very instructive illustrations adds to this value." (H. G. Feichtinger, Monatshefte für Mathematik, Vol. 151 (3), 2007) "Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. … Pattern Recognition and Machine Learning provides excellent intuitive descriptions and appropriate-level technical details on modern pattern recognition and machine learning. It can be used to teach a course or for self-study, as well as for a reference. … I strongly recommend it for the intended audience and note that Neal (2007) also has given this text a strong review to complement its strong sales record." (Thomas Burr, Journal of the American Statistical Association, Vol. 103 (482), June, 2008) "This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning. It presents a unified treatment of well-known statistical pattern recognition techniques. … The book can be used by advanced undergraduates and graduate students … . The illustrative examples and exercises proposed at the end of each chapter are welcome … . The book, which provides several new views, developments and results, is appropriate for both researchers and students who work in machine learning … ." (L. State, ACM Computing Reviews, October, 2008) "Chris Bishop’s … technical exposition that is at once lucid and mathematically rigorous. … In more than 700 pages of clear, copiously illustrated text, he develops a common statistical framework that encompasses … machine learning. … it is a textbook, with a wide range of exercises, instructions to tutors on where to go for full solutions, and the color illustrations that have become obligatory in undergraduate texts. … its clarity and comprehensiveness will make it a favorite desktop companion for practicing data analysts." (H. Van Dyke Parunak, ACM Computing Reviews, Vol. 49 (3), March, 2008)Table of ContentsProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

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Book SynopsisExplores how the impact of code on life has become comparable to that of speech and writing: as language and code have grown entangled, the lines that once separated humans from machines, analog from digital, and old technologies from new ones have become blurred. The book gives us the tools necessary to make sense of these complex relationships.Trade Review"A deeply insightful and significant investigation of how the science and rhetorics of cybernetics have reshaped the boundaries of human identity." - Village Voice "In her important new book, N. Katherine Hayles... traces the evolution over the last half-century of a radical reconception of what it means to be human and, indeed, even of what it means to be alive, a reconception unleashed by the interplay of humans and intelligent machines." - Chicago Tribune"

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Book SynopsisDr. Qinghua Lu is a principal research scientist and leads the Responsible AI science team at CSIRO's Data61. She received her PhD from University of New South Wales in 2013. Her current research interests include responsible AI, software engineering for AI/GAI, and software architecture. She has published 150+ papers in premier international journals and conferences. Her recent paper titled Towards a Roadmap on Software Engineering for Responsible AI received the ACM Distinguished Paper Award. Dr. Lu is part of the OECD.AI's trustworthy AI metrics project team. She also serves a member of Australia's National AI Centre Responsible AI at Scale think tank. She is the winner of the 2023 APAC Women in AI Trailblazer Award.   Dr./Prof. Liming Zhu is a Research Director at CSIRO's Data61 and a conjoint full professor at the University of New South Wales (UNSW). He is the chairperson of Standards Australia's blockchain committee and conTable of Contents Preface.. . . . . . . . . . . . . . . . . xv About the Author.. . . . . . . . . . . . . . xix Part I Background and Introduction. . . . . . . . . . . . .1 1 Introduction to Responsible AI. . . . . . . . . 3 What Is Responsible AI?. . . . . . . . . . . . 4 What Is AI?. . . . . . . . . . . . . . 6 Developing AI Responsibly: Who Is Responsible for Putting the “Responsible” into AI?.. . . . . . . . . . . . 8 About This Book.. . . . . . . . . . . . . 9 How to Read This Book.. . . . . . . . . . . . 11 2 Operationalizing Responsible AI: A Thought Experiment—Robbie the Robot.. . . . . . . . 13 A Thought Experiment—Robbie the Robot.. . . . . . . . 13 Summary. . . . . . . . . . . . . . 22 Part II Responsible AI Pattern Catalogue. . . . . . . . . . .  23 3 Overview of the Responsible AI Pattern Catalogue. . . . . 25 The Key Concepts.. . . . . . . . . . . . . 25 Why Is Responsible AI Different?. . . . . . . . . . 30 A Pattern-Oriented Approach for Responsible AI.. . . . . . . 32 4 Multi-Level Governance Patterns for Responsible AI.. . . . 39 Industry-Level Governance Patterns. . . . . . . . . 42 Organization-Level Governance Patterns.. . . . . . . . 56 Team-Level Governance Patterns.. . . . . . . . . . 72 Summary. . . . . . . . . . . . . . 85 5 Process Patterns for Trustworthy Development Processes. . . 87 Requirements.. . . . . . . . . . . . . 88 Design. . . . . . . . . . . . . . . 96 Implementation.. . . . . . . . . . . . . 105 Testing. . . . . . . . . . . . . . . 110 Operations. . . . . . . . . . . . . . 114 Summary. . . . . . . . . . . . . . 120 6 Product Patterns for Responsible-AI-by-Design.. . . . . 121 Product Pattern Collection Overview.. . . . . . . . . 122 Supply Chain Patterns. . . . . . . . . . . . 123 System Patterns. . . . . . . . . . . . . 134 Operation Infrastructure Patterns. . . . . . . . . 141 Summary. . . . . . . . . . . . . . 158 7 Pattern-Oriented Reference Architecture for Responsible-AI-by-Design. . . . . . . . . 159 Architectural Principles for Designing AI Systems. . . . . . 160 Pattern-Oriented Reference Architecture.. . . . . . . . 161 Summary. . . . . . . . . . . . . . 165 8 Principle-Specific Techniques for Responsible AI.. . . . . 167 Fairness.. . . . . . . . . . . . . . 167 Privacy. . . . . . . . . . . . . . . 172 Explainability. . . . . . . . . . . . . 178 Summary. . . . . . . . . . . . . . 182 Part III Case Studies. . . . . . . . . . . . . . . 183 9 Risk-Based AI Governance in Telstra. . . . . . . 185 Policy and Awareness.. . . . . . . . . . . . 186 Assessing Risk.. . . . . . . . . . . . . 188 Learnings from Practice. . . . . . . . . . . 192 Future Work. . . . . . . . . . . . . . 195 10 Reejig: The World’s First Independently Audited Ethical Talent AI.. . . . . . . . . . . 197 How Is AI Being Used in Talent?.. . . . . . . . . . 198 What Does Bias in Talent AI Look Like?.. . . . . . . . 200 Regulating Talent AI Is a Global Issue.. . . . . . . . . 201 Reejig’s Approach to Ethical Talent AI. . . . . . . . . 202 How Ethical AI Evaluation Is Done: A Case Study in Reejig’s World-First Independently Audited Ethical Talent AI. . . . . . . . 204 Overview.. . . . . . . . . . . . . 204 Project Overview. . . . . . . . . . . . . 206 The Ethical AI Framework Used for the Audit.. . . . . . . 207 The Benefits of Ethical Talent AI.. . . . . . . . . . 210 Reejig’s Outlook on the Future of Ethical Talent AI.. . . . . . 211 11 Diversity and Inclusion in Artificial Intelligence.. . . . . 213 Importance of Diversity and Inclusion in AI.. . . . . . . 215 Definition of Diversity and Inclusion in Artificial Intelligence. . . . 216 Guidelines for Diversity and Inclusion in Artificial Intelligence. . . . 219 Conclusion.. . . . . . . . . . . . . . 234 Part IV Looking to the Future. . . . . . . . . . . . . 237 12 The Future of Responsible AI.. . . . . . . . . 239 Regulation. . . . . . . . . . . . . . 241 Education.. . . . . . . . . . . . . . 242 Standards.. . . . . . . . . . . . . . 244 Tools.. . . . . . . . . . . . . . . 245 Public Awareness.. . . . . . . . . . . . 246 Final Remarks.. . . . . . . . . . . . . 246 Part V Appendix. . . . . . . . . . . . . . . . 249 9780138073923, TOC, 11/7/2023

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Book SynopsisThis cutting-edge guide not only shows how AI is transforming our careers, lives, businesses, and more, but also provides easy, actionable steps to make AI work for us. In this groundbreaking book, celebrated professor, entrepreneur, author, and podcaster Marc Beckman explores the transformative power of artificial intelligence (AI) and how it’s poised to enhance and transform all aspects of society—revolutionizing our careers, enriching our family lives, and bringing our communities closer together. From business and advertising, to medicine, to warfare, to politics—Beckman meticulously explores the different areas where we’ll soon feel AI’s transformative impact. But that’s only half of it. Throughout this book, he also provides the specific steps readers can take now to make sure these coming changes work for them.   From the workplace to the home, AI is poised to reshape the way we approach our professional and personal lives. Beckman uses this book to make the case that AI will free up valuable time and energy, allowing individuals to focus on more creative and meaningful work, but also that AI will create possibilities for engagement that were unthinkable just a generation ago. He shows that with AI as our co-pilot, we’ll unlock new opportunities for growth, innovation, and collaboration—all of which will lead to more fulfilling and rewarding careers. Beckman illustrates how AI will strengthen family bonds and improve the quality of our home lives too, changing everything from how we educate our kids to how we stay connected on social media. And as AI becomes more integrated into our cities and towns, it will play a crucial role in fostering a sense of community and belonging; through AI-powered platforms, Beckman shows how we will collaborate on projects, share resources, and support one another in times of need.   This thought-provoking and essential book is a definitive guide to the many ways in which AI will transform our lives for the better . . . but also surprise us, delight us, force us to (re)consider how we interact with one another, and make us question what exactly counts as “human.” Join Marc Beckman on this exciting journey as he explores the near-endless possibilities of a world powered and transformed by artificial intelligence. It’s an Age of Imagination . . . where the only limit is your own mind.

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Book SynopsisSir Anthony Seldon, the prominent political biographer and leading educationalist, addresses one of the high-stakes issues that will influence our future: the role of artificial intelligence and its impact on education.The use of AI promises an altogether new way of educating, offering learners from all backgrounds widespread access to personalised tuition and digital educational materials from across the world. Educational institutions across the world have been impacted by the COVID-19 pandemic and many have migrated, at least temporarily, to online platforms. The debate about how to deliver knowledge has never been more relevant.Many countries have an excellent education system with their schools and universities excellent, but tailored to the twentieth century. The mass teaching methods of the third revolution era have failed to conquer enduring problems of inequity and lack of individualised learning. AI is disrupting the way we live, work and interact with the environment, and we cannot stop it changing our schools and universities. But we have time albeit not for long to shape this revolution. It will not be a panacea, and if we are not quick, it will start to replace what makes us human being creative, having beliefs, and loving others.This book, presented in considerably updated and extended second edition, is a call to educators everywhere to open their eyes to what is coming. If we do so, then the future will be shaped by us for the common interests of humanity but if we don't, then it will be imposed, and we will all lose.This book has the potential to impel change in our education system which is so badly in need of reform. The new reconsidered version in the wake of the COVID pandemic serves to emphasize even more strongly the role AI can play in education and how its use is being accelerated.' Lord Clement-Jones CBE

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Book SynopsisYou've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain.

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Book SynopsisDr. Len Bass is a seasoned researcher with over 30 years in software architecture and more than a decade in DevOps. He has been teaching DevOps to graduate students for seven years and is the author of a bestselling book on software architecture, along with three books on DevOps. Dr. Qinghua Lu is a principal research scientist at CSIRO's Data61, leading the Software Engineering for AI and Responsible AI science teams. She is a coauthor of Responsible AI: Best Practices for Creating Trustworthy AI Systems (Addison-Wesley, 2024). Prof. Dr. Ingo Weber is a professor at the Technical University of Munich and Director of Digital Transformation and ICT Infrastructure at Fraunhofer-Gesellschaft. He has written numerous publications and textbooks, including DevOps: A Software Architect's Perspective and Architecture for Blockchain Applications. Dr. Liming Zhu is a research director at CSIRO's Data61 and i

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Book SynopsisAI is revolutionizing the world. Here’s how democracies can come out on top.Artificial intelligence is revolutionizing the modern world. It is ubiquitous—in our homes and offices, in the present and most certainly in the future. Today, we encounter AI as our distant ancestors once encountered fire. If we manage AI well, it will become a force for good, lighting the way to many transformative inventions. If we deploy it thoughtlessly, it will advance beyond our control. If we wield it for destruction, it will fan the flames of a new kind of war, one that holds democracy in the balance. As AI policy experts Ben Buchanan and Andrew Imbrie show in The New Fire, few choices are more urgent—or more fascinating—than how we harness this technology and for what purpose. The new fire has three sparks: data, algorithms, and computing power. These components fuel viral disinformation campaigns, new hacking tools, and military weapons that

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Book SynopsisArtificial intelligence (AI) is often discussed as something extraordinary, a dream--or a nightmare--that awakens metaphysical questions on human life. Yet far from a distant technology of the future, the true power of AI lies in its subtle revolution of ordinary life. From voice assistants like Siri to natural language processors, AI technologies use cultural biases and modern psychology to fit specific characteristics of how users perceive and navigate the external world, thereby projecting the illusion of intelligence. Integrating media studies, science and technology studies, and social psychology, Deceitful Media examines the rise of artificial intelligence throughout history and exposes the very human fallacies behind this technology. Focusing specifically on communicative AIs, Natale argues that what we call AI is not a form of intelligence but rather a reflection of the human user. Using the term banal deception, he reveals that deception forms the basis of all human-computer interactions rooted in AI technologies, as technologies like voice assistants utilize the dynamics of projection and stereotyping as a means for aligning with our existing habits and social conventions. By exploiting the human instinct to connect, AI reveals our collective vulnerabilities to deception, showing that what machines are primarily changing is not other technology but ourselves as humans. Deceitful Media illustrates how AI has continued a tradition of technologies that mobilize our liability to deception and shows that only by better understanding our vulnerabilities to deception can we become more sophisticated consumers of interactive media.Trade Reviewa real breath of fresh air ... fundamental reading for an understanding of AI as a socio-material phenomenon * Domenico Napolitano, Prometheus *Deceitful Media makes a compelling case that the development of artificial intelligence is inextricably woven together with fallacies of human perception. Analyzing archival documents from the 1950s onward, Simone Natale demonstrates the prevalence of what he calls 'banal deception,' the everyday taken-for-granted interactions that attribute human-equivalent intelligence to algorithmic processes that in themselves are quite different. A remarkable achievement, this accessible and well-written book is a 'must-read' for media scholars, cultural critics, and anyone interested in the significance of artificial intelligence for our time. * N. Katherine Hayles, author of Postprint: Books and Becoming Computational *From the time of Alan Turing's Game of Imitation, the benchmark of machine intelligence has been deceptive communicative behavior. In Deceitful Media, Simone Natale provides a decisive and revealing analysis of the history, significance, and social consequences of deception in artificial intelligence, demonstrating how and why deceit is not a bug to be fixed but a defining feature of both the theory and practice of AI. * David J. Gunkel, Northern Illinois University *A fundamental fear surrounding artificial intelligence is that it will one day become a technology of deception. As Simone Natale informs us in Deceitful Media, that day is already here. However, such deception is not the malicious kind of science fiction; rather, the deceit of AI is one enacted in our minds as they encounter technologies carefully crafted to our social nature. By situating AI within the context of media and communication theory, Natale dispels the hype surrounding AI as a technology, replacing it with a theoretical lens informed by the seemingly mundane elements of our ongoing interactions with AI as forms of media. As a result, Deceitful Media provides us with not only a new way to think about AI, but also a more grounded approach to assessing its impact for ourselves and society. * Andrea Guzman, Northern Illinois University *A remarkable critical history of the artifice central to artificial intelligence. Natale has peered beyond the scandalously uncanny valleys, the many muddily mediated human-machine thought experiments, and scurrilous bids for grants and investor capital to uncover the dark heart of artificial intelligence: namely, the everyday ordinary ways that 'banal deception' is integrated into our lives. In so doing, Deceitful Media offers pressingly ethical, sober, and sophisticated pathways to reclaiming the unnatural ordinariness of the human psyche in the shadow of artificial intelligence. Highly readable and deeply instructive. * Benjamin Peters, University of Tulsa *Table of ContentsTable of Contents Acknowledgments Introduction Chapter 1. The Turing Test: Cultural life of an idea Chapter 2. How to dispel magic: Computers, interfaces, and the problem of the observer Chapter 3. The Eliza effect: Joseph Weizenbaum and the emergence of chatbots Chapter 4. Of daemons, dogs and trees: Situating AI in software Chapter 5. How to create a bot: Programming deception at the Loebner Prize Chapter 6. To believe in Siri: A critical analysis of voice assistants Conclusion: Our sophisticated selves Bibliography

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Book SynopsisArtificial intelligence has long been a mainstay of science fiction and increasingly it feels as if AI is entering our everyday lives, with technology like Apple's Siri now prominent, and self-driving cars almost upon us.But what do we actually mean when we talk about 'AI'? Are the sentient machines of 2001 or The Matrix a real possibility or will real-world artificial intelligence look and feel very different? What has it done for us so far? And what technologies could it yield in the future?AI expert Yorick Wilks takes a journey through the history of artificial intelligence up to the present day, examining its origins, controversies and achievements, as well as looking into just how it works. He also considers the future, assessing whether these technologies could menace our way of life, but also how we are all likely to benefit from AI applications in the years to come.Entertaining, enlightening, and keenly argued, this is the essential one-stop guide to the AI debate.

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Book SynopsisA New York Times bestselling author and tech columnist's counter-intuitive guide to staying relevant - and employable - in the machine age by becoming irreplaceably human.It's not a future scenario any more. We've been taught that to compete with automation and AI, we'll have to become more like the machines themselves, building up technical skills like coding. But, there's simply no way to keep up. What if all the advice is wrong? And what do we need to do instead to become futureproof?We tend to think of automation as a blue-collar phenomenon that will affect truck drivers, factory workers, and other people with repetitive manual jobs. But it's much, much broader than that. Lawyers are being automated out of existence. Last year, JPMorgan Chase built a piece of software called COIN, which uses machine learning to review complicated contracts and documents. It used to take the firm's lawyers more than 300,000 hours every year to review all of those documents. Now, it takes a few seconds, and requires just one human to run the program. Doctors are being automated out of existence, too. Last summer, a Chinese tech company built a deep learning algorithm that diagnosed brain cancer and other diseases faster and more accurately than a team of 15 top Chinese doctors.Kevin Roose has spent the past few years studying the question of how people, communities, and organisations adapt to periods of change, from the Industrial Revolution to the present. And the insight that is sweeping through Silicon Valley as we speak -- that in an age dominated by machines, it's human skills that really matter - is one of the more profound and counter-intuitive ideas he's discovered. It's the antidote to the doom-and-gloom worries many people feel when they think about AI and automation. And it's something everyone needs to hear.In nine accessible, prescriptive chapters, Roose distills what he has learned about how we will survive the future, that the way to become futureproof is to become incredibly, irreplaceably human.Trade ReviewA concise, insightful and sophisticated guide to maintaining humane values in an age of new machines -- The New York Times Book ReviewWhile we need to rewrite the rules of the twenty-first-century economy, Kevin's book is a great look at how people can do this on a personal level to always put humanity first -- Andrew YangLightly written and engaging * The Times *Kevin Roose provides a clear, compelling strategy for surviving the next wave of technology with our jobs - and souls - intact... Futureproof is the survival guide you need. * Charles Duhigg, The Power of Habit *AI will be a far bigger game changer for the world than COVID-19. And unless we start thinking and planning for it far more seriously now, we will be in even greater peril. Futureproof is a brilliant book that explains what we need to do, all of us, right now * Anthony Seldon *Roose offers an upbeat, practical guide for dealing with "a world that is increasingly arranged by and for machines" . . . Helpful advice to quell workers' anxiety * Kirkus Reviews *PRAISE FOR YOUNG MONEY - If Kevin Roose's finely crafted Young Money does not scare you straight about the life of a young financial analyst on Wall Street, it can't be done. Roose's frolic through Wall Street's playpen is a must-read. * House of Cards; Money and Power *Despite all the press about Wall Street, the stories that don't usually get told are those of the recent college graduates who clamour for the chance to work 100 hour plus weeks at the big banks. Kevin Roose's new book, which follows a handful of analysts through the trials and tribulations of their early years on the Street, is a thoughtful exploration of their motivations and their experiences - and it's a great read. * The Smartest Guys in the Room and All the Devils are Here *A cautionary true-life tale, Young Money should be required reading for every college student who is contemplating a job on Wall Street. As for the rest of us, who remember Wall Street before 2008, Kevin Roose has provided a great window into how that world has changed-and how it hasn't. * The Predator's Ball *Highly entertaining and impressive ...Roose's captivating read is sure to appeal to readers young and old who are interested in the zeitgeist of Wall Street since the crash * Publisher's Weekly *[Young Money] offers a compelling glimpse of Wall Street in the post-2008 recession era...thought provoking, excellent book * Booklist *The young people who have flocked to Wall Street are often badly used, caught up in power struggles among middle management and little appreciated ... [Young Money] captures the daily indignities to which the junior capitalists are subjected * Kirkus Reviews *

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

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Book SynopsisArtificial intelligence is being used, on a massive scale, to decide who gets hired, fired and promoted. Through whistleblower exclusives, leaked internal documents and astonishing real-world practices, journalist Hilke Schellmann reveals the secret rise of AI in the world of work. Testing them herself, she discovers that many algorithms making these high-stakes calculations do more harm than good, and traces their origins to troubling pseudoscientific ideas about people’s ‘true’ essence. Interviewing experts, developers and ordinary workers, The Algorithm offers fascinating and alarming truths. From software analysing interviewees’ facial expressions and tone of voice, to video games assessing their performance, to ‘personality profiles’ built from candidates’ social media, almost all major employers use AI in recruitment. Programmes track their staff’s activity, group dynamics and physical health, identifying who is productive, a bully, worth long-term investment, or likely to quit. But can we trust them? In a world of severe job insecurity, workplace algorithms are on the brink of dominating or even threatening us—if we don’t fight back.Trade Review‘The best available case study [of] … the use of artificial intelligence by human resource departments.' -- The New York Times, 'Top 5 Books on Artificial Intelligence'‘Focuses on how the technology is already deployed in personnel decisions in the workplace — with alarming results.’ -- Financial Times'Schellmann pulls the curtain back on the AI-driven "HR tech" revolution taking over hiring and managing employees, and finds tools that are arbitrary, ineffective, discriminatory and likely unlawful. Reads like a dozen scandals waiting to erupt.' -- Gavin Mueller, author of 'Breaking Things at Work''A disturbing investigation into use of AI systems in hiring, firing, and employee surveillance. As Schellmann demonstrates, AI has moved into crucial areas of our lives, but the process has been so fast and silent that its influence is almost invisible. She argues that HR managers should be required to understand how their algorithms work, and there must be greater human input to personnel decisions. This eye-opening book makes it hard to disagree.' -- 'Kirkus Reviews''In "The Algorithm", Hilke Schellmann has done the impossible: she has rendered the baffling 'Wild West' of AI immensely readable and approachable. Schellmann gives us the dark and hidden history of tech innovation and the marketplace through the stories of those whose lives have been smashed by its glitches.' -- Eliza Griswold, Pulitzer Prize-winning author of 'Amity and Prosperity''One of the most important topics of our time--one that impacts all of us more than we realise. The book takes a balanced approach to illuminating the current state of AI in the workplace. It's not just about incredible benefits or doomsday scenarios, but a real look into the current state of these tools, the incentive systems driving their proliferation, the mixed results they provide, and how we might ensure better outcomes. Highly recommended.' -- Ryan Fuller, former vice president for workplace intelligence, Microsoft'A fresh, important perspective on how AI is changing many critical workplace decisions in organizations. Schellmann's research is thorough and clever, and exposes the many of problems that AI and its proponents have already created for companies and employees.' -- David Futrell, former senior director of organization performance, Walmart'Hilke Schellmann was one of the first journalists to understand the dangers of AI passing judgement on workers, and The Algorithm is an absolutely vital book about the risks and harms of the systems already operating--on us--today.' -- Clay Shirky, author of "Cognitive Surplus" and "Here Comes Everybody"

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Book SynopsisWhat is artificial intelligence? What problems does it solve? Should we fear its potential? In this highly accessible guide to the subject, Richard Urwin explains how AI came about and how it has developed over the years through the construction of ever more sophisticated computer programs. From primitive calculators and early robotics to stock market analytics and ChatGPT, readers can explore the history and far-reaching capabilities of this dynamic field and its potentially frightening possibilities. Includes:• The History of Artificial Intelligence• Data-mining and Statistics• Deep learning• Swarm Intelligence• The singularity By turns fascinating and scary, Artificial Intelligence will take the reader on an amazing journey of this field''s world-changing potential.

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Book SynopsisPrepare for the coming convergence of AI and quantum computing A collection of essays from 20 renowned, international authors working in industry, academia, and government, Convergence: Artificial Intelligence and Quantum Computing explains the impending convergence of artificial intelligence and quantum computing. A diversity of viewpoints is presented, each offering their view of this coming watershed event. In the book, you'll discover that we're on the cusp of seeing the stuff of science fiction become reality, with huge implications for ripping up the existing social fabric, global economy, and current geopolitical order. Along with an incisive foreword by Hugo- and Nebula-award winning author David Brin, you'll also find: Explorations of the increasing pace of technological developmentExplanations of why seemingly unusual and surprising breakthroughs might be just around the cornerMaps to navigate the potential minefields that await us as AI and quantum computing come togetherA fascinating and thought-provoking compilation of insights from some of the leading technological voices in the world, Convergence convincingly argues that we should prepare for a world in which very little will remain the same and shows us how to get ready.Table of ContentsPreface xi Foreword xix Part I Policy and Regulatory Impacts 1 Chapter 1 Quantum Inflection Points 3Jim Gable Chapter 2 Quantum Delegation 11Mandy Sweeney and Chris Gauthier Chapter 3 The Problem of Machine Actorhood 23Patrick Thaddeus Jackson Chapter 4 Data Privacy, Security, and Ethical Governance Under Quantum AI 37Sarah Pearce Chapter 5 The Challenge of Quantum Noise 45Philip Johnson Chapter 6 A New Kind of Knowledge Discovery 53Ramin Ayanzadeh and Milton Halem Part II Economic Impacts 61 Chapter 7 Quantum Tuesday: How the U.S. Economy Will Fall, and How to Stop It 63Alexander W. Butler Chapter 8 Quantum-AI Space Communications 83Mason Peck Chapter 9 Quantum Planet Hacking 93Philip L. Frana Chapter 10 Ethics and Quantum AI for Future Public Transit Systems 111Benjamin Crawford Chapter 11 The Road to a Better Future 119Denise Ruffner and André M. König Part III Social Impacts 127 Chapter 12 The Best Numbers Are in Sight. But Understanding? 129Roald Hoffmann and Jean-Paul Malrieu Chapter 13 The Advancement of Intelligence or the End of It? 143Kate Jeffery Chapter 14 Quantum of Wisdom 157Colin Allen and Brett Karlan Chapter 15 Human Imagination and HAL 167Erik Viirre Chapter 16 A Critical Crossroad 175Joseph N. Pelton Chapter 17 Empathetic AI and Personalization Algorithms 183Philippe Beaudoin and Alexander W. Butler Chapter 18 Should We Let the Machine Decide What Is Meaningful? 193J. M. Taylor Chapter 19 The Ascent of Quantum Intelligence in Steiner’s Age of the Consciousness Soul 205Stephen R. Waite Chapter 20 Quantum Computing’s Beautiful Accidents 213Christopher Savoie Appendix A What Is Quantum Computing? 221Philip L. Frana Appendix B What Is Artificial Intelligence? 239Philip L. Frana Glossary 247 References 251 Index 259 About the Editor 271

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Book SynopsisThis book examines a variety of different concepts related to data science ethics and techniques that can help with, or lead to, ethical concerns, whilst featuring cautionary tales that illustrate the importance and potential impact of data science ethics.Trade ReviewAn excellent reading with both depth and breadth on some of the most important challenges and risks data scientists, businesses, governments and societies face today as Artificial Intelligence adoption grows. These are topics everyone needs to be aware of, and this is one of the very few must read books on these issues * Theodoros Evgeniou, Professor of Decision Sciences and Technology Management at INSEAD, France *This is an important and timely book for data scientists, written in a clear and engaging way. Motivated by many relevant examples, the author successfully de-mystifies data ethics lingo and presents a comprehensive view of ethical considerations during the entire data science lifecycle. * Galit Shmueli, Tsing Hua Distinguished Professor, Institute of Service Science and Institute Director, College of Technology Management, National Tsing Hua University, Taiwan *Table of ContentsFoster Provost: Foreword Preface 1: Introduction to Data Science Ethics 2: Ethical Data Gathering 3: Ethical Data Preprocessing 4: Ethical Modelling 5: Ethical Evaluation 6: Ethical Deployment 7: Conclusion

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Book SynopsisTable of ContentsIntroduction 1 Part 1: Exploring Practical AI and How It Works 7 Chapter 1: Demystifying Artificial Intelligence 9 Chapter 2: Looking at Uses for Practical AI 29 Chapter 3: Preparing for Practical AI 45 Chapter 4: Implementing Practical AI 63 Part 2: Exploring Vertical Market Applications 81 Chapter 5: Healthcare/HMOs: Streamlining Operations 83 Chapter 6: Biotech/Pharma: Taming the Complexity 91 Chapter 7: Manufacturing: Maximizing Visibility 99 Chapter 8: Oil and Gas: Finding Opportunity in Chaos 111 Chapter 9: Government and Nonprofits: Doing Well by Doing Good 119 Chapter 10: Utilities: Renewing the Business 133 Chapter 11: Banking and Financial Services: Making It Personal 141 Chapter 12: Retail: Reading the Customer’s Mind 149 Chapter 13: Transportation and Travel: Tuning Up Your Ride 157 Chapter 14: Telecommunications: Connecting with Your Customers 167 Chapter 15: Legal Services: Cutting Through the Red Tape 173 Chapter 16: Professional Services: Increasing Value to the Customer 181 Chapter 17: Media and Entertainment: Beating the Gold Rush 189 Part 3: Exploring Horizontal Market Applications 197 Chapter 18: Voice of the Customer/Citizen: Finding Coherence in the Cacophony 199 Chapter 19: Asset Performance Optimization: Increasing Value by Extending Lifespans 207 Chapter 20: Intelligent Recommendations: Getting Personal 217 Chapter 21: Content Management: Finding What You Want, When You Want It 231 Chapter 22: AI-Enhanced Content Capture: Gathering All Your Eggs into the Same Basket 239 Chapter 23: Regulatory Compliance and Legal Risk Reduction: Hitting the Bullseye On a Moving Target 249 Chapter 24: Knowledge Assistants and Chatbots: Monetizing the Needle in the Haystack 265 Chapter 25: AI-Enhanced Security: Staying Ahead by Watching Your Back 275 Part 4: The Part of Tens 287 Chapter 26: Ten Ways AI Will Influence the Next Decade 289 Chapter 27: Ten Reasons Why AI Is Not a Panacea 297 Index 313

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Book SynopsisThe best fictional treatment of the possibilities and horrors of artificial intelligence that I've read' GuardianIn 1997 Laura Bow invented Organon, a rudimentary artificial intelligence.Now she and her creation are at the forefront of the new wave of technology, and Laura must decide whether or not to reveal Organon's full potential to the world. If it falls into the wrong hands, its power could be abused. Will Organon save humanity, or lead it to extinction?I Still Dream is a powerful tale of love, loss and hope; a frightening, heartbreakingly human look at who we are now and who we can be, if we only allow ourselves.Trade Review‘Smythe's most accomplished work… Utterly engrossing’ Observer Best Summer Books ‘Superbly evocative… a book about varieties of intelligence (human, artificial, moral and emotional) which showcases the novelistic one of its very smart and very talented creator’ Sunday Times ‘I Still Dream is amazing!’ Beth Lewis, author of Wolf Road ‘Sad, beguiling…a beautiful mixtape about what it is to be human, and alive, and to love another more than oneself’ Irish Times ‘A haunting meditation on the implications of AI, on intelligence itself, and on what it means to live and die in the age of technology. I Still Dream is a must-read for fans of David Mitchell, for anyone who’s ever used a smartphone, and for anyone who appreciates riveting plots and beautiful prose.’ Emily St. John Mandel, author of Station Eleven ‘A humane, thought-provoking and powerful book … superbly orchestrated … beautiful, involving, emotionally compelling’ Adam Roberts ‘One of the most affecting and brilliant books I've read this year … a huge achievement: toweringly ambitious, and yet beautifully controlled and crafted’ Sam Byers, author of Idiopathy ‘This is a visionary novel about what it is to be human. It is a startling look at intelligence, empathy and grief in the face of technology. Smythe has written his masterpiece’Nikesh Shukla, editor of The Good Immigrant and author of Meatspace ‘I STILL DREAM begins with melancholy nostalgia, before growing urgently contemporary and finally chillingly prescient. It is a strikingly intelligent book about intelligence itself: artificial intelligence, emotional intelligence, and all the ways we watch each other. Having read it, you may wish to turn off your phone’ Sarah Perry, author of The Essex Serpent

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Book Synopsis'an essential and fascinating manual for every woman who wants to understand equality within an ever-changing, modern world.' Scarlett Curtis[this book] taught me more than any book has ever taught me about AI.' Chris Evans, Virgin RadioHow To Talk To Robots, is your girls guide to Artificial Intelligence. Entrepreneur Tabitha Goldstaub welcomes you into the AI world with a warm embrace. She brilliantly breaks down the tech-bro barriers offering a straightforward introduction and makes clear the enormous benefits of understanding AI..If your social feed defines your spending habits or you've downloaded the latest filter to see what you'll look like when you are old or now connect with your doctor using an app, have applied for a job online or used your phone to arrive at work in record time, AI is playing a part in how you live, work and play. We live in an era where machines are taught to learn and act without human intervention and there are infinite possibilities to their applicatioTrade Review Praise for How To Talk To Robots: ‘Takes on the tech bros and provides a fun and accessible primer to artificial intelligence.’ The Financial Times ‘If you’re a woman who feels like the world of tech and AI is a boys club that you’ve been purposefully left out of, you might not be wrong! Artificial intelligence has the potential to hugely improve the lives of women or further propel us into a world even more dominated by the patriarchy. How to Talk to Robots is an essential and fascinating manual for every woman who wants to understand equality within an ever-changing, modern world. It is a VITAL text for every woman who wants answers to their questions – whether you know about tech or barely understand what AI means. READ THIS BOOK! It’s very important.’Scarlett Curtis, Sunday Times bestselling author of Feminists Don’t Wear Pink ‘…[this book] taught me more than any book has ever taught me about AI’Chris Evans, Virgin Radio ‘The must have equivalent of The Social Dilemma’Virgin Radio listener Praise for Tabitha Goldstaub:‘Fast becoming one of the best-connected women in the UK…. Goldstaub is one of a small yet influential number of women who are reshaping the world of tech and how it reads – and interacts with our minds and bodies.’ VOGUE ‘There's a revolution under way — and a woman called Tabitha Goldstaub is on a mission to make sure not only that London takes the lead but that women are given an equal role in it.’ EVENING STANDARD ‘Tabitha Goldstaub…is particularly alarmed by AI’s potential for gender bias and, in an effort to address this, is spearheading a campaign to get more women working in the field.’ THE GUARDIAN ‘Start training like a robot now, says CognitionX's Tabitha Goldstaub’ CAMPAIGN MAGAZINE ‘…she’s on a mission to make sure women are given an equal role in it [AI].’ GRAZIA

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