The great majority of books on artificial intelligence are written by AI experts who understandably focus on its achievements and potential transformative effects on society. In contrast, AI vs Humans is written by two psychologists (Michael and Christine Eysenck) whose perspective on AI (including robotics) is based on their knowledge and understanding of human cognition. This book evaluates the strengths and limitations of people and AI. The authors' expertise equips them well to consider this by seeing how well (or badly) AI compares to human intelligence. They accept that AI matches or exceeds human ability in many spheres such as mathematical calculations, complex games (e.g., chess, Go, and poker), diagnosis from medical images, and robotic surgery. However, the human tendency to anthropomorphise has led many people to claim mistakenly that AI systems can think, infer, reason, and understand while engaging in information processing. In fact, such systems lack all those cognitive skills and are also deficient in the quintessentially human abilities of flexibility of thinking and general intelligence. At a time when human commitment to AI appears unstoppable, this up-to-date book advocates a symbiotic and co-operative relationship between humans and AI. It will be essential reading for anyone interested in AI and human cognition.

What will you do when your AI misbehaves? The promise of artificial intelligence is automated decision-making at scale, but that means AI also automates risk at scale. Are you prepared for that risk? Already, many companies have suffered real damage when their algorithms led to discriminatory, privacy-invading, and even deadly outcomes. Self-driving cars have hit pedestrians; HR algorithms have precluded women from job searches; mortgage systems have denied loans to qualified minorities. And often the companies who deployed the AI couldn't explain why the black box made the decision it did. In this environment, AI ethics isn't merely an academic curiosity, it's a business necessity. In Ethical Machines, Reid Blackman gives you all you need to understand AI ethics as a risk management challenge. He'll help you build, procure, and deploy AI in a way that's not only ethical but also safe in terms of your organization's reputation, regulatory compliance, and legal standing-and do it at scale. And don't worry-the book's purpose is to get work done, not to ponder deep and existential questions about ethics and technology. Blackman's clear and accessible writing helps make a complex and often misunderstood concept like ethics easy to grasp. Most importantly, Blackman makes ethics actionable by tackling the big three ethical risks with AI-bias, explainability, and privacy-and tells you what to do (and what not to do) to mitigate them. With practical approaches to everything from writing a strong statement of AI ethics principles to creating teams that effectively evaluate ethical risks, Ethical Machines is the one guide you need to ensure your AI advances your company's objectives instead of undermining them.

1. Understand the audit culture, challenges, and benefits of the CAE role in digitally transforming business environment in smart cities 2. Identify ways to advance the value of Internal Audit in digital era 3. Use and control the resources of the city efficiently, and to ensure that the system units work properly in an integrated way.

Features Focus on the foundational theory underpinning Reinforcement Learning Suitable as a primary text for courses in Reinforcement Learning, but also as supplementary reading for applied/financial mathematics, programming, and other related courses Suitable for a professional audience of quantitative analysts or industry specialists Blends theory/mathematics, programming/algorithms and real-world financial nuances while always striving to maintain simplicity and to build intuitive understanding.

Blockchain for IoT provides the basic concepts of Blockchain technology and its applications to varied domains catering to socio-technical fields. It also introduces intelligent Blockchain platforms by way of infusing elements of computational intelligence into Blockchain technology. With the help of an interdisciplinary approach, it includes insights into real-life IoT applications to enable the readers to assimilate the concepts with ease. This book provides a balanced approach between theoretical understanding and practical applications. Features: A self-contained approach to integrating the principles of Blockchain with elements of computational intelligence A rich and novel foundation of Blockchain technology with reference to the internet of things conjoined with the tenets of artificial intelligence in yielding intelligent Blockchain platforms Elucidates essential background, concepts, definitions, and theories thereby putting forward a complete treatment on the subject Information presented in an accessible way for research students of computer science and information technology, as well as software professionals who can inherit the much-needed developmental ideas to boost up their computing knowledge on distributed platforms This book is aimed primarily at undergraduates, postgraduates, and researchers studying Blockchain.

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.

Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. * Foundational reinforcement learning concepts and methods * The most popular deep reinforcement learning agents solving high-dimensional environments * Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior on their own from raw sensory input. The system perceives the environment, interprets the results of its past decisions and uses this information to optimize its behavior for maximum long-term return.

Accomplished leadership consultant Nick Chatrath offers a revolutionary framework for how leaders in all kinds of organizations can adapt to the new age of technology--the Age of AI-- by leaning into the qualities and skills that make us uniquely human. For readers of Yuval Noah Harari's 21 Lessons for the 21st Century and Max Tegmark's Life 3.0, The Threshold is a bold new way to think about human, emotionally intelligent leadership right now as we stand at the threshold of AI superintelligence. We are living in a new age: the Age of AI. With developments emerging every day, Artificial Intelligence will soon surpass most human competencies, and as a result drastically transform technology's role in our day-to-day world. The solution for organizational leaders is not to become more like computers. In order for our organizations to survive as we stand at the threshold of a new era, we must tap into the qualities that make us uniquely human. In the face of increasingly intelligent technology, old models of leadership are becoming obsolete. In The Threshold: Leading in the Age of AI, accomplished leadership consultant Nick Chatrath interweaves an analysis of antiquated leadership models--the ones that leave AI-Era organizations exposed and ineffective with colleagues frustrated, unmotivated, and burnt-out--with his newly developed strategies for more effective "threshold" leadership methods. Supported with anecdotes, research, and a practical toolkit, The Threshold demonstrates that adaptive, effective organizations can be built with human, emotional intelligence: cultivating stillness, nurturing independent thinking, finding rhythms of rest and performance, and raising leadership consciousness. With a basis in the ideas and practices that have shaped our organizations in the past, The Threshold illuminates how accessing advanced stages of human development can be both competitive and harmonious with AI's growing insinuation into our working world.

Provides strong and accessible theoretical bases to swarm intelligence algorithms, from particle optimization to bioinspired and meta-heuristic algorithms Presents emerging meta-heuristic algorithms and applications Provides overviews on Python and R based computing libraries for swarm intelligence and meta-heuristic algorithms Presenting real-world applications, especially on Industry, Medicine and Biology.

This book is based on deep learning approaches used for the diagnosis of neurological disorders, including basics of deep learning algorithms using diagrams, data tables, and practical examples, for diagnosis of neurodegenerative and neurodevelopmental disorders. It includes application of feed-forward neural networks, deep generative models, convolutional neural networks, graph convolutional networks, and recurrent neural networks in the field of diagnosis of neurological disorders. Along with this, data pre-processing including scaling, correction, trimming, normalization is also included. Offers a detailed description of the deep learning approaches used for the diagnosis of neurological disorders Demonstrates concepts of deep learning algorithms using diagrams, data tables, and examples for the diagnosis of neurodegenerative disorders; neurodevelopmental, and psychiatric disorders. Helps build, train, and deploy different types of deep architectures for diagnosis Explores data pre-processing techniques involved in diagnosis Include real-time case studies and examples This book is aimed at graduate students and researchers in biomedical imaging and machine learning.

Artificial Intelligence: Applications and Innovations is a book about the science of artificial intelligence (AI). AI is the study of the design of intelligent computational agents. This book provides a valuable resource for researchers, scientists, professionals, academicians and students dealing with the new challenges and advances in the areas of AI and innovations. This book also covers a wide range of applications of machine learning such as fire detection, structural health and pollution monitoring and control. Key Features Provides insight into prospective research and application areas related to industry and technology Discusses industry- based inputs on success stories of technology adoption Discusses technology applications from a research perspective in the field of AI Provides a hands- on approach and case studies for readers of the book to practice and assimilate learning This book is primarily aimed at graduates and post- graduates in computer science, information technology, civil engineering, electronics and electrical engineering and management.

Explores the history of telepresence from the 1948 developments of master-slave manipulation, through to current telepresence technology used in space, undersea, surgery and telemedicine, operations in nuclear and other hazardous environments, policing and surveillance, agriculture, construction, mining, warehousing, education, amusement, social media and other contexts Reviews the differing technologies for visual, haptic, tactile remote sensing at the remote site, and the corresponding means of the display to the human operator Reviews the sensing and control technology, its history, and likely future, and discusses the many research and policy issues Reviews psychological experiments in telepresence with relation to virtual and augmented reality Examines social and ethical concerns: ease of spying, mischief, and crime via remote control of an avatar

This concise volume offers an accessible introduction to state-of-the-art artificial intelligence (AI) language models, providing a platform for their use in textual interpretation across the humanities and social sciences. The book outlines the affordances of new technologies for textual analysis, which has historically employed established approaches within the humanities. Neuman, Danesi, and Vilenchik argue that these different forms of analysis are indeed complementary, demonstrating the ways in which AI-based perspectives echo similar theoretical and methodological currents in traditional approaches while also offering new directions for research. The volume showcases examples from a wide range of texts, including novels, television shows, and films to illustrate the ways in which the latest AI technologies can be used for "dialoguing" with textual characters and examining textual meaning coherence. Illuminating the potential of AI language models to both enhance and extend research on the interpretation of texts, this book will appeal to scholars interested in cognitive approaches to the humanities in such fields as literary studies, discourse analysis, media studies, film studies, psychology, and artificial intelligence.

This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes. Key Features: * It covers both technical and soft skills. * It has a chapter dedicated to the big data cloud environment. For industry applications, the practice of data science is often in such an environment. * It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems, if possible. The best way to learn data science is to do it!

The best source for cutting-edge insights into AI in healthcare operations AI in Healthcare: How Artificial Intelligence Is Changing IT Operations and Infrastructure Services collects, organizes and provides the latest, most up-to-date research on the emerging technology of artificial intelligence as it is applied to healthcare operations. Written by a world-leading technology executive specializing in healthcare IT, this book provides concrete examples and practical advice on how to deploy artificial intelligence solutions in your healthcare environment. AI in Healthcare reveals to readers how they can take advantage of connecting real-time event correlation and response automation to minimize IT disruptions in critical healthcare IT functions. This book provides in-depth coverage of all the most important and central topics in the healthcare applications of artificial intelligence, including: Healthcare IT AI Clinical Operations AI Operational Infrastructure Project Planning Metrics, Reporting, and Service Performance AIOps in Automation AIOps Cloud Operations Future of AI Written in an accessible and straightforward style, this book will be invaluable to IT managers, administrators, and engineers in healthcare settings, as well as anyone with an interest or stake in healthcare technology.

This text emphasizes the importance of artificial intelligence techniques in the field of biological computation. It also discusses fundamental principles that can be applied beyond bio-inspired computing. It comprehensively covers important topics including data integration, data mining, machine learning, genetic algorithms, evolutionary computation, evolved neural networks, nature-inspired algorithms, and protein structure alignment. The text covers the application of evolutionary computations for fractal visualization of sequence data, artificial intelligence, and automatic image interpretation in modern biological systems. The text is primarily written for graduate students and academic researchers in areas of electrical engineering, electronics engineering, computer engineering, and computational biology. This book: * Covers algorithms in the fields of artificial intelligence, and machine learning useful in biological data analysis. * Discusses comprehensively artificial intelligence and automatic image interpretation in modern biological systems. * Presents the application of evolutionary computations for fractal visualization of sequence data. * Explores the use of genetic algorithms for pair-wise and multiple sequence alignments. * Examines the roles of efficient computational techniques in biology.

This book brings together history and theory in art and media to examine the effects of artificial intelligence and machine learning in culture, and reflects on the implications of delegating parts of the creative process to AI. In order to understand the complexity of authorship and originality in relation to creativity in contemporary times, Navas combines historical and theoretical premises from different areas of research in the arts, humanities, and social sciences to provide a rich historical and theoretical context that critically reflects on and questions the implications of artificial intelligence and machine learning as an integral part of creative production. As part of this, the book considers how much of postproduction and remix aesthetics in art and media preceded the current rise of metacreativity in relation to artificial intelligence and machine learning, and explores contemporary questions on aesthetics. The book also provides a thorough evaluation of the creative application of systematic approaches to art and media production, and how this in effect percolates across disciplines including art, design, communication, as well as other fields in the humanities and social sciences. An essential read for students and scholars interested in understanding the increasing role of AI and machine learning in contemporary art and media, and their wider role in creative production across culture and society.

Since the invention of computers or machines, scientists and researchers are trying very hard to enhance their capabilities to perform various tasks. As a consequence, the capabilities of computers are growing exponentially day by day in terms of diverse working domains, versatile jobs, processing speed, and reduced size. Now, we are in the race to make the computers or machines as intelligent as human beings. Artificial Intelligence (AI) came up as a way of making a computer or computer software think in the similar manner the intelligent humans think. AI is inspired by the study of human brain like how humans think, learn, decide and act while trying to solve a problem. The outcomes of this study are the basis of developing intelligent software and systems or Intelligent Computing (IC). An IC system has the capability of reasoning, learning, problem solving, perception, and linguistic intelligence. The IC systems consist of AI techniques as well as other emerging techniques that make a system intelligent. The use of intelligent computing has been seen in almost every sub-domain of computer science such as networking, software engineering, gaming, natural language processing, computer vision, image processing, data science, robotics, expert systems, and security. Now a days, the use of IC can also be seen for solving various complex problems in diverse domains such as for predicting disease in medical science, predicting land fertility or crop productivity in agriculture science, predicting market growth in economics, weather forecasting and so on. For all these reasons, this book presents the advances in AI techniques, under the umbrella of IC. In this context, the book includes the recent research works have been done in the areas of machine learning, neural networks, deep learning, evolutionary algorithms, genetic algorithms, swarm intelligence, fuzzy systems and so on. This book provides theoretical, algorithmic, simulation, and implementation-based recent research advancements related to the Intelligent Computing.

This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes. Key Features: * It covers both technical and soft skills. * It has a chapter dedicated to the big data cloud environment. For industry applications, the practice of data science is often in such an environment. * It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems, if possible. The best way to learn data science is to do it!

In recent years, there has been steady increase in the interest shown in both big data analytics and the use of information technology (IT) solutions to improve healthcare services. Despite the growing interest, there are limited materials, to addressing the needs and challenges posed by the activities and processes including the use of big data. From IT solutions' perspectives, this book aims to advance the deployment and use of big data analytics to increase patients' big data usefulness and improve healthcare service delivery. The book provides significant insights and useful guide on how to access and manage big data, in improving healthcare service delivery. The book contributes a fresh perspective, which primarily comes from the complementary use of analytics approach with actor-network theory (ANT), and other techniques, in advancing healthcare service delivery. Accessing and managing healthcare big data have always been a challenging exercise. Due to the sensitivity of the health sector, the focus on patients' big data is from either technical or social perspective. Thus, the book employs sociotechnical theories, ANT and structuration theory (ST) as lenses to examine and explain the factors that enable and constrain the use of patients' big data for health services. By doing so, the book brings a different dimension and advance health service delivery. Providing a timely and important contribution to this critical area, this book is a valuable, international resource for academics, postgraduate students and researchers in the areas of IT, big data analytics, data management and health informatics.

Covers applications of various image processing techniques in thermal imaging applications for diagnosis of different medical conditions Describes the development of a computer diagnostics system (CAD) based on thermographic data Discusses customized deep learning models for accurate diagnosis of various diseases Includes new aspects in rheumatoid arthritis and diabetes research with usage of advanced analytical tools Reviews application of feature fusion algorithms and feature reduction algorithms for accurate classification of images

In the past decade, artificial intelligence (AI) has become a disruptive force around the world, offering enormous potential for innovation but also creating hazards and risks for individuals and the societies in which they live. This volume addresses the most pressing philosophical, ethical, legal, and societal challenges posed by AI. Contributors from different disciplines and sectors explore the foundational and normative aspects of responsible AI and provide a basis for a transdisciplinary approach to responsible AI. This work, which is designed to foster future discussions to develop proportional approaches to AI governance, will enable scholars, scientists, and other actors to identify normative frameworks for AI to allow societies, states, and the international community to unlock the potential for responsible innovation in this critical field. This book is also available as Open Access on Cambridge Core.

By proposing and forming a mobile manipulator for modern multi-floor buildings, A Robotic Framework for the Mobile Manipulator: Theory and Application helps readers visualize an end-to-end workflow for making a robot system work in a targeted environment. From a product-oriented viewpoint, this book is considered as a bridge from theories to real products, in which robotic software modules and the robotic system integration are mainly concerned. In the end, readers will have an overview of how to build and integrate various single robotic modules to execute a list of designed tasks in the real world, as well as how to make a robot system work independently, without human interventions. With references and execution guidelines provided at the end of each chapter, the book will be a useful tool for developers and researchers looking to expand their knowledge about the robotics and the robotic software.

- First book to focus on deep learning-based approaches in the field of cancer diagnostics. - Covers the state of the art across a wide-range of topics. - Topics include preprocessing data, prediction of cancer susceptibility and reoccurence, detection of different cancers, complexity and challenges.

'Real science can be far stranger than science fiction, and much more satisfying' Will artificial intelligence outsmart us? Is there other intelligent life in the universe? Throughout his extraordinary career, Stephen Hawking expanded our understanding of the universe and unravelled some of its greatest mysteries. Will Artificial Intelligence Outsmart Us? considers the threat of artificial super-intelligence - as well as the likelihood of intelligent life beyond our planet. 'Modest, profound and sometimes very funny' Sunday Times Brief Answers, Big Questions: this stunning paperback series offers electrifying essays from one of the greatest minds of our age, taken from the original text of the No. 1 bestselling Brief Answers to the Big Questions.