First published in 1981. Routledge is an imprint of Taylor & Francis, an informa company.

The Home in the Digital Age is a set of multidisciplinary studies exploring the impact of digital technologies in the home, with a shift of emphasis from technology to the people living and using this in their homes. The book covers a wide variety of topics on the design, introduction and use of digital technologies in the home, combining the technological dimension with the cognitive, emotional, cultural and symbolic dimensions of the objects that incorporate digital technologies and project them onto people's lives. It offers a coherent approach, that of the home, which gives unity to the discussion. Scholars of the home, the house and the family will find here the connection with the problems derived from the use of domestic robots and connected devices. Students of artificial intelligence, machine learning, robotics, big data and other branches of digital technologies will find ideas and arguments to apply their disciplines to the home and participate fruitfully in forums where digital technologies are built and negotiated in the home. Experts from various disciplines psychologists and sociologists; philosophers, epistemologists and ethicists; economists; engineers, architects, urban planners and designers and so on and also those interested in developing policies for the home and family will find this book contains well-founded and useful ideas to focus their work.

This book provides an authoritative resource on the topic of intelligent robots, artificial intelligence and the ethical implications of these revolutionary innovations. It examines the moral and ethical problems that arise in relation to the development, design and use of intelligent robots, which are capable of autonomous or semi-autonomous decision-making. These problems might relate, for example, to medical robots, driverless cars, intelligent military drones, pedagogical robots, police robots, legal robots and many others. The main question addressed in this book is how we can understand, explain and apply the concept of ethics in relation to intelligent robots and artificial intelligence. In each chapter, the author examines a different aspect of this question. The author also questions how we can ensure that intelligent robots are of service to humans and under what conditions intelligent robots could become more ethical than humans. The book employs an original approach to examining this cutting-edge research question, combining different research areas, and offers a wealth of practical relevance and real-world examples, illustrated through vivid case studies. With its jargon free approach and a dedicated chapter on relevant concepts at the end, this book is also accessible to readers without prior knowledge on intelligent robots and the Fourth Industrial Revolution. By providing a general account of this debate, and of the consequences of the innovations resulting from these trends, the book serves as an important contribution to the discussion and will find a natural readership among scholars and students of the innovation economy and those concerned with the ethical considerations arising in the wake of the Fourth Industrial Revolution

What is AI? A dominant view describes it as the quest "to solve intelligence" - a solution supposedly to be found in the secret logic of the mind or in the deep physiology of the brain, such as in its complex neural networks. The Eye of the Master argues, to the contrary, that the inner code of AI is shaped not by the imitation of biological intelligence, but the intelligence of labour and social relations, as it is found in Babbage's "calculating engines" of the industrial age as well as in the recent algorithms for image recognition and surveillance. The idea that AI may one day become autonomous (or "sentient", as someone thought of Google's LaMDA) is pure fantasy. Computer algorithms have always imitated the form of social relations and the organisation of labour in their own inner structure and their purpose remains blind automation. The Eye of the Master urges a new literacy on AI for scientists, journalists and new generations of activists, who should recognise that the "mystery" of AI is just the automation of labour at the highest degree, not intelligence per se.

1. Provides a toolkit of templates for common VR interactions, as well as practical advice on when to use them and how to tailor them for specific use cases; 2. Includes case studies detailing the practical application of interaction theory discussed in each chapter; 3. Presents tables of guidelines for practicing VR developers, for reference during software development; 4. Covers procedures for Interface Evaluation - formulas and testing methodologies to ensure that VR interfaces are effective, efficient, engaging, error-tolerant, and easy to learn; 5. Non-linear organisation - chapters of the book on different concepts can be read to gain knowledge on a single topic, without requiring other chapters to be read beforehand; 6. Includes ancillaries - PowerPoint slides, 3D models, videos, and a teacher's guide

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.

Embedded Analytics is one of the hottest trends in business intelligence right now. It's being used in multiple ways to improve decision making, provide faster insights, gain competitive advantages and grow revenue. Over the last 10 years, data analytics and data visualization have become essential components of an enterprise information strategy. Nevertheless, despite this recognition, the adoption of data analytics has remained remarkably static - perhaps reaching no more than thirty percent of potential users. This book explores the most important techniques for taking that adoption further: embedding analytics into the workflow of our everyday operations.

Whether you're part of a small startup or a planet-spanning megacorp, this practical book shows data scientists, SREs, and business owners how to run ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization. By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guests show you how to run an efficient ML system. Whether you want to increase revenue, optimize decision-making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind. You'll examine: What ML is: how it functions and what it relies on Conceptual frameworks for understanding how ML "loops" work Effective "productionization," and how it can be made easily monitorable, deployable, and operable Why ML systems make production troubleshooting more difficult, and how to get around them How ML, product, and production teams can communicate effectively

With the emergence of Artificial Intelligence (AI) in the business world, a new era of Business Intelligence (BI) has been ushered in to create real-world business solutions using analytics. BI developers and practitioners now have tools and technologies to create systems and solutions to guide effective decision making. Decisions can be made on the basis of more reliable and accurate information and intelligence, which can lead to valuable, actionable insights for business. Previously, BI professionals were stymied by bad or incomplete data, poorly architected solutions, or even just outright incapable systems or resources. With the advent of AI, BI has new possibilities for effectiveness. This is a long-awaited phase for practitioners and developers and, moreover, for executives and leaders relying on knowledgeable and intelligent decision making for their organizations. Beginning with an outline of the traditional methods for implementing BI in the enterprise and how BI has evolved into using self-service analytics, data discovery, and most recently AI, AI Meets BI first lays out the three typical architectures of the first, second, and third generations of BI. It then takes an in-depth look at various types of analytics and highlights how each of these can be implemented using AI-enabled algorithms and deep learning models. The crux of the book is four industry use cases. They describe how an enterprise can access, assess, and perform analytics on data by way of discovering data, defining key metrics that enable the same, defining governance rules, and activating metadata for AI/ML recommendations. Explaining the implementation specifics of each of these four use cases by way of using various AI-enabled machine learning and deep learning algorithms, this book provides complete code for each of the implementations, along with the output of the code, supplemented by visuals that aid in BI-enabled decision making. Concluding with a brief discussion of the cognitive computing aspects of AI, the book looks at future trends, including augmented analytics, automated and autonomous BI, and security and governance of AI-powered BI.

In recent years, drones have been integrated with the Internet of Things to offer a variety of exciting new applications. Here is a detailed exploration of adapting and implementing Internet of Drones technologies in real-world applications, emphasizing solutions to architectural challenges and providing a clear overview of standardization and regulation, implementation plans, and privacy concerns. The book discusses the architectures and protocols for drone communications, implementing and deploying of 5G-drone setups, security issues, deep learning techniques applied on real-time footage, and more. It also explores some of the varied applications, such as for monitoring and analysis of troposphere pollutants, providing services and communications in smart cities (such as for weather forecasting, communications, transport, safety and protection), for disaster relief management, for agricultural crop monitoring, and more.

Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks. Features: A structured overview of deep learning in object detection A diversified collection of applications of object detection using deep neural networks Emphasize agriculture and remote sensing domains Exclusive discussion on moving object detection

* Essay-based format weaves together technical details and case studies to cut through complexity * Provides a strong background in business situations that companies face, to ensure that data analytics efforts are productively directed and organized * Appropriate for both business and engineering students who need to understand the data analytics lifecycle

This book focuses on environmental sustainability by employing elements of engineering and green computing through modern educational concepts and solutions. It visualizes the potential of artificial intelligence, enhanced by business activities and strategies for rapid implementation, in manufacturing and green technology. This book covers utilization of renewable resources and implementation of the latest energy-generation technologies. It discusses how to save natural resources from depletion and illustrates facilitation of green technology in industry through usage of advanced materials. The book also covers environmental sustainability and current trends in manufacturing. The book provides the basic concepts of green technology, along with the technology aspects, for researchers, faculty, and students.

Unique selling point: Combines theory with practice and applications for advanced intelligent healthcare informatics Core audience: Researchers and academics in healthcare informatics and machine learning Place in the market: Reference work

The rise in living standards increases the expectation of people in almost every field. At the forefront is health. Over the past few centuries, there have been major developments in healthcare. Medical device technology and developments in artificial intelligence (AI) are among the most important ones. The improving technology and our ability to harness the technology effectively by means such as AI have led to unprecedented advances, resulting in early diagnosis of diseases. AI algorithms enable the fast and early evaluation of images from medical devices to maximize the benefits. While developments in the field of AI were quickly adapted to the field of health, in some cases this contributed to the formation of innovative artificial intelligence algorithms. Today, the most effective artificial intelligence method is accepted as deep learning. Convolutional neural network (CNN) architectures are deep learning algorithms used for image processing. This book contains applications of CNN methods. The content is quite extensive, including the application of different CNN methods to various medical image processing problems. Readers will be able to analyze the effects of CNN methods presented in the book in medical applications.

This book is a timely and critical introduction for those interested in what data science is (and isn't), and how it should be applied. The language is conversational and the content is accessible for readers without a quantitative or computational background; but, at the same time, it is also a practical overview of the field for the more technical readers. The overarching goal is to demystify the field and teach the reader how to develop an analytical mindset instead of following recipes. The book takes the scientist's approach of focusing on asking the right question at every step as this is the single most important factor contributing to the success of a data science project. Upon finishing this book, the reader should be asking more questions than I have answered. This book is, therefore, a practising scientist's approach to explaining data science through questions and examples.

This book develops an original theoretical framework for understanding human-technology relations. The author's approach, which he calls technoanalysis, analyzes artificial intelligence based on Freudian psychoanalysis, biosemiotics, and Latour's actor-network theory. How can we communicate with AI to determine shared values and objectives? And what, ultimately, do we want from machines? These are crucial questions in our world, where the influence of AI-based technologies is rapidly growing. Unconscious dynamics influence AI and digital technology and understanding them is essential to better controlling AI systems. This book's unique methodology- which combines psychoanalysis, biosemiotics, and actor-network theory-reveals a radical reformulation of the problem of the human mind. Technoanalysis views the mind as a hybrid network of humans and nonhuman actants in constant interaction with one another. The author argues that human unconscious dynamics influence and shape technology, just as technology influences and shapes human unconscious dynamics. He proceeds to show how this conception of the relationship between the unconscious and technology can be applied to social robotics and AI. Unconscious Networks will appeal to scholars and advanced students interested in philosophy of technology, philosophy of artificial intelligence, psychoanalysis, and science and technology studies.

Novel perspectives on machine behaviour as it relates to behavioural science. A framework of terminology to empower discussion of AI within behavioural science. A compelling definition of hypernudging developed from AI and behavioural science principles.

Underwater robots play a significant role in ocean exploration. This book provides full coverage of the theoretical and practical aspects of bionic gliding underwater robots, including system design, modeling control, and motion planning. To overcome the inherent shortcomings of traditional underwater robots that can simultaneously lack maneuverability and endurance, a new type of robot, the bionic gliding underwater robot, has attracted much attention from scientists and engineers. On the one hand, by imitating the appearance and swimming mechanisms of natural creatures, bionic gliding underwater robots achieve high maneuverability, swimming efficiency, and strong concealment. On the other hand, borrowing from the buoyancy adjustment systems of underwater gliders, bionic gliding underwater robots can obtain strong endurance, which is significant in practical applications. Taking gliding robotic dolphin and fish as examples, the designed prototypes and proposed methods are discussed, offering valuable insights into the development of next-generation underwater robots that are well suited for various oceanic applications. This book will be of great interest to students and professionals alike in the field of robotics or intelligent control. It will also be a great reference for engineers or technicians who deal with the development of underwater robots.

This reference text provides the theoretical foundations, the emergence, and the application areas of Blockchain in an easy-to-understand manner that would be highly helpful for the researchers, academicians, and industry professionals to understand the disruptive potentials of Blockchain. It explains Blockchain concepts related to Industry 4.0, Smart Healthcare, and the Internet of Things (IoT) and explores Smart Contracts and Consensus algorithms. This book will serve as an ideal reference text for graduate students and academic researchers in electrical engineering, electronics and communication engineering, computer engineering, and information technology. This book * Discusses applications of blockchain technology in diverse sectors such as industry 4.0, education, finance, and supply chain. * Provides theoretical concepts, applications, and research advancements in the field of blockchain. * Covers industry 4.0 digitization platform and blockchain for data management in industry 4.0 in a comprehensive manner. * Emphasizes analysis and design of consensus algorithms, fault tolerance, and strategy to choose the correct consensus algorithm. * Introduces security issues in the industrial internet of things, internet of things, blockchain integration, and blockchain-based applications. The text presents in-depth coverage of theoretical concepts, applications and advances in the field of blockchain technology. This book will be an ideal reference for graduate students and academic researchers in diverse engineering fields such as electrical, electronics and communication, computer, and information technology.

Artificial Intelligence and Learning Futures: Critical Narratives of Technology and Imagination in Higher Education explores the implications of artificial intelligence's adoption in higher education and the challenges to building sustainable instead of dystopic schooling. As AI becomes integral to both pedagogy and profitability in today's colleges and universities, a critical discourse on these systems and algorithms is urgently needed to push back against their potential to enable surveillance, control, and oppression. This book examines the development, risks, and opportunities inherent to AI in education and curriculum design, the problematic ideological assumptions of intelligence and technology, and the evidence base and ethical imagination required to responsibly implement these learning technologies in a way that ensures quality and sustainability. Leaders, administrators, and faculty as well as technologists and designers will find these provocative and accessible ideas profoundly applicable to their research, decision-making, and concerns.

This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with the foundations of the theory and building it up, this is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It provides guidance on how to think about scientific questions, and leads readers through the history of the field and its fundamental connections to neuroscience. The author discusses many applications to beautiful problems in the natural sciences, in physics, chemistry, and biomedicine. Examples include the search for exotic particles and dark matter in experimental physics, the prediction of molecular properties and reaction outcomes in chemistry, and the prediction of protein structures and the diagnostic analysis of biomedical images in the natural sciences. The text is accompanied by a full set of exercises at different difficulty levels and encourages out-of-the-box thinking.

Introduces the main philosophical ideas concerning mind and freedom Explains the basic principles of computer science, artificial intelligence of brain research, quantum physics, and quantum artificial intelligence Provides an essential compilation of knowledge in philosophy, computer science, biology, and quantum physics

SDN-Supported Edge-Cloud Interplay for Next Generation Internet of Things is an invaluable resource coveringa wide range of research directions in the field of edge-cloud computing, SDN, and IoT. The integration of SDN in edge-cloud interplay is a promising framework for enhancing the QoS for complex IoT-driven applications. The interplay between cloud and edge solves some of the major challenges that arise in traditional IoT architecture. This book is a starting point for those involved in this research domain and explores a range of significant issues including network congestion, traffic management, latency, QoS, scalability, security, and controller placement problems. Features: The book covers emerging trends, issues and solutions in the direction of Edge-cloud interplay It highlights the research advances in on SDN, edge, and IoT architecture for smart cities, and software-defined internet of vehicles It includes detailed discussion has made of performance evaluations of SDN controllers, scalable software-defined edge computing, and AI for edge computing Applications areas include machine learning and deep learning in SDN-supported edge-cloud systems Different use cases covered include smart health care, smart city, internet of drones, etc This book is designed for scientific communities including graduate students, academicians, and industry professionals who are interested in exploring technologies related to the internet of things such as cloud, SDN, edge, internet of drones, etc.

The proposed title intends to provide a comprehensive view of emerging paradigms of computer science. The initial chapters will introduce various emerging paradigms and discuss research challenges related to them. Then some of the chapters will focus on the research solutions to address the identified challenges. The last few chapters will provide a discussion on future research directions.