Artificial intelligence (AI) Books

Book SynopsisFeeling uneasy about AI? Worried it'll make your job obsolete and your once-solid training outdated? You're not alone.

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Book SynopsisDino Esposito is CTO and co-founder of Crionet, a company that provides innovative software and technology to professional sports organizations. A 16-time Microsoft MVP, he has authored 20+ books, including Introducing Machine Learning; and the Microsoft Press best-seller Microsoft .NET: Architecting Applications for the Enterprise. Francesco Esposito holds a degree in Mathematics, is the co-author of Introducing Machine Learning, and lives suspended between the depth of advanced mathematics and the intrigue of data science. He currently serves as the Head of Engineering and Data at Crionet. As an entrepreneur he founded Youbiquitous, a data analysis and software factory, and KBMS Data Force, a startup in Digital Therapy and intelligent healthcare.Table of ContentsCHAPTER 1 Artificially Intelligent Software CHAPTER 2 An Architectural Perspective of ML.NET CHAPTER 3 The Foundation of ML.NET CHAPTER 4 Prediction Tasks CHAPTER 5 Classification Tasks CHAPTER 6 Clustering Tasks CHAPTER 7 Anomaly Detection Tasks CHAPTER 8 Forecasting Tasks CHAPTER 9 Recommendation Tasks CHAPTER 10 Image Classification Tasks CHAPTER 11 Overview of Neural Networks CHAPTER 12 A Neural Network to Recognize Passports APPENDIX A Model Explainability

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Book SynopsisIn Robots We Trust explores the evolving relationship between humans and intelligent machines, focusing on the critical issue of trust. As robots become more integrated into our daily lives-from self-driving cars to delivery drones-our society faces two critical questions: can we trust them, and can they trust us?

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Book SynopsisA proposal that we think about digital technologies such as machine learning not in terms of artificial intelligence but as artificial communication.Algorithms that work with deep learning and big data are getting so much better at doing so many things that it makes us uncomfortable. How can a device know what our favorite songs are, or what we should write in an email? Have machines become too smart? In Artificial Communication, Elena Esposito argues that drawing this sort of analogy between algorithms and human intelligence is misleading. If machines contribute to social intelligence, it will not be because they have learned how to think like us but because we have learned how to communicate with them. Esposito proposes that we think of “smart” machines not in terms of artificial intelligence but in terms of artificial communication. To do this, we need a concept of communication that can take in

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Book SynopsisAn accessible explanation of the technologies that enable such popular voice-interactive applications as Alexa, Siri, and Google Assistant.Have you talked to a machine lately? Asked Alexa to play a song, asked Siri to call a friend, asked Google Assistant to make a shopping list? This volume in the MIT Press Essential Knowledge series offers a nontechnical and accessible explanation of the technologies that enable these popular devices. Roberto Pieraccini, drawing on more than thirty years of experience at companies including Bell Labs, IBM, and Google, describes the developments in such fields as artificial intelligence, machine learning, speech recognition, and natural language understanding that allow us to outsource tasks to our ubiquitous virtual assistants.Pieraccini describes the software components that enable spoken communication between humans and computers, and explains why it's so difficult to build machines that understand humans. He explains speech reco

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Book SynopsisGeorge Luger is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico. He received his PhD from the University of Pennsylvania and spent five years researching and teaching at the Department of Artificial Intelligence at the University of Edinburgh.Table of Contents PART I: ARTIFICIAL INTELLIGENCE: ITS ROOTS AND SCOPE 1 AI: HISTORY AND APPLICATIONS PART II: ARTIFICIAL INTELLIGENCE AS REPRESENTATION AND SEARCH 2 THE PREDICATE CALCULUS 3 STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH 4 HEURISTIC SEARCH 5 STOCHASTIC METHODS 6 CONTROL AND IMPLEMENTATION OF STATE SPACE SEARCH PART III: CAPTURING INTELLIGENCE: THE AI CHALLENGE 7 KNOWLEDGE REPRESENTATION 8 STRONG METHOD PROBLEM SOLVING 9 REASONING IN UNCERTAIN SITUATIONS PART IV: MACHINE LEARNING 10 MACHINE LEARNING: SYMBOL-BASED 11 MACHINE LEARNING: CONNECTIONIST 12 MACHINE LEARNING: GENETIC AND EMERGENT 13 MACHINE LEARNING: PROBABILISTIC PART V: ADVANCED TOPICS FOR AI PROBLEM SOLVING 14 AUTOMATED REASONING 15 UNDERSTANDING NATURAL LANGUAGE PART VI 16 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY Bibliography Author Index Subject Index

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Book SynopsisThe book is based on the best papers of IEEE IRI 2018 and IEEE FMI 2018, Salt Lake City, July, 2018. They have been enhanced and modified suitably for publication. The book comprises recent works covering several aspects of reuse in intelligent systems including Scientific Theory and Technology-Based Applications. New data analytic algorithms, technologies, and tools are sought to be able to manage, integrate, and utilize large amounts of data despite hardware, software, and/or bandwidth constraints; to construct models yielding important data insights, and to create visualizations to aid in presenting and understanding the data. Furthermore, it addresses the representation, cleansing, generalization, validation, and reasoning strategies for the scientifically-sound and cost-effective advancement of all kinds of intelligent systems including all software and hardware aspects. The book addresses problems such as, how to optimally select the information/data sets for reuse and Table of ContentsExperimental Studies on the Impact of Data Sampling with Severely Imbalanced big data. How to Optimally Combine Univariate and Multivariate Feature Selection with Data Sampling for Classifying Noisy, High Dimensional and Class Imbalanced DNA Microarray Data. Big Data and Class Imbalance in Medicare Fraud Detection. Movie Recommendations Based on a Recurrent Neural Network Model. A Recommendation System Enhanced by Topic Modeling for Knowledge Reuse in MOOCs Ecosystems. Toward a Computer Vision Based Approach for Developing Algorithms for Soccer Playing Robots. Context-dependent Reachability Analysis for Hybrid Systems. Netflow Feature Evaluation for the Detection of Slow Read HTTP Attacks. Predictive Analysis of Server Log Data for Forecasting Events.

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Book SynopsisIt can be used for a course that provides an introduction to basic functional analysis, approximation theory, and numerical analysis, while building upon and applying basic techniques of real variable theory.Trade ReviewSecond Edition S.C. Brenner and L.R. Scott The Mathematical Theory of Finite Element Methods "[This is] a well-written book. A great deal of material is covered, and students who have taken the trouble to master at least some of the advanced material in the later chapters would be well placed to embark on research in the area." ZENTRALBLATT MATH From the reviews of the third edition: "An excelent survey of the deep mathematical roots of finite element methods as well as of some of the newest and most formal results concerning these methods. … The approach remains very clear and precise … . A significant number of examples and exercises improve considerably the accessability of the text. The authors also point out different ways the book could be used in various courses. … valuable reference and source for researchers (mainly mathematicians) in the topic." (Calin Ioan Gheorghiu, Zentralblatt MATH, Vol. 1135 (13), 2008)Table of ContentsPreface(3rdEd).- Preface(2ndEd).- Preface(1stED).- Basic Concepts.- Sobolev Spaces.- Variational Formulation of Elliptic Boundary Value Problems.- The Construction of a Finite Element of Space.- Polynomial Approximation Theory in Sobolev Spaces.- n-Dimensional Variational Problems.- Finite Element Multigrid Methods.- Additive Schwarz Preconditioners.- Max-norm Estimates.- Adaptive Meshes.- Variational Crimes.- Applications to Planar Elasticity.- Mixed Methods.- Iterative Techniques for Mixed Methods.- Applications of Operator-Interpolation Theory.- References.- Index.

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Book SynopsisCould a computer have a mind? What kind of machine would this be? Exactly what do we mean by ''mind'' anyway?The notion of the ''intelligent'' machine, whilst continuing to feature in numerous entertaining and frightening fictions, has also been the focus of a serious and dedicated research tradition. Reflecting on these fictions, and on the research tradition that pursues ''Artificial Intelligence'', raises a number of vexing philosophical issues. Minds and Computers introduces readers to these issues by offering an engaging, coherent, and highly approachable interdisciplinary introduction to the Philosophy of Artificial Intelligence.Readers are presented with introductory material from each of the disciplines which constitute Cognitive Science: Philosophy, Neuroscience, Psychology, Computer Science, and Linguistics. Throughout, readers are encouraged to consider the implications of this disparate and wide-ranging material for the possibility of developing machines with minds. And they can expect to de

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Book SynopsisArtificial intelligence (AI) is increasingly impacting many aspects of peopleâs lives across the globe, from relatively mundane technology to more advanced digital systems that can make their own decisions. While AI has great potential, it also holds great peril depending on how it is designed and used. AI for Diversity questions how AI technology can lead to inclusion or exclusion for diverse groups in society. The way data is selected, trained, used, and embedded into societies can have unfortunate consequences unless we critically investigate the dangers of systems left unchecked, and can lead to misogynistic, homophobic, racist, ageist, transphobic, or ableist outcomes. This book encourages the reader to take a step back to see how AI is impacting diverse groups of people and how diversity-awareness strategies can impact AI.Trade Review"The book is written in a really approachable way for non-specialists and will engage introductory and interdisciplinary audiences. The sections on gender and queering AI are particularly strong, and the book is a highly worthy and important contribution for those chapters alone." --Ashley Shew, Associate Professor, Virginia TechTable of Contents1.Opening the Black Box of AI. 2. Gendered AI: performativity, expectations, and sexism. 3. Queering AI: gender expression, identity, and binaries. 4. AI and Race: recognition, bias, and systemic issues. 5. Bodies and AI: Health, ageing, and disabilities. 6. AI and Class: socioeconomic issues reproduced by technology. 7. Intersectionality and Responsible AI.

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

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Book SynopsisMost of the research aimed at counterterrorism, fraud detection, or other forensic applications assumes that this is a specialized application domain for mainstream knowledge discovery. Unfortunately, knowledge discovery changes completely when the datasets being used have been manipulated in order to conceal some underlying activity. Knowledge Discovery for Counterterrorism and Law Enforcement operates from the premise that detection algorithms must be rethought to be effective in this domain, and presents a new approach based on cutting-edge analysis for use in adversarial settings.Reveals How Criminals Conceal Information This volume focuses on four main forms of knowledge discovery: prediction, clustering, relationship discovery, and textual analysis. For each of these application areas, the author discusses opportunities for concealment that are available to crimiTrade ReviewThe writing is organized, clear, and free of technical jargon and mathematical notation. ... In summary, this is a highly readable introduction to an interesting field. While it is certainly a valuable introductory resource for the technically adept, it is also appropriate for those with the desire to understand the applicability, potential, and limitations of computer-assisted knowledge discovery techniques in counterterrorism and law enforcement. -Computing Reviews, September 2009Table of ContentsIntroduction. Data. High-Level Principles. Looking for Risk - Prediction and Anomaly Detection. Looking for Similarity – Clustering. Looking Inside Groups - Relationship Discovery. Discovery from Public Textual Data. Discovery in Private Communication. Discovering Mental and Emotional State. The Bottom Line.

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Book SynopsisComputational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book: Explains how reputation-based systems are used to determine trust in diverse online communities Describes how machine learning techniques are employed to build robust reputation systems Explores two distinctive approaches to determining credibility of resourcesone where the human role is implicit, and one that leverages human input explicitly Shows how decision support can be facilitated by computational trust models Discusses collaborative filtering-based trust aware recommendation systems Defines a framework for translating a trust modeling problem into a learning problem<Table of ContentsIntroduction. Trust in Online Communities. Judging the Veracity of Claims and Reliability of Sources with Fact-Finders. Web Credibility Assessment. Trust-Aware Recommender Systems. Biases in Trust-Based Systems.

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Book SynopsisA social robot is a robot that interacts and communicates with humans or other autonomous physical agents by following social behaviors and rules attached to its role. We seem to accept the use of robots that perform dull, dirty, and dangerous jobs. But how far do we want to go with the automation of care for children and the elderly, or the killing of terrorists? Would we be setting humanity aside if we accepted such automation?Just Ordinary Robots: Automation from Love to War provides a socially involved, yet sober, view into the new age of robots. It supplies a cutting-edge look at robot technologies, including what these technologies are capable of and the ethical and regulatory questions they raise.The book surveys the various types of social robots and examines their social significance in homes, health care, traffic, the police, and the military. Considering the technical characteristics and societal expectations of robots in these areaTrade Review"Just Ordinary Robots: Automation from Love to War examines the social significance of the new generation of five types of robots: the home robot, the care robot, police and private drones, the car robot, and the military robot. ... This book is the result of many years of research by the Rathenau Institute, The Netherlands’ key research and debating center for science, technology, and society. In 2012, this research led to the publication of the Dutch book Overal Robots (Robots Everywhere), written by Lambèr Royakkers, Floortje Daemen, and Rinie van Est. This book is an updated and drastically revised version of that book."—From the Book’s Foreword by Frans Brom, Head of Technology Assessment, Rathenau Institute, The Hague, The NetherlandsTable of ContentsRobots Everywhere. Home Is Where the Robot Is: Mechanoids, Humanoids, and Androids. Taking Care of Our Parents: The Role of Domotics and Robots. Drones in the City: Toward a Floating Robotic Panopticon? Who Drives the Car? Armed Military Drones: The Ethics behind Various Degrees of Autonomy. Automation from Love to War.

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Book SynopsisImprove the Safety, Flexibility, and Reliability of Autonomous Navigation in Complex EnvironmentsAutonomous Vehicle Navigation: From Behavioral to Hybrid Multi-Controller Architectures explores the use of multi-controller architectures in fully autonomous robot navigationeven in highly dynamic and cluttered environments. Accessible to researchers and graduate students involved in mobile robotics and fully autonomous vehicle navigation, the book presents novel techniques and concepts that address different complex mobile robot tasks.The author examines the development of reliable elementary controllers and proposes mechanisms to manage the interaction of these multi-controller architectures while addressing different constraints and enhancing metrics/criteria linked to the safety, flexibility, and reliability of the proposed control architectures. He covers the modeling of subtasks, reliable obstacle avoidance, appropriate stable controlTable of ContentsGlobal concepts/challenges related to the control of intelligent mobile robots. Autonomous navigation in cluttered environments. HybridCD (continuous/discrete) multi-controller architectures. HybridRC (reactive/cognitive) and homogeneous control architecture based on PELC. Flexible and reliable autonomous vehicle’s navigation using optimal waypoint configuration. Cooperative control of multi-robot systems. General conclusion and prospects. Appendices.

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Book SynopsisVisual Tracking in Conventional Minimally Invasive Surgery introduces the various tools and methodologies that can be used to enhance a conventional surgical setup with some degree of automation. The main focus of this book is on methods for tracking surgical tools and how they can be used to assist the surgeon during the surgical operation. Various notions associated with surgeoncomputer interfaces and image-guided navigation are explored, with a range of experimental results.The book starts with some basic motivations for minimally invasive surgery and states the various distinctions between robotic and non-robotic (conventional) versions of this procedure. Common components of this type of operation are presented with a review of the literature addressing the automation aspects of such a setup. Examples of tracking results are shown for both motion and gesture recognition of surgical tools, which can be used as parTable of ContentsIntroduction. Endoscope Setup and Calibration. Marker-Based Tracking. Marker-less Tracking: Gaussian Type. Marker-lessTracking: Non-Gaussian Type. Reign-Based Tracking. Appendix A: Morphological operation and Neural Network. Appendix B:Adaptive Gaussian Mixture Model. Appendix C: Overview of Particle Filter. Appendix D: Planar Homography. Appendix E:Overview of Region Matching Approaches.

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Book SynopsisHow the autonomous digital forces jolting our lives - as uncontrollable as the weather and plate tectonics - are transforming life, society, culture, and politics.David Auerbach's exploration of the phenomenon he has identified as the meganet begins with a simple, startling revelation: There is no hand on the tiller of some of the largest global digital forces that influence our daily lives: from corporate sites such as Facebook, Amazon, Google, YouTube, Instagram, and Reddit to the burgeoning metaverse encompassing cryptocurrencies and online gaming to government systems such as China's Social Credit System and India's Aadhaar.As we increasingly integrate our society, culture and politics within a hyper-networked fabric, Auerbach explains how the interactions of billions of people with unfathomably large online networks have produced a new sort of beast: ever-changing systems that operate beyond the control of the individuals, companies, and governments that created them.Meganets, Auerbach explains, have a life of their own, actively resisting attempts to control them as they accumulate data and produce spontaneous, unexpected social groups and uprisings that could not have even existed twenty years ago. And they constantly modify themselves in response to user behavior, resulting in collectively authored algorithms none of us intend or control. These enormous invisible organisms exerting great force on our lives are the new minds of the world, increasingly commandeering our daily lives and inner realities.Auerbach's analysis of these gargantuan opaque digital forces yield important insights such as:- The conventional wisdom that the Googles and Facebook of this world are tightly run algorithmic entities is a myth. No one is really in control.- The efforts at reform - to get lies and misinformation off meganets - run into a brick wall because the companies and executives who run them are trapped by the persistent, evolving, and opaque systems they have created.- Bitcoin and other cryptocurrencies are uncontrollable and their embrace by elite financial institutions threatens the entire economy- We are asking the wrong questions in assuming that if only the Facebooks of this world could be better regulated or broken up that they would be better, more ethical citizens- Why questions such as making algorithms fair and bias-free and whether AI can be a tool for good or evil are wrong and misinformedAuerbach then comes full circle, showing that while we cannot ultimately control meganets we can tame them through the counterintuitive measures he describes in detail.

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Book SynopsisCompanies everywhere are moving everyday business processes over to the cloud, and AI is increasingly being given the reins in these tasks. As this massive digital transformation continues, the combination of serverless computing and AI promises to become the de facto standard for business-to-consumer platform development—and developers who can design, develop, implement, and maintain these systems will be in high demand! AI as a Service is a practical handbook to building and implementing serverless AI applications, without bogging you down with a lot of theory. Instead, you’ll find easy-to-digest instruction and two complete hands-on serverless AI builds in this must-have guide! Key features Cloud AI from development to production Applying cloud AI services to your existing platform Understanding orchestration patterns for cloud AI systems How to architect and build scalable, resilient data pipelines Audience For software developers with intermediate skills in at least one programming language and a basic understanding of IP networking and HTTP protocol. About the technology Artificial Intelligence (AI), a machine’s ability to learn and make predictions based on patterns it identifies, is already being leveraged by businesses around the world in areas like targeted product recommendations, financial forecasting and resource planning, customer service chatbots, healthcare diagnostics, data security, and more. Peter Elger is the founder and CEO of fourTheorem, a software solutions company providing expertise on architecture, development, DevOps, and machine learning. He is also the cofounder and CTO of two other companies: nearForm, a Node.js consultancy, and StitcherAds, a digital advertising platform. He is a regular conference speaker and coauthor of The Node Cookbook as well as several academic papers. Eoin Shanaghy is the cofounder and CTO fourTheorem and the founder of Showpiper, a machine learning-driven video content marketing startup. He previously created real-time trading applications for Fortis Bank and developed Hewlett-Packard's enterprise application server as well as 3G network management systems for Ericsson and Arantech (now Netscout). Johannes Ahlmann is the founder and CEO of Sensatus.io, a Machine Learning company specialized in large-scale Web Crawling, Market Intelligence and On-Premise AI solutions. He previously created Information Extraction solutions as Head of Data Science at ScrapingHub, and created responsive mobile solutions at Dell EMC.

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Book Synopsis"Do you want to learn how to creat your own virtual agent? If so, have I got the book for you!" - Thomas Joseph Heiman Design, develop, and deploy human-like AI solutions that chat with your customers, solve their problems, and streamline your support services. Creating Virtual Assistants: Chatbots that work teaches you to create the kind of AI-enabled chatbots that are revolutionizing the customer service industry. You'll learn to build effective conversational AI that can automate common inquiries and easily address your customers' most common problems. In Creating Virtual Assistants, you will learn how to: Pick the right virtual assistant type and channel for your needsWrite dialog with intentional tone and specificityTrain a virtual assistant classifier from the ground upCreate question-and-direct-response assistantsDesign and optimize a process flow for web and voiceTest your assistant's accuracy and plan out improvements This engaging and entertaining book delivers the essential technical and creative skills for designing successful virtual assistants, from coding process flows and training machine learning, to improving your written dialog. about the technologyVirtual assistants multiply the effectiveness of customer-facing communication. Great chatbots automate common tasks like gathering information, responding to questions, and improving product selections so human staff can work on more challenging problems. These powerful tools range from event classifiers that can act on an email message to conversational assistants that actually talk to your users! about the bookCreating Virtual Assistants is a hands-on guide to building custom virtual assistants for a wide variety of use cases. You'll dive right into developing an assistant capable of identifying top user requests and making a suitable response for each request type. Once you've got the basics, you'll master a multi-step process flow that can be easily adapted for both text and voice assistants. You'll learn how to hone your assistant by writing better dialog, training and improving its underlying machine learning, and spotting issues like abandonment and underperformance. By the time you're done, you'll be ready to deploy anything from a simple chatbot to a complex call center tool. about the readerFor software developers. Examples in Watson Assistant and in Python. about the authorAndrew R. Freed is a Master Inventor and Senior Technical Staff Member at IBM. He's the co-host of the AI Zen podcast. Andrew has extensive experience deploying high-volume virtual assistants and has been working in AI solutions since 2012.Trade Review"A thorough guide to the entire process of designing and implementing virtual assistants. Goes way beyond the technicalities." Maxim Volgin, KLM "All you need to know about building your own virtual assistant." Fernando Antonio da Silva Bernardino, TransferWise "Learn how to build virtual assistants in all platforms; IBM Watson, but also Azure and AWS." Jose Luis Perez, PolyhedraTech "All the steps for developing a real-world virtual assistant without overwhelming you with complex technical details." Sowmya Vajjala, National Research Council, Canada

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Book SynopsisThis volume helps to fill the gap between data analytics, image processing, and soft computing practices. Soft computing methods are used to focus on data analytics and image processing to develop good intelligent systems. To this end, readers of this volume will find quality research that presents the current trends, advanced methods, and hybridized techniques relating to data analytics and intelligent systems. The book also features case studies related to medical diagnosis with the use of image processing and soft computing algorithms in particular models. Providing extensive coverage of biometric systems, soft computing, image processing, artificial intelligence, and data analytics, the chapter authors discuss the latest research issues, present solutions to research problems, and look at comparative analysis with earlier results. Topics include some of the most important challenges and discoveries in intelligent systems today, such as computer vision concepts and image identification, data analysis and computational paradigms, deep learning techniques, face and speaker recognition systems, and more.Table of ContentsPart 1: Biometric Systems And Image Processing 1. Intelligent Techniques: An Overview 2. A Survey on Artificial Intelligence Techniques Used in Bio-Metric Systems 3. Speech-Based Biometric Using Odia Phonetics 4. Deep Learning Techniques to Classify and Analyze Medical Imaging Data 5. Face Recognition System: An Overview 6. An Overview on the Concept of Speaker Recognition 7. Analysis of a Unimodal and Multimodal Biometric System Part 2: Soft Computing And Data Analytics 8. A Heuristic Approach of Parameter Tuning in a Smote-Based Preprocessing Algorithm for Imbalanced Ordinal Classification 9. Aspects of Deep Learning: Hyper-Parameter Tuning, Regularization, and Normalization 10. Super-Resolution of Reconstruction of Infrared Images Adopting Counter Neural Networks 11. High-End Tools and Technologies for Managing Data in the Age of Big Data 12. An AI-Based Chatbot Using Deep Learning Part 3: Intelligent Systems And Hybrid Systems 13. A Real-Time Data Analytics-Based Crop Diseases Recognition System 14. Image Caption Generation with Beam Search

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Book SynopsisAI IS SMART; YOU ARE SMARTER! AI has been termed as the fourth industrial revolution; it will transform the way you live and work. It can enrich your life but also threaten your privacy. It can give you fantastic insight, support, and more quality time, but also take your job. You++ is an easy to read guide that explains what AI can, and cannot do, using real world examples. It provides simple strategies you can adopt to keep ahead of the game. You++ can help you realise new opportunities whilst mitigating the downside risks.

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Book Synopsis** A Granta Book of the Year 2023 **'Raises the stakes for the rest of us writers' ISABEL WAIDNER'An important new talent' FIONA SHAW'Curious, queer, whip-smart, hilarious and tender' GAIL MCCONNELLA living exploration of undoing and redoing queer single parenting and love, in conversation with an AI algorithm and a toddler.As Hannah Silva navigates friendship, dating and life as a queer single parent in London, her toddler and the algorithm contribute humour, play and insight. With the help/disruption of these unreliable narrators, Hannah deconstructs her story, and constructs a new one. She unravels everything she has been taught to want, finding alternative ways of thinking, loving and parenting today.Queer, creative, sexy and compassionate, My Child, the Algorithm is non-fiction at its finest.Trade ReviewMy Child, the Algorithm redoes what autobiography is. Funny, inventive and moving, it raises the stakes for the rest of us writers -- Isabel Waidner * author of STERLING KARAT GOLD *Hannah breaks all the rules in order to refresh our minds. No writer I know in drama is as brave or as vulnerable. She talks to another hinterland of the mind. She writes like she is climbing a mountain naked.... an important new talent -- Fiona ShawCurious, queer, whip-smart, hilarious and tender. Just read it. Hannah Silva is my favourite male seahorse -- Gail McConnellA bold and inventive meditation on the complexities of motherhood. I loved it -- Irenosen Okojie * author of NUDIBRANCH *A work worthy to stand beside classics of queer parenting by Maggie Nelson and Gail McConnell, Silva's book asks all the right questions about art, family and love, with clarity, invention and passion -- Joanna Walsh * author of GIRL ONLINE: A USER MANUAL *My Child, the Algorithm is extraordinary, in both its subject matter and in the purpose and power of its prose. This is writing from life's bleeding edge, driven by curiosity, by pain, and at times by howling need. There's light here, too; playfulness, humour, and so much love. Just as children remake their mothers, so this book seems, at times, to remake language itself. I adored it -- Marianne LevyHannah Silva's writing is searing, sharp, moving, and alive to the inanities and absurdities of her worlds. With a poet's ear and a truly poetic eye and playwright's feel for all life's dramas her talent is one to treasure and audio drama's rarest of pleasures -- David Eldridge * dramatist and screenwriter *Hannah Silva lets bodies and rooms be the waxing and waning things of space and line they also are, instead of manoeuvrable substance. How beautiful - this is amazing work -- Anthony (Vahni) CapildeoA brilliant, surprising, funny, moving, unexpected, unique work of genius -- Nadia Molinari

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Book SynopsisThis book deals with the issue of problematic market price prediction in the context of crowd behaviour affected by the psychology of the masses. It highlights the contrast between a phenomenon of mass psychology and the efficient market hypothesis, which is essentially based on a common economic theory. The basic assumption is that if there is a model of interaction between masses and agents participating in markets, then there also exist means for prediction of the whole market's behaviour, though nevertheless the behaviour of every single agent is not predictable. From a practical point of view, this book describes technical analysis methods used to predict price movements, and discusses a soft computing approach used in a composition of automated trading systems. This book brings alternative, soft computing computational models to trading strategies and innovatively combines two different areas of science - artificial intelligence and technical analysis. One of the main benefits of this book is a demonstration that the soft computing approach in a combination with the "soft" social sciences accounts more reliable results than the conventional mathematical models.This book is for anyone interested in trading, financial markets and security exchanges, as well as for those who have theoretical or practical knowledge from the fields of artificial intelligence and soft computing, and want to know how these topics can be applied in financial markets.Table of ContentsIntroduction 1: Reality, the intersection of multiple theories Efficient market hypothesis The theory of chaos Behavioral market theory 2: The dynamics of crowd behavior Methodologies for the study of markets The system theory point of view The wave principle Fibonacci mathematics in financial markets 3: Security exchanges at a glance Financial markets Security exchanges Exchange clearing systems 4: Basic tenets of automated trading Indicators and oscillators Money management Statistics The sensitivity to changes of system parameters 5: Simulation and backtesting of trading strategies The value of simulation in the trading Human factor in the trading chain Modeling of order execution Modeling of time and price skews Discrete Event System Specification Simulation of the trading environment Embedding trading strategies into the simulation Simulation case study 6: Optimization of trading strategies Parametric trading strategies Genetic algorithms Inspiration from nature Computational model of genetic evolution Optimization case study 7: Fuzzy approach to trading strategies Concept of uncertainty and the basics of fuzzy logic theory Fuzzy logic and fuzzy inference Fuzzy-based trading strategies Analysis of sensitivity and robustness Case study Summary Bibliography and further reading Notations, functions and mathematical symbols

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Book SynopsisThis textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies. Table of ContentsNotation xv.- Part 1: Machine Learning, NLP, and Speech Introduction.- Chapter 1 Introduction 1.- Chapter 2 Basics of Machine Learning 2.- Chapter 3 Text and Speech Basics 49.- Part 2: Deep Learning Basics.- Chapter 4 Basics of Deep Learning 105.- Chapter 5 Distributed Representations 213.- Chapter 6 Convolutional Neural Networks 275.- Chapter 7 Recurrent Neural Networks 329.- Chapter 8 Automatic Speech Recognition 387.- Part 3: Advance Deep Learning Techniques for Text and Speech.- Chapter 9 Attention and Memory Augmented Networks 429.- Chapter 10 Transfer learning: Scenarios, Self-Taught Learning, and Multitask Learning 485.- Chapter 11 Transfer Learning: Domain Adaptation 515.- Chapter 12 End-to-end Speech Recognition 559.- Chapter 13 Deep Reinforcement Learning for Text and Speech 601.- Future Outlook 647.

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Book SynopsisThis book provides a comprehensive introduction to Conversational AI. While the idea of interacting with a computer using voice or text goes back a long way, it is only in recent years that this idea has become a reality with the emergence of digital personal assistants, smart speakers, and chatbots. Advances in AI, particularly in deep learning, along with the availability of massive computing power and vast amounts of data, have led to a new generation of dialogue systems and conversational interfaces. Current research in Conversational AI focuses mainly on the application of machine learning and statistical data-driven approaches to the development of dialogue systems. However, it is important to be aware of previous achievements in dialogue technology and to consider to what extent they might be relevant to current research and development. Three main approaches to the development of dialogue systems are reviewed: rule-based systems that are handcrafted using best practice guidelines; statistical data-driven systems based on machine learning; and neural dialogue systems based on end-to-end learning. Evaluating the performance and usability of dialogue systems has become an important topic in its own right, and a variety of evaluation metrics and frameworks are described. Finally, a number of challenges for future research are considered, including: multimodality in dialogue systems, visual dialogue; data efficient dialogue model learning; using knowledge graphs; discourse and dialogue phenomena; hybrid approaches to dialogue systems development; dialogue with social robots and in the Internet of Things; and social and ethical issues.Table of ContentsPreface.- Acknowledgments.- Glossary.- Introducing Dialogue Systems.- Rule-Based Dialogue Systems: Architecture, Methods, and Tools.- Statistical Data-Driven Dialogue Systems.- Evaluating Dialogue Systems.- End-to-End Neural Dialogue Systems.- Challenges and Future Directions.- Bibliography.- Author's Biography .

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