- Accessible introduction to the application of Artificial Intelligence in schools - Provides a practical framework for teachers and headteachers to develop an AI strategy - Examines evidence informed interventions across all school age groups - Written by a world-leading expert

Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications. Academic scientists, researchers, and students in the various domains of computer science engineering, electronics and communication engineering, and information technology, as well as industrial engineers, biomedical engineers, and management, will find this book useful. By the end of this book, you will understand the fundamentals of AI. Various case studies will develop your adaptive thinking to solve real-time AI problems. Features Includes AI-based decision-making approaches Discusses computational intelligence in the detection and diagnosis of clinical and biomedical images Covers automation of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining for decision-support applications Offers case-based reasoning

Metaheuristic algorithms are considered as generic optimization tools that can solve very complex problems characterized by having very large search spaces. Metaheuristic methods reduce the effective size of the search space through the use of effective search strategies. Book Features: Provides a unified view of the most popular metaheuristic methods currently in use Includes the necessary concepts to enable readers to implement and modify already known metaheuristic methods to solve problems Covers design aspects and implementation in MATLAB (R) Contains numerous examples of problems and solutions that demonstrate the power of these methods of optimization The material has been written from a teaching perspective and, for this reason, this book is primarily intended for undergraduate and postgraduate students of artificial intelligence, metaheuristic methods, and/or evolutionary computation. The objective is to bridge the gap between metaheuristic techniques and complex optimization problems that profit from the convenient properties of metaheuristic approaches. Therefore, engineer practitioners who are not familiar with metaheuristic computation will appreciate that the techniques discussed are beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas.

Human Factors in Intelligent Vehicles addresses issues related to the analysis of human factors in the design and evaluation of intelligent vehicles for a wide spectrum of applications and over different dimensions. To commemorate the 8th anniversary of the IEEE ITS Workshop on Human Factors (http://hfiv.net) some recent works of authors active in the automotive human factors community have been collected in this book. Enclosed here are extended versions of papers and tutorials that were presented at the IEEE ITSS Workshop on "Human Factors in Intelligent Vehicles" and also included is additional deeper analysis along with detailed experimental and simulation results. The contributors cover autonomous vehicles as well as the frameworks for analyzing automation, modelling and methods for road users' interaction such as intelligent user interfaces, including brain-computer interfaces and simulation and analysis tools related to human factors.

Inspired by Roland Barthes's practice of "semioclasm" in Mythologies, this book offers a "technoclasm"; a cultural critique of US narratives, discourses, images, and objects that have transformed the politics of automation into statements of fact about the "rise of the robots". Treating automation as an ensemble of technologies and science fictions, this book foregrounds automation's ideologies, exaggerations, failures, and mystifications of the social value of human labor in order to question accepted and prolific automation mythologies. Jesse Ramirez offers a study of automation that recognizes automation as a technosocial project, that uses the tools of cultural studies and history to investigate the narratives and ideologies that often implicitly frame the automation debate, and that concretely and soberly assesses the technologies that have made the headlines. The case studies featured include some of the most widely cited and celebrated automatic technologies, such as the Baxter industrial robot, the self-driving car, and the Watson AI system. An ideal resource for anyone interested in or studying emerging technology and society, automation, Marxist cultural theory, cultural studies, science fiction studies, and the cultural history of technology.

Swarm Intelligence in Cloud Computing is an invaluable treatise for researchers involved in delivering intelligent optimized solutions for reliable deployment, infrastructural stability, and security issues of cloud-based resources. Starting with a bird's eye view on the prevalent state-of-the-art techniques, this book enriches the readers with the knowledge of evolving swarm intelligent optimized techniques for addressing different cloud computing issues including task scheduling, virtual machine allocation, load balancing and optimization, deadline handling, power-aware profiling, fault resilience, cost-effective design, and energy efficiency. The book offers comprehensive coverage of the most essential topics, including: Role of swarm intelligence on cloud computing services Cloud resource sharing strategies Cloud service provider selection Dynamic task and resource scheduling Data center resource management. Indrajit Pan is an Associate Professor in Information Technology of RCC Institute of Information Technology, India. He received his PhD from Indian Institute of Engineering Science and Technology, Shibpur, India. With an academic experience of 14 years, he has published around 40 research publications in different international journals, edited books, and conference proceedings. Mohamed Abd Elaziz is a Lecturer in the Mathematical Department of Zagazig University, Egypt. He received his PhD from the same university. He is the author of more than 100 articles. His research interests include machine learning, signal processing, image processing, cloud computing, and evolutionary algorithms. Siddhartha Bhattacharyya is a Professor in Computer Science and Engineering of Christ University, Bangalore. He received his PhD from Jadavpur University, India. He has published more than 230 research publications in international journals and conference proceedings in his 20 years of academic experience.

Artificial Intelligence (AI) is the most rapidly developing technology in the current Digital Age, but it is also the least defined, understood and adequately explained technological advance. This book brings together a group of leading experts who assess different aspects of AI from different disciplinary perspectives. The book argues that robots are not living systems but the creations of humans who must ultimately be accountable for the actions of the robots that they have invented. Robots do not have ownership entitlement. The book uses Intellectual Property Rights cases, evidence from roboticists, cybersecurity experts, Patent Court judges, technology officers, climate change scientists, economists, physicists and those from the legal profession to demonstrate that while AI can have very beneficial uses for many aspects of human economy and society, robots are not living systems autonomous from human decision making. This book will be useful to those in banking and insurance, cybersecurity, lawyers, judges, technology officers, economists, scientist inventors, computer scientists, large and small companies and postgraduate students.

This book examines the digitalization of longstanding problems of technological advance that produce inequalities and automated governance, which relieves subjects of agency and critical thought, and prompts a need to weaponize thoughtfulness against technocratic designs. The book situates digital-era problems relative to those of previous sociotechnical milieux and argues that technical advance perennially embeds corrosive effects on social relations and relations of production, recognizing variation across contexts and relative to entrenched societal hierarchies of race and other axes of difference and their intersections. Societal tolerance, despite abundant evidence for harmful effects of digital technologies, requires attention. The book explains blindness to social injustice by technocratic thinking delivered through education as well as truths embraced in the data sciences coupled with governance in universities and the private sector that protect these truths from critique. Institutional inertia suggests benefits of communitarianism, which strives for change emanating from civil society. Scaling postcapitalist communitarian values through communitybased peer production presents opportunities. However, enduring problems require critical reflection, continual revision of strategies, and active participation among diverse community citizens. This book is written with critical geographic sensibilities for an interdisciplinary audience of scholars and graduate and undergraduate students in the social sciences, humanities, and data sciences.

In today's world, the use of technology is growing rapidly, and people need effective solutions for their real-life problems. This book discusses smart applications of associated technologies to develop cohesive and comprehensive solutions for the betterment of humankind. It comprehensively covers the effective use of the Internet of Things (IoT), wireless sensor network, wearable sensors, body area network, cloud computing, and distributed computing methodologies. The book comprehensively covers IoT and fog computing sensor supported technologies or protocols including web of things, near-field communication, 6LoWPAN, LoRAWAN, XMPP, DDS, LwM2M, Mesh Protocol, and radio-frequency identification. The book- Discusses smart applications to develop cohesive and comprehensive solutions for real-life problems. Covers analytical descriptions with appropriate simulation and prototype models. Examines the role of IoT and fog computing technologies during global emergency situations. Discusses key technologies including cloud computing, 5G communication, big data, artificial intelligence, control systems, and wearable sensors. The text is primarily written for graduate students, and academic researchers working in diverse fields of electrical engineering, biomedical engineering, electronics and communication engineering, computer engineering, and information technology.

Explains smart city ecosystem and AI-centric solutions Presents the application of design principles and computer vision models for operating smart cities and security systems Discusses how to integrate the AI-based controls systems to make the IoT devices smarter Explains data engineering and visualization patterns for monitoring smart city systems Discusses self-driving car models and transportation infrastructures

Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time. For each possibility, the book shows how to model the social network using spectral embedding. It also shows how to compose the techniques so that multiple edge semantics can be modeled together, and the modeling techniques are then applied to a range of datasets. Features Introduces the reader to difficulties with current social network analysis, and the need for richer representations of relationships among nodes, including accounting for intensity, direction, type, positive/negative, and changing intensities over time Presents a novel mechanism to allow social networks with qualitatively different kinds of relationships to be described and analyzed Includes extensions to the important technique of spectral embedding, shows that they are mathematically well motivated and proves that their results are appropriate Shows how to exploit embeddings to understand structures within social networks, including subgroups, positional significance, link or edge prediction, consistency of role in different contexts, and net flow of properties through a node Illustrates the use of the approach for real-world problems for online social networks, criminal and drug smuggling networks, and networks where the nodes are themselves groups Suitable for researchers and students in social network research, data science, statistical learning, and related areas, this book will help to provide a deeper understanding of real-world social networks.

This book develops a framework that shows how uncertainty in Artificial Intelligence (AI) expands and generalizes traditional AI. It explores the uncertainties of knowledge and intelligence. The authors focus on the importance of natural language - the carrier of knowledge and intelligence, and introduce efficient physical methods for data mining amd control. In this new edition, we have more in-depth description of the models and methods, of which the mathematical properties are proved strictly which make these theories and methods more complete. The authors also highlight their latest research results.

If the intelligence of artificial systems were to surpass that of humans, humanity would face significant risks. The time has come to consider these issues, and this consideration must include progress in artificial intelligence (AI) as much as insights from AI theory. Featuring contributions from leading experts and thinkers in artificial intelligence, Risks of Artificial Intelligence is the first volume of collected chapters dedicated to examining the risks of AI. The book evaluates predictions of the future of AI, proposes ways to ensure that AI systems will be beneficial to humans, and then critically evaluates such proposals. The book covers the latest research on the risks and future impacts of AI. It starts with an introduction to the problem of risk and the future of artificial intelligence, followed by a discussion (Armstrong/Sokala/OhEigeartaigh) on how predictions of its future have fared to date. Omohundro makes the point that even an innocuous artificial agent can easily turn into a serious threat for humans. T. Goertzel explains how to succeed in the design of artificial agents. But will these be a threat for humanity, or a useful tool? Ways to assure beneficial outcomes through 'machine ethics' and 'utility functions' are discussed by Brundage and Yampolskiy. B. Goertzel and Potapov/Rodionov propose 'learning' and 'empathy' as paths towards safer AI while Kornai explains how the impact of AI may be bounded. Sandberg explains the implications of human-like AI via the technique of brain emulation. Dewey discusses strategies to deal with the 'fast takeoff' of artificial intelligence and, finally, Bishop explains why there is no need to worry because computers will remain in a state of 'artificial stupidity'. Sharing insights from leading thinkers in artificial intelligence, this book provides you with an expert-level perspective of what is on the horizon for AI, whether it will be a threat for humanity, and how we might counteract this threat.

This textbook is an introduction to Scientific Computing, in which several numerical methods for the computer-based solution of certain classes of mathematical problems are illustrated. The authors show how to compute the zeros, the extrema, and the integrals of continuous functions, solve linear systems, approximate functions using polynomials and construct accurate approximations for the solution of ordinary and partial differential equations. To make the format concrete and appealing, the programming environments Matlab and Octave are adopted as faithful companions. The book contains the solutions to several problems posed in exercises and examples, often originating from important applications. At the end of each chapter, a specific section is devoted to subjects which were not addressed in the book and contains bibliographical references for a more comprehensive treatment of the material. From the review: ".... This carefully written textbook, the third English edition, contains substantial new developments on the numerical solution of differential equations. It is typeset in a two-color design and is written in a style suited for readers who have mathematics, natural sciences, computer sciences or economics as a background and who are interested in a well-organized introduction to the subject." Roberto Plato (Siegen), Zentralblatt MATH 1205.65002.

The most exciting and productive areas of academic inquiry are often where the interests of two disciplines meet. This is certainly the case for the subject of this book, originally published in 1994, which explores the contribution that computer-based modelling and artificial intelligence can make to understanding fundamental issues in social science. Simulating Societies shows how computer simulations can help to clarify theoretical approaches, contribute to the evaluation of alternative theories, and illuminate one of the major issues of the social sciences: how social phenomena can "emerge" from individual action. The authors discuss how simulation models can be constructed using recently developed artificial intelligence techniques and they consider the methodological issues involved in using such models for theory development, testing and experiment. The introductory chapters situate the book within social science, and suggest why the time was ripe for significant progress, before defining basic terminology, showing how simulation has been used to theorize about organizations, and indicating through examples some of the fundamental issues involved in simulation. The main body of the text provides case studies drawn from economics, anthropology, archaeology, planning, social psychology and sociology. The appeal of this path-breaking book was twofold. It offered an essential introduction to simulation for social scientists and it provided case study applications for computer scientists interested in the latest advances in the burgeoning area of distributed artificial intelligence (DAI) at the time.

The purpose of this book, originally published in 1987, was to contribute to the advance of artificial intelligence (AI) by clarifying and removing the major sources of philosophical confusion at the time which continued to preoccupy scientists and thereby impede research. Unlike the vast majority of philosophical critiques of AI, however, each of the authors in this volume has made a serious attempt to come to terms with the scientific theories that have been developed, rather than attacking superficial 'straw men' which bear scant resemblance to the complex theories that have been developed. For each is convinced that the philosopher's responsibility is to contribute from his own special intellectual point of view to the progress of such an important field, rather than sitting in lofty judgement dismissing the efforts of their scientific peers. The aim of this book is thus to correct some of the common misunderstandings of its subject. The technical term Artificial Intelligence has created considerable unnecessary confusion because of the ordinary meanings associated with it, and for that very reason, the term is endlessly misused and abused. The essays collected here all aim to expound the true nature of AI, and to remove the ill-conceived philosophical discussions which seek answers to the wrong questions in the wrong ways. Philosophical discussions and decisions about the proper use of AI need to be based on a proper understanding of the manner in which AI-scientists achieve their results; in particular, in their dependence on the initial planning input of human beings. The collection combines the Anglo-Saxon school of analytical philosophy with scientific and psychological methods of investigation. The distinguished authors in this volume represent a cross-section of philosophers, psychologists, and computer scientists from all over the world. The result is a fascinating study in the nature and future of AI, written in a style which is certain to appeal and inform laymen and specialists alike.

This book, originally published in 1992, encapsulates ten years of research at the Open University's Human Cognition Research Laboratory. The research investigates the problems of novice programmers, and is strongly oriented toward the design and implementation of "programming environments" aimed at eliminating or easing novices' problems. A range of languages is studied: Pascal, SOLO, Lisp, Prolog and "Knowledge Engineering Programming". The primary emphasis of the empirical studies is to gain some understanding of novices' "mental models" of the inner workings of computers. Such (erroneous) models are constructed by novices in their own heads to account for the idiosyncrasies of particular programming languages. The primary emphasis of the implementations described in the book is the provision of "automatic debugging aids", i.e. artificial intelligence programs which can analyse novices' buggy programs, and make sense of them, thereby providing useful advice for the novices. Another related strand taken in some of the work is the concept of "pre-emptive design", i.e. the provision of tools such as syntax-directed editors and graphical tracers which help programmers avoid many frequently-occurring errors. A common thread throughout the book is its Cognitive Science/Artificial Intelligence orientation. AI tools are used, for instance, to construct simulation models of subjects writing programs, in order to provide insights into what their deep conceptual errors are. At the other extreme, AI programs which were developed in order to help student debug their programs are observed empirically in order to ensure that they provide facilities actually needed by real programmers. This book will be of great interest to advanced undergraduate, postgraduate, and professional researchers in Cognitive Science, Artificial Intelligence, and Human-Computer Interaction.

Originally published in 1986, in order to probe, dispute and analyse the role of artificial intelligence in cybernetic thought and information science, the author pursues this topic within its philosophical, behavioral and neurophysiological contexts, while drawing attention to cognitive issues. By elucidating the problems and potential associated with knowledge-based systems, the book emphasized the need to examine artificial intelligence in its own right.

This book provides knowledge, skills, and strategies an engineer requires to effectively integrate Internet of Things (IoT) into the field of mechanical engineering. Divided into three sections named IoT Strategies, IoT Foundation topics, and IoT system development, the volume covers introduction to IoT framework, its components, advantages, challenges, and practical process for effective implementation of IoT from mechanical engineering perspective. Further, it explains IoT systems and hands-on training modules, implementation, and execution of IoT Systems. Features: Presents exclusive material on application of IoT in mechanical engineering. Combines theory and practice including relevant terminologies and hands-on. Emphasis on use of IoT to streamline operations, reduce costs, and increased profits. Focusses on development and implementation of Raspberry Pi and Arduino based IoT systems. Illustrates use IoT data to improve performance of robots, machines, and systems. This book aims at Researchers, Graduate students in Mechanical Engineering, Computer Programming, Automobile, Robotics, and Industry 4.0/automation.

Aims to be broad in scope yet practical in approach, so that it can serve the needs of several different audiences, including researchers, teachers, developers, and theologians; Covers a wide variety of issues which have been neglected in other research texts; Studies the impact of creating a digital immortal on relatives and friends, the consequences of persistent digital legacies, and ways digital media are currently being used to expand the possibilities of commemorating the dead and managing the grief of those left behind; Analyzes the extent to which digital media are complementing or replacing the well-established formal structures and religious rituals; Explores the legal and ethical impact of creating a digital immortal.

FROM THE BBC REITH LECTURER 2021 'The most important book I have read in quite some time' Daniel Kahneman; 'A must-read' Max Tegmark; 'The book we've all been waiting for' Sam Harris Humans dream of super-intelligent machines. But what happens if we actually succeed? Creating superior intelligence would be the biggest event in human history. Unfortunately, according to the world's pre-eminent AI expert, it could also be the last. In this groundbreaking book, Stuart Russell sets out why he has come to consider his own discipline an existential threat to humanity, and how we can change course before it's too late. In brilliant and lucid prose, he explains how AI actually works and its enormous capacity to improve our lives - and why we must never lose control of machines more powerful than we are. Russell contends that we can avert the worst threats by reshaping the foundations of AI to guarantee that machines pursue our objectives, not theirs. Profound, urgent and visionary, Human Compatible is the one book everyone needs to read to understand a future that is coming sooner than we think. LONGLISTED FOR THE FINANCIAL TIMES & McKINSEY BUSINESS BOOK OF THE YEAR 'Thought-provoking' Financial Times 'Fascinating and significant' Sunday Times 'The most important book on AI this year' Guardian

accessible for readers unfamiliar with both SDGs and ESG concepts; it will enable leaders and businesses to assess deployment and development of AI more easily

Originally published in 1995, this volume is the direct result of a conference in which a number of leading researchers from the fields of artificial intelligence and biology gathered to examine whether there was any ground to assume that a new AI paradigm was forming itself and what the essential ingredients of this new paradigm were. A great deal of scepsis is justified when researchers, particularly in the cognitive sciences, talk about a new paradigm. Shifts in paradigm mean not only new ideas but also shifts in what constitutes good problems, what counts as a result, the experimental practice to validate results, and the technological tools needed to do research. Due to the complexity of the subject matter, paradigms abound in the cognitive sciences -- connectionism being the most prominent newcomer in the mid-1980s. This workshop group was brought together in order to clarify the common ground, see what had been achieved so far, and examine in which way the research could move further. This volume is a reflection of this important meeting. It contains contributions which were distributed before the workshop but then substantially broadened and revised to reflect the workshop discussions and more recent technical work. Written in polemic form, sometimes criticizing the work done thus far within the new paradigm, this collection includes research program descriptions, technical contributions, and position papers.

Physical and behavioral biometric technologies such as fingerprinting, facial recognition, voice identification, etc. have enhanced the level of security substantially in recent years. Governments and corporates have employed these technologies to achieve better customer satisfaction. However, biometrics faces major challenges in reducing criminal, terrorist activities and electronic frauds, especially in choosing appropriate decision-making algorithms. To face this challenge, new developments have been made, that amalgamate biometrics with artificial intelligence (AI) in decision-making modeling. Advanced software algorithms of AI, processing information offered by biometric technology, achieve better results. This has led to growth in the biometrics technology industry, and is set to increase the security and internal control operations manifold. This book provides an overview of the existing biometric technologies, decision-making algorithms and the growth opportunity in biometrics. The book proposes a throughput model, which draws on computer science, economics and psychology to model perceptual, informational sources, judgmental processes and decision choice algorithms. It reviews how biometrics might be applied to reduce risks to individuals and organizations, especially when dealing with digital-based media.

Covers details on wireless communication problems, conducive for data-driven solutions Provides a comprehensive account of programming languages, tools, techniques, and good practices Provides an introduction to data-driven techniques applied to wireless communication systems Examines data-driven techniques, performance, and design issues in wireless networks Includes several case studies that examine data-driven solution for QoS in heterogeneous wireless networks