Artificial intelligence (AI) Books

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 Because of this, most of the available resources are outdated or too academic in nature, and none of them are made with Elixir programmers in mind.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

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

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

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

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

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

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Book SynopsisAn essential handbook for our time of astonishing technological transformation by the world''s leading AI poet.In 2018, Sasha Stiles found herself wondering what the rise of large language models might mean for writers - and for creativity at large. To probe the possibilities, she began translating over a decade of analog poems and research into a personalized AI model, augmenting human voice with next-gen imagination.Crafted jointly by Stiles and her poetic alter ego, and first published in hardcover in 2021, Technelegy is a prescient artifact of the pre-ChatGPT era - a collection of generative poems nestled in their own training data - and an unprecedented experiment fusing past and future, woman and machine, verse and code, elegy and wordplay, in search of answers to the urgent question: what does it mean to be human in a nearly posthuman world?

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Book SynopsisArtificial intelligence changes everything. This book encourages readers to consider the challenges of the digital transformation driven by Artificial Intelligence. The reader will discover why this transformation is to be regarded as the greatest cultural revolution since the invention of mass printing and how it can be shaped positively in a value-oriented way. The author pursues the thesis that intelligent objects on the internet, as well as physical objects, are attaining their own consciousness. Using many examples, he shows how these digital companions become our digital partners. This non-fiction book provides many suggestions for one's own living and working environment and is full of examples of how artificial intelligence systems can be implemented. The reader learns what is already possible today and what can be expected in the next ten to twenty years. The book is of interest to anyone interested in AI and the digital transformation - from those responsible in companies, public institutions, and in politics, to all teachers and parents who want to understand what the next generation can expect.Trade Review“This book might be worth reading for someone who wants to inform themselves about the challenges of coming digital transformations (including AI) in popular style … .” (G. Haring, Computing Reviews, August 11, 2022)Table of ContentsChapter 1 . It's all about us.- Chapter 2 . The Objects of This World Become Intelligent .- chapter 3. How did Artificial Intelligence come into being and where do we stand today? .- Chapter 4. Can Machines have their own Consciousness? .- Chapter 5. The Homo Zappiens .- Chapter 6. The Inverse Gutenberg Revolution .- Chapter 7. The Age of Hybrid Intelligence .- Chapter 8. The Digital System Landscape .- Chapter 9. On the Way to new Business models .- Chapter 10. Artificial Intelligence is a Gamechanger of all Jobs .- Chapter 11. Everything is linked to everything and becomes transparent .- Chapter 12. The ethical and legal Implications .- Chapter 13. Guidelines for the necessary Redesign of our regulatory Systems in Industry and Society .

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Book SynopsisThis book focuses on neuromorphic computing principles and organization and how to build fault-tolerant scalable hardware for large and medium scale spiking neural networks with learning capabilities. In addition, the book describes in a comprehensive way the organization and how to design a spike-based neuromorphic system to perform network of spiking neurons communication, computing, and adaptive learning for emerging AI applications. The book begins with an overview of neuromorphic computing systems and explores the fundamental concepts of artificial neural networks. Next, we discuss artificial neurons and how they have evolved in their representation of biological neuronal dynamics. Afterward, we discuss implementing these neural networks in neuron models, storage technologies, inter-neuron communication networks, learning, and various design approaches. Then, comes the fundamental design principle to build an efficient neuromorphic system in hardware. The challenges that need to be solved toward building a spiking neural network architecture with many synapses are discussed. Learning in neuromorphic computing systems and the major emerging memory technologies that promise neuromorphic computing are then given.A particular chapter of this book is dedicated to the circuits and architectures used for communication in neuromorphic systems. In particular, the Network-on-Chip fabric is introduced for receiving and transmitting spikes following the Address Event Representation (AER) protocol and the memory accessing method. In addition, the interconnect design principle is covered to help understand the overall concept of on-chip and off-chip communication. Advanced on-chip interconnect technologies, including si-photonic three-dimensional interconnects and fault-tolerant routing algorithms, are also given. The book also covers the main threats of reliability and discusses several recovery methods for multicore neuromorphic systems. This is important for reliable processing in several embedded neuromorphic applications. A reconfigurable design approach that supports multiple target applications via dynamic reconfigurability, network topology independence, and network expandability is also described in the subsequent chapters. The book ends with a case study about a real hardware-software design of a reliable three-dimensional digital neuromorphic processor geared explicitly toward the 3D-ICs biological brain’s three-dimensional structure. The platform enables high integration density and slight spike delay of spiking networks and features a scalable design. We present methods for fault detection and recovery in a neuromorphic system as well.Neuromorphic Computing Principles and Organization is an excellent resource for researchers, scientists, graduate students, and hardware-software engineers dealing with the ever-increasing demands on fault-tolerance, scalability, and low power consumption. It is also an excellent resource for teaching advanced undergraduate and graduate students about the fundamentals concepts, organization, and actual hardware-software design of reliable neuromorphic systems with learning and fault-tolerance capabilities.Table of Contents1 Introduction to Neuromorphic Computing Systems.- 2 Neuromorphic System Design Fundamentals.- 3 Learning in Neuromorphic Systems.- 4 Emerging Memory Devices for Neuromorphic Systems.- 5 Communication Networks for Neuromorphic Systems.- 6 Fault-Tolerant Neuromorphic System Design.- 7 Reconfigurable Neuromorphic Computing System.- 8 Case Study: Real Hardware-Software Design of 3D-NoC-based Neuromorphic System.- 9 Survey of Neuromorphic Systems.

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Book SynopsisThis definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”.The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big. Table of Contents1. Data Science Process2. Dimensionality Reduction - Creating Manageable Training Datasets3. Classical Algorithms - Overview4. Regression Analysis5. Decision Tree6. Ensemble - Bagging and Boosting7. K-Nearest Neighbors8. Naive Bayes9. Support Vector Machines: A supervised learning algorithm for Classification and Regression10. Clustering Overview11. Centroid-based Clustering12. Connectivity-based Clustering13. Gaussian Mixture Model14. Density-based15. BIRCH16. CLARANS17. Affinity Propagation Clustering18. STING19. CLIQUE20. Artificial Neural Networks21. ANN-based Applications22. Automated Tools23. Data Scientist’s Ultimate Workflow

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Book SynopsisAI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers.Table of Contents1.Introduction to AI in Learning – Designing the Future.- Part I: AI expanding learning and wellbeing throughout life.- 2.Artificial Intelligence Innovations for Multimodal Learning, Interfaces, and Analytics.- 3.Curiosity and Interactive Learning in Artificial Systems.- 4.Assessing and Tracking Students’ Wellbeing through an Automated Scoring System: Schoolday Wellbeing Model.- 5.Learning from Intelligent Social Agents as Social and Intellectual Mirrors.- 6.An AI-Powered Teacher Assistant for Student Problem Behavior Diagnosis.- 7.Analysis and Improvement of Classroom Teaching Based on Artificial Intelligence.- Part II. AI in Games and Simulations.- 8.Perspectives and Metaphors of Learning: A Commentary on James Lester’s Narrative-centered AI-based Environments.- 9.Learning Career Knowledge: Can AI Simulation and Machine Learning Improve Career Plans and Educational Expectations?.- 10.Learning clinical reasoning through gaming in nursing education – Future scenarios of game metrics and AI.- 11.AI-Supported Simulation-Based Learning: Learners’ Emotional Experiences and Self-Regulation in Challenging Situations.- Part III. AI Technologies for education and Intelligent Tutoring Systems.- 12.Training Hard Skills in Virtual Reality: Developing a Theoretical Framework for AI-based Immersive Learning.-13.Multiple users’ experiences of an AI-aided educational platform for teaching and learning. 14.Deep Learning in Automatic Math Word Problem Solvers. 15.Recent Advances in Intelligent Textbooks for Better Learning.- Part IV. AI and Ethical Challenges in New Learning Environments.- 16.Ethical Guidelines for Artificial Intelligence-based Learning: A Transnational Study between in China and Finland.- 17.Artificial Intelligence Ethics from the Perspective of Educational Technology Companies and Schools.- 18.Artificial Intelligence in Education as a Rawlsian Massively Multiplayer Game: A thought experiment on AI Ethics.- 19.Four surveillance technologies creating challenges for education.- 20.Reflections on the contributions and future scenarios in AI-based learning.

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Book SynopsisThis book offers a structured overview and a comprehensive guide to the emerging field of Autonomous Intelligent Cyber Defense Agents (AICA). The book discusses the current technical issues in autonomous cyber defense and offers information on practical design approaches. The material is presented in a way that is accessible to non-specialists, with tutorial information provided in the initial chapters and as needed throughout the book. The reader is provided with clear and comprehensive background and reference material for each aspect of AICA.Today’s cyber defense tools are mostly watchers. They are not active doers. They do little to plan and execute responses to attacks, and they don’t plan and execute recovery activities. Response and recovery – core elements of cyber resilience – are left to human cyber analysts, incident responders and system administrators. This is about to change. The authors advocate this vision, provide detailed guide to how such a vision can be realized in practice, and its current state of the art.This book also covers key topics relevant to the field, including functional requirements and alternative architectures of AICA, how it perceives and understands threats and the overall situation, how it plans and executes response and recovery, how it survives threats, and how human operators deploy and control AICA. Additionally, this book covers issues of testing, risk, and policy pertinent to AICA, and provides a roadmap towards future R&D in this field.This book targets researchers and advanced students in the field of cyber defense and resilience. Professionals working in this field as well as developers of practical products for cyber autonomy will also want to purchase this book.Table of ContentsChapter. 1. Autonomous Intelligent Cyber-defense Agent: Introduction and OverviewChapter. 2. Alternative Architectural ApproachesChapter. 3. Perception of EnvironmentChapter. 4. Perception of Cyber ThreatsChapter. 5. Situational Understanding and DiagnosticsChapter. 6. Learning about the AdversaryChapter. 7. Response PlanningChapter. 8. Recovery PlanningChapter. 9. Strategic Cyber CamouflageChapter. 10. Adaptivity & AntifragilityChapter. 11. Negotiation and CollaborationChapter. 12. Human InteractionsChapter. 13. Testing and MeasurementsChapter. 14. Deployment and OperationChapter. 15. Command in AICA-intensive OperationsChapter. 16. Risk ManagementChapter. 17. Policy IssuesChapter. 18. Development ChallengesChapter. 19. Case Study A: A Prototype Autonomous Intelligent Cyber-defense AgentChapter. 20. Case Study B: AI Agents for Tactical EdgeChapter. 21. Case Study C: the Sentinel Agents

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Book SynopsisChapter 1 The Landscape.- Chapter 2 "Explainable AI: needs, opportunities and challenges".- Chapter 3 Intrinsic Explainable Models.- Chapter 4 Model-agnostic methods for XAI.- Chapter 5 Explaining Deep Learning Models.- Chapter 6 Additive Models for Interpretability.- Chapter 7 Adversarial Machine Learning and Explainability.-Chapter 8 Explainability of Language Models (XAI and LLM).- Chapter 9 Making science with Machine Learning and XAI.- Chapter 10 AGI, LLM, XAI.- Chapter 11 "A proposal for a sustainable model of Explainable AI.

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Book SynopsisForeword.- Introduction.- Navigating the AI Frontier: Emerging Trends and Governance Implications.- How to Evaluate GenAI Models: Unlocking Their Potential for Your Unique Tasks.- Taming AI Hallucinations: Innovations in Retrieval-Augmented Generation and Evaluation.- Invisible Exposure: The Privacy Risks of AI Models.- Unmasking Deepfakes: Navigating Challenges and Solutions in the Age of AI-Driven Manipulation.- Unmasking the Security Risks of Generative AI: Threats and Strategies for Defense.- Unmasking Model Bias: Building Fair, Reliable, and Trustworthy Models.- Unveiling the Black Box: Enhancing AI/ML Model Explainability for Transparency and Trust.- Model Monitoring: Lessons from Recent Failures.- Navigating Change Management: Lessons and Best Practices for Managing AI/GenAI Models in a Dynamic Landscape.- AI Governance: Preparing for the Rise of Agentic AI.- Epilogue.

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Book Synopsis5G, the emerging technology in mobile communication, is expected to deliver an important and decisive impact on several of the UN’s Sustainable Development Goals where universal accessibility to ICTs remains a serious concern. However, cyber security has emerged as a serious challenge, not least because of the increased accessibility and broader usage with associated vulnerability. Developing countries have additional challenges associated with both the expected faster build-up of accessibility and lack of qualified competencies within cyber security. Discussion of these challenges is the overall theme and motivation for this book.Technical topics discussed in the book include: 5G in rural networks Critical infrastructures Open RAN Protection of privacy Cybersecurity and machine learning Cybersecurity and disaster monitoring Table of Contents1. Expansion-security Tradeoffs in the Pathway to Rural 5G Networks 2. Cybersecurity Threats to 5G's Critical Infrastructure 3. Critical Infrastructure Security: Issues, Challenges and 5G Solutions in Indian Perspective 4. OpenRAN and Security in an Emerging Country Context 5. Deployment of 5G in Emerging Economies: Cyber-Security Challenges and Potentials for Ghana 6. Mapping the Iranian Policy Network for Protecting Users’ Data on Platforms 7. Empirical Investigation of the Drives of 5G and Mediating Role of Users' Attitude to Achieve Word of Mouth and Willingness: A Case Study of Microfinance Institutions of Zambia 8. Study of Cyber Security in 5G using a Machine Learning Algorithm for Protecting Financial Transactions – Developing World Cases 9. Integrating InSAR, GNSS, IoT, 5G, and Cybersecurity for Earthquakes/Tremor Monitoring and Forecasting in Abuja, Nigeria

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Book SynopsisThis book presents a unique examination of mobile robots and embedded systems, from introductory to intermediate level. It is structured in three parts, dealing with Embedded Systems (hardware and software design, actuators, sensors, PID control, multitasking), Mobile Robot Design (driving, balancing, walking, and flying robots), and Mobile Robot Applications (mapping, robot soccer, genetic algorithms, neural networks, behavior-based systems, and simulation). The book is written as a text for courses in computer science, computer engineering, IT, electronic engineering, and mechatronics, as well as a guide for robot hobbyists and researchers.Table of ContentsPart I, Embedded Systems and Programming Environments.- Robots and Controllers.- Central Processing Unit.- Sensors.- Actuators.- Actuators.- Control.- Multitasking.- Wireless Communication.- Raspberry Pi.- Arduino.- Part II, Mobile Robot Design.- Driving Robots.- Omnidirectional Robots.- Balancing Robots.- Walking Robots.- Autonomous Planes.- Autonomous Vessels and Underwater Vehicles.- Robot Manipulators.- Simulation Systems.- Part III, Mobile Robot Applications.- Localization and Navigation.- Maze Exploration.- Map Generation.- Real-Time Image Processing.- Robot Soccer.- Neural Networks.- Evolutionary Computing Approaches.- Behavior-Based Systems.- Evolution of Walking Gaits.- Automotive Systems.- Outlook.- Appendices, Index.

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Book SynopsisEntering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence. This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.Table of ContentsPart I Python.- 1 About Python.- 2 What’s Python?.- 3 An Introductory Example.- 4 Basic Python.- 5 Intermediate Python.- 6 Advanced Python.- 7 Python for data analysis.- Part II Artificial Intelligence Basics.- 8 Introduction to artificial intelligence.- 9 Data wrangling.- 10 Regression.- 11 Classification.- 12 Clustering.- 13 Association Rules.- Part III Artificial Intelligence.- Implementations.- 14 Text Mining.- 15 Image Processing.- 16 Convolutional Neural Networks.- 17 Chatbot, Speech and NLP.- 18 Deep Convolutional Generative Adversarial Network.- 19 Neural style transfer.- 20 Reinforcement learning.- 21 References.

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Book SynopsisEntering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence. This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.Table of ContentsPart I Python.- 1 About Python.- 2 What’s Python?.- 3 An Introductory Example.- 4 Basic Python.- 5 Intermediate Python.- 6 Advanced Python.- 7 Python for data analysis.- Part II Artificial Intelligence Basics.- 8 Introduction to artificial intelligence.- 9 Data wrangling.- 10 Regression.- 11 Classification.- 12 Clustering.- 13 Association Rules.- Part III Artificial Intelligence.- Implementations.- 14 Text Mining.- 15 Image Processing.- 16 Convolutional Neural Networks.- 17 Chatbot, Speech and NLP.- 18 Deep Convolutional Generative Adversarial Network.- 19 Neural style transfer.- 20 Reinforcement learning.- 21 References.

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Book SynopsisScience has made great strides in modeling space, time, mass and energy. Yet little attention has been paid to the precise representation of the information ubiquitous in nature.Introduction to Evolutionary Informatics fuses results from complexity modeling and information theory that allow both meaning and design difficulty in nature to be measured in bits. Built on the foundation of a series of peer-reviewed papers published by the authors, the book is written at a level easily understandable to readers with knowledge of rudimentary high school math. Those seeking a quick first read or those not interested in mathematical detail can skip marked sections in the monograph and still experience the impact of this new and exciting model of nature's information.This book is written for enthusiasts in science, engineering and mathematics interested in understanding the essential role of information in closely examined evolution theory.

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Book SynopsisChapter 1. What is learning-based sports analytics?.- Chapter 2. Data acquisition with computer vision.- Chapter 3. Predictive analysis and play evaluation with machine learning.- Chapter 4. Potential play evaluation with learning-based agent modeling.- Chapter 5. Future perspectives and ecosystems.

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

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Book SynopsisA dramatic new future of work in which managers assemble exactly the expertise they need—within minutes.Gone are the days of static organizational charts and staffing based on the manager’s rolodex and intuition. Now you can recruit any expertise you need from a global online network within minutes: an on-demand, on-the-spot expert at the exact moment that you need their help. You can right-size their involvement: some of those experts give a second-opinion or a moment of brainstorming, while others join as full-fledged team members for a sustained collaborative effort.This is the future promised by Flash Teams, a model that The New York Times has already praised for its “revolutionary potential”: a world where experts are available anytime and everywhere, where remote work has become a norm, and where AI is in the loop to guide team decisions. In Flash Teams, award-winning management scholar Melissa Valentine and computer scientist Michael Bernstein chart the opportunities of flash teams and navigate the challenges that teams and managers will face. They distill lessons from their own work assembling and managing flash teams on demand that every manager can learn from so they can successfully use flash teams in their own organizations.Drawing on original research and industry examples, this book will help readers to:•   Recognize and source needed expertise within minutes•   Anticipate and plan team designs with AI•   Redesign team structures on the fly as projects evolveIndustries are already being transformed by this new approach to teaming. Flash Teams arms leaders, managers, and entrepreneurs with the tools they need to accomplish their goals with confidence, speed, and agility.

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Book SynopsisThe industry bible for graphic arts professionals, with pricing guidelines and information on business, ethical, and legal issues in a new, updated edition with essential information on AI, NFTs, social media, and more.

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Book SynopsisArtificial intelligence is everywhere - from self-driving cars, to image generation from text, to the unexpected power of language systems like ChatGPT - yet few people seem to know how it all really works. How AI Works unravels the mysteries of artificial intelligence, without the complex math and unnecessary jargon. You'll learn: the relationship between artificial intelligence, machine learning, and deep learning; the history behind AI and why the artificial intelligence revolution is happening now; how decades of work in symbolic AI failed and opened the door for the emergence of neural networks; what neural networks are, how they are trained, and why all the wonder of modern AI boils down to a simple, repeated unit that knows how to multiply input numbers to produce an output number, and the implications of large language models, like ChatGPT and Bard, on our society - nothing will be the same again. AI isn't magic. If you've ever wondered how it works, what it can do, or why theTrade Review"Succinct and clear-cut . . . a beacon for those wanting to delve into the mesmerizing world of AI, offering a detailed yet comprehensible overview of the evolutionary trajectory of machine learning technologies, from their rudimentary forms to the pioneering entities of today."—Antoine Tardif, CEO, Unite.AI“A tour de force of the rich history of artificial intelligence . . . For beginners, it demystifies AI and is a perfect resource to get up to date with more than six decades of research and development. . . . Even AI experts will gain a fresh perspective, enhancing their understanding and ability to articulate complex concepts."—Ben Dickson, Software Engineer, Editor of TechTalks"Kneusel does a superb job of explaining the evolution and intricacies of artificial intelligence with a combination of clarity and wit . . . Anyone who wants to understand the concepts of artificial intelligence, as well as its limitations and possible pitfalls, along with the tremendous potential it has to transform technology and society over time should read How AI Works."—Tony Bradley, Editor-in-Chief, TechSpective“A friendly and personal peek behind the curtain of modern AI. Ronald T. Kneusel tells the story of how the field grew, and surveys the ideas that are powering the AI revolution. From this book, you'll learn not only how AI works today, but its limits, its capabilities, and where it might take us tomorrow.”—Andrew Glassner, Author of Deep Learning: A Visual Approach"A must-read for anyone wishing to dig into AI without getting lost in the weeds. Kneusel has succeeded in explaining how AI works to a layperson like myself."—Kenneth Gass, Honorary Curator of Geology, Milwaukee Public Museum“After reading this book, I have a better understanding of the ML tools I have already used in my work . . . I recommend this book to anyone who works with software systems, including management, and anyone who just wants to know what AI actually does under the hood.”—Daniel Kosey, CISSP, Cybersecurity Engineer"If all the hype and reporting about AI has piqued your curiosity, but you don’t want to wade through the math and equations, Ron Kneusel’s latest book is for you. . . . You will no doubt learn a lot as you make your way from the earliest ideas about AI to today’s best models that are knocking at the door of True AI."—David Gorodetzky, Senior Scientist, Machine Learning and Remote Sensing"An excellent starting point for the enthusiast and anyone who wants to know what AI is and will be. . . . The author has another hit science book on a fascinating and apropos topic."—David S. Mazel, Principal/Manager Systems Engineer, Regulus-Group "AI isn't magic. If you've ever wondered how it works, what it can do, or why there's so much hype, How AI Works will teach you everything you want to know."—Midwest Book ReviewTable of ContentsAcknowledgmentsPrefaceChapter 1: And Away We Go: An AI OverviewChapter 2: Why Now? A History of AIChapter 3: Classical Models: Old-School Machine LearningChapter 4: Neural Networks: Brain-Like AIChapter 5: Convolutional Neural Networks: AI Learns to SeeChapter 6: Generative AI: AI Gets Creative Chapter 7: Large Language Models: True AI at Last?Chapter 8: Musings: The Implications of AIGlossaryResourcesIndex

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Book SynopsisTable of ContentsForeword vii Preface xi Acknowledgments xv Introduction 3 Chapter 1: A Primer on Modern AI 13 Chapter 2: Fair and Impartial 23 Chapter 3: Robust and Reliable 43 Chapter 4: Transparent 61 Chapter 5: Explainable 75 Chapter 6: Secure 93 Chapter 7: Safe 111 Chapter 8: Privacy 127 Chapter 9: Accountable 147 Chapter 10: Responsible 161 Chapter 11: Trustworthy AI in Practice 175 Chapter 12: Looking Forward 187 Notes 191 Index 199

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Book SynopsisKevin Wheeler is a HR, talent acquisition and L&D consultant specializing in advising global firms on how to improve their recruitment function. Based in Fremont, California, he is a frequent keynote speaker and industry writer.Bas van de Haterd is a professional speaker, consultant and trainer with a particular focus on using technology to improve recruitment. Based in Soest, The Netherlands, he has more than 20 years' experience and his consulting clients include Dutch Rail, Dutch Post and Twente University.Trade Review"With Talent Acquisition Excellence, Kevin and Bas offer a timely and astute look at how we are in a revolutionary period in recruiting. Blending keen insights with compelling examples, this book illuminates the future for talent leaders seeking to drive innovation and results. Required reading for anyone aiming to master modern recruiting's tools and tactics." * Toby Culshaw, Head of Talent Intelligence, Amazon Stores *"People teams worldwide are wrestling with the implications of divergent yet intersecting trends in the gig economy, shift to remote, cloud & collaboration, profound social and cultural change, the arrival of transformation technology in generative artificial intelligence, all amidst an unstable and increasingly worrisome geopolitical environment. This is a timely book from Kevin Wheeler and Bas van de Haterd, two of the most original and iconoclastic thinkers in the world of TA / HR, who pull together these diverse threads into a concise, imaginative and persuasive narrative." * Hung Lee, Curator Recruiting Brainfood *"This book is an insightful exploration of how the world of work is evolving and how the field of recruiting needs to adapt alongside it. Few authors can bring this to light with the credibility and bonafide of Kevin Wheeler and Bas van de Haterd. The depth of their collective experience is on display throughout each page as they explore the evolving dynamics of the workplace, seamlessly blending insights on technology, human psychology and the art of recruitment. The book covers an impressive spectrum, from exploring the role of algorithms and analytics in shaping recruitment strategies to the human-centric aspects of workforce planning and talent attraction. It is a guide, a reflection of decades of earned wisdom and a strategic tool all rolled into one." * Lars Schmidt, Founder & CEO Amplify Talent *"In Talent Acquisition Excellence, Bas van de Haterd and Kevin Wheeler set out a compelling and powerful template for talent acquisition in the age of AI, analytics and complex talent ecosystems. I particularly enjoyed the scenarios laid out in the book, which provide a compelling treatise on how strategy, technology, process and data can be interwoven together to build a world-class talent acquisition function. A must-read for recruiters, HR professionals and business leaders alike." * David Green, Managing Partner at Insight222, co-author of Excellence in People Analytics, and host of the Digital HR Leaders podcast. *"Talent Acquisition Excellence is a pragmatic guide for a TA or HR leader on what to consider when building a future-proof TA function from scratch. The authors use results and scientific data to understand what's integral to a best practice TA function and are not afraid to conjecture on how this will play out in the future with a mixture of recent real-life examples and near future projections to inspire HR and TA leaders. I welcome the thought-provoking discussion this forward-looking book will inspire and influence Talent Acquisition development for years to come." * Andrea Marston, Senior Director Global Talent Acquisition, VMware *"Kevin and Bas offer a balanced perspective that underscores how the universal themes and best practices of Talent Acquisition transcend industry sectors, languages, and borders across the globe. It's a treasure trove of wisdom, filled with real-world examples and insights. A must-read for TA leaders seeking practical answers and thought-provoking insights." * Shelley Billinghurst & Serge Boudreau, The Recruitment Flex Podcast *"Embark on a transformative odyssey alongside the visionary insights of Bas and Kevin, the oracles of talent acquisition. In their meticulously crafted guide, business leaders will discover the strategic power of talent acquisition, gaining profound insights into the future of recruitment. Peer into the crystal ball of emerging trends, technologies and strategies that will sculpt the hiring landscape. Stay ahead, triumph in the war for talent, elevate your leadership, make informed decisions and propel your organization to unprecedented success!" * Tash Johnston, Talent Acquisition Leader *"A timely and profound exploration of how to attract and retain top talent in today's rapidly evolving work environment. Talent Acquisition Excellence offers a masterclass in recruitment strategy, providing state-of-art perspectives and guidance." * Ross Dawson, Futurist, Keynote Speaker and Author of "Thriving on Chaos", Chairman, Advanced Human Technologies Group *"In an industry typically heavily influenced by vendors setting the tone for what TA excellence means, Kevin & Bas have curated an independent & refreshing take on navigating our ever-evolving landscape of recruiting & hiring practices. Your new 'go-to' recruiting resource is a blueprint for innovative approaches for success in sourcing, engaging, & securing top talent. This book is packed with punchy narratives, cool charts & contemporary insights that have practical application. leveraging real-world scenarios, useful tools and meaningful data it's a comprehensive guide that will resonate and provoke thought, as well as enable recruiters and hiring partners. A must-read for anyone aiming to keep ahead of the curve or elevate their recruitment game in the times ahead!" * Tracy Quinn, APAC Regional Head of Talent Acquisition *"Talent Acquisition Excellence is a masterful orientation to the current state of the art in TA with a big look at what we can expect next. Kevin and Bas combine their broad knowledge of international trends, and Kevin, as always, considers the grand sweep of history before making predictions about the future. It's practical, smart and full of concrete examples. I wish I had this book ten years ago!" * Dart Lindsley, Strategic Advisor, People Experience, Google, Speaker and host of Work For Humans *"This is a vital book for any talent acquisition professional looking to get ahead of the curve. These are incredibly disruptive times for the recruiting function with challenging talent markets, exponential technological advances, and fast-changing business needs that make it impossible to know what skills will be needed and when. Talent Acquisition Excellence provides a roadmap to the radically changed talent acquisition function of the future. The book is visionary but at the same time also accessible, pragmatic and practical." * Matt Alder, Host, The Recruiting Future Podcast *"Talent Acquisition Excellence provides a clear roadmap for building a more effective and efficient talent supply chain that is capable of meeting your organization's current and future talent needs. This book is a must read for any HR leader looking for practical advice and real-world examples for how to transform the recruiting function into a strategic capability." * Thomas Bertels, Founder and President, Purpose Works Consulting *"Talent Acquisition Excellence by Kevin Wheeler and Bas van de Haterd is a game-changer in recruitment literature. It stands out with its clear, no-nonsense approach, directly addressing how digital tools and analytics can revolutionize hiring. The authors, renowned experts in the field, effectively combine recruitment fundamentals with emerging technologies. The book offers unique insights and real-world case studies, making complex concepts accessible and applicable. This book is especially valuable for HR professionals and recruiters looking to leverage technology for smarter, more effective talent acquisition. Readers can expect to gain practical strategies and see tangible improvements in their recruitment outcomes. It's a concise and impactful read, essential for staying ahead in the modern recruitment landscape." * Felix Wetzel, Vice President, Product Management, Cielo *Table of Contents Chapter - 00: Introduction – what’s happening to work?; Chapter - 01: The emerging talent ecosystem; Chapter - 02: Technology and talent acquisition; Chapter - 03: Talent supply chain thinking; Chapter - 04: Talent attraction; Chapter - 05: Selection; Chapter - 06: RPO or in-house?; Chapter - 07: A radical new model for talent acquisition; Chapter - 08: ‘What hath God wrought?’;

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Book SynopsisWith this practical book, practitioners, students, and academics in both finance and data science will learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading.

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Book SynopsisRecent technological advances have resulted in a new generation of powerful text processing and generation tools, that produce text very similar to what a human could do. Such tools have the potential to change the way we work, learn and teach – and this book enables you to understand and be part of this exciting development. Did you wonder how tools like ChatGPT or Bard work, but do not have a technical background? Then this is the right book for you! To discuss and better understand what such technologies are capable of and how this will impact our lives and our society, a basic background knowledge about text processing and generation technologies is required. In particular, the book discusses the following questions: How did the field of automated text processing and generation evolve over the last years, and what happened to allow the incredible recent advances? Are chatbots such as ChatGPT or Bard truly understanding humans? What pitfalls exist and how are stereotypes of the society reflected in such models? What is the potential of such technology, and how will the digital society of the future look like in terms of human-chatbot-collaboration? The book is aimed for a general audience, briefly explaining mathematical or technical background when necessary. After having read this book, you will be confident to participate in public discussions about how this new generation of language models will impact society. You will be aware of the risks and pitfalls these technologies can bring along, and how to deal responsibly when making use of tools built from AI technology in general.Trade Review“Mascha Kurpicz-Briki’s book takes readers under the hood of today’s chatbots by explaining how LLMs work and looks through the hype. ... Her guide to how LLMs work steps through all of the need-to-know concepts underpinning natural language processing. And by the end of the journey, readers will have a clear understanding of what it takes for computers to process written text.” (James Tyrrell, T_HQ technology and business, techhq.com, December 20, 2023)Table of Contents1. Introduction.- 2. An Introduction to Machine Learning.- 3. Processing Written Language.- 4. Do Chatbots Have Emotions?.- 5. The True and the False.- 6. Stereotypes in Language Models.- 7. The Future of Humans and Language Models.

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Book SynopsisWhy and when to use a chatbot.- Good human writing.- Avoiding Typical Errors in Human Writing: Can Chatbots Help?.- Beyond common uses: the power and utility of chatbots.- How to sound natural and unbiased.- Exploiting, rather than rejecting, a chatbot's negative feedback on your work.- AI detectors and humanizers.- Hallucination, plausibility, confirmation bias, and over-reliance on chatbots.- Paraphrasing and plagiarism.- When are chatbots possibly better than humans.

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Book SynopsisThe future of AI is here.The world is transfixed by the marvel (and possible menace) of ChatGPT and other generative AI tools. It's clear Gen AI will transform the business landscape, but when and how much remain to be seen. Meanwhile, your smartest competitors are already navigating the risks and reaping the rewards of these new technologies. They're experimenting with new business models around generating text, images, and code at astonishing speed. They're automating customer interactions in ways never before possible. And they're augmenting human creativity in order to innovate faster. How can you take advantage of generative AI and avoid having your business disrupted?Generative AI: The Insights You Need from Harvard Business Review will help you understand the potential of these new technologies, pick the right Gen AI projects, and reinvent your business for the new age of AI.Business is changing. Will you adapt or be left behind?Get up to speed and deepen your understanding of the topics that are shaping your company's future with the Insights You Need from Harvard Business Review series. Featuring HBR's smartest thinking on fast-moving issues—blockchain, cybersecurity, AI, and more—each book provides the foundational introduction and practical case studies your organization needs to compete today and collects the best research, interviews, and analysis to get it ready for tomorrow.You can't afford to ignore how these issues will transform the landscape of business and society. The Insights You Need series will help you grasp these critical ideas—and prepare you and your company for the future.

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Book Synopsis'This colourful page-turner puts artificial intelligence into a human perspective . . . Metz explains this transformative technology and makes the quest thrilling.' Walter Isaacson, author of Steve Jobs____________________________________________________This is the inside story of a small group of mavericks, eccentrics and geniuses who turned Artificial Intelligence from a fringe enthusiasm into a transformative technology. It's the story of how that technology became big business, creating vast fortunes and sparking intense rivalries. And it's the story of breakneck advances that will shape our lives for many decades to come - both for good and for ill. ________________________________________________'One day soon, when computers are safely driving our roads and speaking to us in complete sentences, we'll look back at Cade Metz's elegant, sweeping Genius Makers as their birth story - the Genesis for an age of sentient machines.' Brad Stone, author of The Everything Store and The Upstarts'A ringside seat at what may turn out to be the pivotal episode in human history . . . easy and fun to read . . . undeniably charming.' ForbesTrade ReviewIn Genius Makers, Cade Metz delivers the definitive take on how AI technology came to be and what its arrival will mean for us humans. The book relies on tireless reporting and delightful writing to bring to life one of the most surprising and important stories of our time. If you want to read one book to understand AI, this is the one. -- Ashlee Vance, New York Times bestselling author of ELON MUSKThis colourful page-turner puts artificial intelligence into a human perspective. Through the lives of Geoff Hinton and other major players, Metz explains this transformative technology and makes the quest thrilling. -- Walter Isaacson, #1 New York Times bestselling author of LEONARDO DA VINCI, STEVE JOBS and THE INNOVATORSCade Metz has produced an enthralling narrative of the advance of artificial intelligence. He describes the key personalities, the seminal meetings and the crucial breakthroughs with his customary eye for detail, building them into a dramatic history of this era-defining technology. -- Kai-Fu Lee, author of AI SUPERPOWERSThis is the inside story of how AI entered Google, Facebook, and the rest of high tech. It is also the story of how Silicon Valley and its mega-bucks infiltrated AI and changed its course. Chock full of behind-the-scenes anecdotes and wry humour - we learn the true tale of the technology that is transforming humanity. -- Oren Etzioni, chief executive, Allen Institute for Artificial IntelligenceOne day soon, when computers are safely driving our roads and speaking to us in complete sentences, we'll look back at Cade Metz's elegant, sweeping Genius Makers as their birth story - the Genesis for an age of sentient machines. -- Brad Stone, author of THE EVERYTHING STORE and THE UPSTARTS

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Book SynopsisDeep learning from the ground up using R and the powerful Keras library! In Deep Learning with R, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Deep Learning with R, Second Edition shows you how to put deep learning into action. It's based on the revised new edition of François Chollet's bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks. about the technology Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R. what's inside Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation about the reader For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required.

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Book SynopsisTable of Contents1. Programming in Python 2. Machine Learning Basics 3. Regression 4. Classification 5. Dimensionality reduction 6. Clustering 7. Ensemble methods 8. Feature extraction and selection 9. Introduction to Deep Learning 10. Neural Networks 11. Convolutional Neural Networks

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