Features Only mathematical prerequisite is an elementary knowledge of calculus Accessible to anyone with an interest in AI and its mathematics and computer science Suitable as a supplementary reading for a course in AI or the History of Mathematics and Computer Science in regard to artificial intelligence.

- Braces readers for future knowledge. - Provides a handle on the power of learning mechanisms. - Assesses the progress made by using AI to better understand the mind.

- Braces readers for future knowledge. - Provides a handle on the power of learning mechanisms. - Assesses the progress made by using AI to better understand the mind.

This book consolidates and summarizes smart technologies like IoT, edge computing, and AI used in different aspects of waste material management, mitigation, and recycling for a sustainable environment. One of the cases explains how IoT-based systems and wireless sensors can be used to continuously detect common pollutants such as volatile organic compounds (VOCs), carbon monoxide, and particulate matter (PM) and how the data collected are used to assess the overall air quality and determine actions for improvements. A collection of practical case studies, this book provides a comprehensive knowledge in smart waste management to readers in universities, research centers, and industries.

This book discusses the technological aspects for the implementation of Society 5.0. The foundation and recent advances of emerging technologies such as artificial intelligence, data science, Internet of Things, and Big Data for the realization of Society 5.0 are covered. Practical solutions to existing problems, examples, and case studies are also offered. Society 5.0 and the Future of Emerging Computational Technologies: Practical Solutions, Examples, and Case Studies discusses technologies such as machine learning, artificial intelligence, and Internet of Things for the implementation of Society 5.0. It offers a firm foundation and understanding of the recent advancements in various domains such as data analytics, neural networks, computer vision, and robotics, along with practical solutions to existing problems in fields such as healthcare, manufacturing industries, security, and infrastructure management. Applications and implementations are highlighted along with the correlation between technologies. Examples and case studies are presented throughout the book to augment text. This book can be used by research scholars in the engineering domain who wish to gain knowledge and contribute towards a modern and secure future society. The book will also be useful as a reference at universities for postgraduate students who are interested in technological advancements.

The strategically sound combination of edge computing and artificial intelligence (AI) results in a series of distinct innovations and disruptions enabling worldwide enterprises to visualize and realize next-generation software products, solutions and services. Businesses, individuals, and innovators are all set to embrace and experience the sophisticated capabilities of Edge AI. With the faster maturity and stability of Edge AI technologies and tools, the world is destined to have a dazzling array of edge-native, people-centric, event-driven, real-time, service-oriented, process-aware, and insights-filled services. Further on, business workloads and IT services will become competent and cognitive with state-of-the-art Edge AI infrastructure modules, AI algorithms and models, enabling frameworks, integrated platforms, accelerators, high-performance processors, etc. The Edge AI paradigm will help enterprises evolve into real-time and intelligent digital organizations. Applied Edge AI: Concepts, Platforms, and Industry Use Cases focuses on the technologies, processes, systems, and applications that are driving this evolution. It examines the implementation technologies; the products, processes, platforms, patterns, and practices; and use cases. AI-enabled chips are exclusively used in edge devices to accelerate intelligent processing at the edge. This book examines AI toolkits and platforms for facilitating edge intelligence. It also covers chips, algorithms, and tools to implement Edge AI, as well as use cases. FEATURES The opportunities and benefits of intelligent edge computing Edge architecture and infrastructure AI-enhanced analytics in an edge environment Encryption for securing information An Edge AI system programmed with Tiny Machine learning algorithms for decision making An improved edge paradigm for addressing the big data movement in IoT implementations by integrating AI and caching to the edge Ambient intelligence in healthcare services and in development of consumer electronic systems Smart manufacturing of unmanned aerial vehicles (UAVs) AI, edge computing, and blockchain in systems for environmental protection Case studies presenting the potential of leveraging AI in 5G wireless communication

The Problem? Companies are failing to deliver on AI and analytics with over half stating they are "not yet treating data as a business asset". Over half admit that they are not competing on data and analytics. Seven out of 10 companies in a 2020 MIT study reported minimal or no impact from AI so far. Among the 90% of companies that have made some investment in AI, fewer than 2 out of 5 (40%) report business gains from AI in the past three years. And only about 25% of organizations have actually forged this data-driven culture. Is investment lacking? No. Companies now are spending more than ever in data, analytics, and AI technologies. Is it a lack of technology? No. There are fascinating breakthroughs occurring on all fronts with image, voice, and streaming pattern recognition on the forefront. Is it a lack of technical talent? Not really. While some studies cite that we need to train more data scientists, developers, and related professionals, the curve of demand by supply is dampening. Is it a lack of creating an executable strategic plan? Yes. While there has been a lot of strategic wishing, organizations lack meaningful strategic plans. Specifically, the development of executable strategies and the leadership to see these strategies brought to fruition. This is the problem. Lack of execution and lack of incorporating key components that align and enable execution of the business strategy to delivery is killing AI and analytics programs. Scott Burk and Gary D. Miner have written this book for executives at all levels who are charged with executing on analytics that need to address this issue. The book provides unique insights into repairing the gaps that programs need to fill to provide value from analytics programs. It complements their three-part series, It's All Analytics! by focusing on leadership decisions that augment data literacy, organizational architecture, and AI case studies.

Address brain and cognitive intelligence based control Combine neuroscience with robotics control Combine biomechanics with robotics control Provide applications used in such as human-robot interaction

The book Digital Health Transformation with Blockchain and Artificial Intelligence covers the global digital revolution in the field of healthcare sector. The population has been overcoming the COVID-19 period; therefore, we need to establish intelligent digital healthcare systems using various emerging technologies like Blockchain and Artificial Intelligence. Internet of Medical Things is the technological revolution that has included the element of "smartness" in the healthcare industry and also identifying, monitoring, and informing service providers about the patient's clinical information with faster delivery of care services. This book highlights the important issues i.e. (a) How Internet of things can be integrated with the healthcare ecosystem for better diagnostics, monitoring, and treatment of the patients, (b) Artificial Intelligence for predictive and preventive healthcare systems, (c) Blockchain for managing healthcare data to provide transparency, security, and distributed storage, and (d) Effective remote diagnostics and telemedicine approach for developing smart care. The book encompasses chapters belong to the blockchain, Artificial Intelligence, and Big health data technologies. Features: Blockchain and internet of things in healthcare systems Secure Digital Health Data Management in Internet of Things Public Perception towards AI-Driven Healthcare Security, privacy issues and challenges in adoption of smart digital healthcare Big data analytics and Internet of things in the pandemic era Clinical challenges for digital health revolution Artificial intelligence for advanced healthcare Future Trajectory of Healthcare with Artificial Intelligence 9 Parkinson disease pre-diagnosis using smart technologies Emerging technologies to combat the COVID-19 Machine Learning and Internet of Things in Digital Health Transformation Effective Remote Healthcare and Telemedicine Approaches Legal implication of blockchain technology in public health This Book on "Digital Health Transformation with Blockchain and Artificial Intelligence" aims at promoting and facilitating exchanges of research knowledge and findings across different disciplines on the design and investigation of secured healthcare data analytics. It can also be used as a textbook for a Masters course in security and biomedical engineering. This book will also present new methods for the medical data analytics, blockchain technology, and diagnosis of different diseases to improve the quality of life in general, and better integration into digital healthcare.

The Problem? Companies are failing to deliver on AI and analytics with over half stating they are "not yet treating data as a business asset". Over half admit that they are not competing on data and analytics. Seven out of 10 companies in a 2020 MIT study reported minimal or no impact from AI so far. Among the 90% of companies that have made some investment in AI, fewer than 2 out of 5 (40%) report business gains from AI in the past three years. And only about 25% of organizations have actually forged this data-driven culture. Is investment lacking? No. Companies now are spending more than ever in data, analytics, and AI technologies. Is it a lack of technology? No. There are fascinating breakthroughs occurring on all fronts with image, voice, and streaming pattern recognition on the forefront. Is it a lack of technical talent? Not really. While some studies cite that we need to train more data scientists, developers, and related professionals, the curve of demand by supply is dampening. Is it a lack of creating an executable strategic plan? Yes. While there has been a lot of strategic wishing, organizations lack meaningful strategic plans. Specifically, the development of executable strategies and the leadership to see these strategies brought to fruition. This is the problem. Lack of execution and lack of incorporating key components that align and enable execution of the business strategy to delivery is killing AI and analytics programs. Scott Burk and Gary D. Miner have written this book for executives at all levels who are charged with executing on analytics that need to address this issue. The book provides unique insights into repairing the gaps that programs need to fill to provide value from analytics programs. It complements their three-part series, It's All Analytics! by focusing on leadership decisions that augment data literacy, organizational architecture, and AI case studies.

utilizes the Consolidated Serial Homicide Offender Database, one of the largest and most robust open access databases of multiple murders available illustrated with in-depth case studies of SHOs, such as Felix Vail, Michael Sumpter, the Seminole Heights Killer, and the Austin Bomber provides commentary from those who have used these patterning methods in practice, in addition to laying out how to put the current suite of data tools to use within organizations

Bringing a unique perspective to the burgeoning ethical and legal issues surrounding the presence of artificial intelligence in our daily lives, the book uses theory and practice on animal rights and the rights of nature to assess the status of robots. Through extensive philosophical and legal analyses, the book explores how rights can be applied to nonhuman entities. This task is completed by developing a framework useful for determining the kinds of personhood for which a nonhuman entity might be eligible, and a critical environmental ethic that extends moral and legal consideration to nonhumans. The framework and ethic are then applied to two hypothetical situations involving real-world technology-animal-like robot companions and humanoid sex robots. Additionally, the book approaches the subject from multiple perspectives, providing a comparative study of legal cases on animal rights and the rights of nature from around the world and insights from structured interviews with leading experts in the field of robotics. Ending with a call to rethink the concept of rights in the Anthropocene, suggestions for further research are made. An essential read for scholars and students interested in robot, animal and environmental law, as well as those interested in technology more generally, the book is a ground-breaking study of an increasingly relevant topic, as robots become ubiquitous in modern society. The Open Access version of this book, available at http://www.taylorfrancis.com/books/e/ISBN, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.

* The first book to take an interdisciplinary and international approach to understanding how our everyday lives are being affected by automated decision-making (ADM) * Showcases groundbreaking research in this cutting-edge field but will also be accessible enough to be useful for upper-level undergraduate and postgraduate teaching * Covers a uniquely wide range of ADM technologies, geographical and sociocultural contexts, and theoretical perspectives not reflected in other ADM books which tend to focus solely on the USA.

This book highlights the state-of-the-art research on data usage, security, and privacy in the scenarios of the Internet of Things (IoT), along with related applications using Machine Learning and Big Data technologies to design and make efficient Internet-compatible IoT systems. ICT and Data Sciences brings together IoT and Machine Learning and provides the careful integration of both, along with many examples and case studies. It illustrates the merging of two technologies while presenting basic to high-level concepts covering different fields and domains such as the Hospitality and Tourism industry, Smart Clothing, Cyber Crime, Programming, Communications, Business Intelligence, all in the context of the Internet of Things. The book is written for researchers and practitioners, working in Information Communication Technology and Computer Science.

Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest. An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date. Fully revised and expanded Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from astronomical surveys Uses a freely available Python codebase throughout Ideal for graduate students, advanced undergraduates, and working astronomers

Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning, Internet of Things (IoT), and big data, accelerate the deployment of biomedical devices in various healthcare applications. This volume explores how artificial intelligence (AI) can be applied to these expert systems by mimicking the human expert's knowledge in order to predict and monitor the health status in real time. The accuracy of the AI systems is drastically increasing by using machine learning, digitized medical data acquisition, wireless medical data communication, and computing infrastructure AI approaches, helping to solve complex issues in the biomedical industry and playing a vital role in future healthcare applications. The volume takes a multidisciplinary perspective of employing these new applications in biomedical engineering, exploring the combination of engineering principles with biological knowledge that contributes to the development of revolutionary and life-saving concepts.

Provides a comprehensive overview on the recent developments on clinical decision support systems, precision health and data science in medicine Examines the advancements, challenges and opportunities of using AI in medical and health applications Includes 10 cases for practical application and reference Reviews melanoma detection by deep learning techniques

Covers computational Intelligence techniques like fuzzy sets, artificial neural networks, deep neural networks, and genetic algorithm for Healthcare systems Provides easy understanding concepts like signal and image filtering techniques Includes discussion over filtering and classification problems Details studies with medical signal (ECG, EEG, EMG) and image (X-rays, FMRI, CT) datasets Describes evolution parameters such as signal-to-noise ratio, mean square error, accuracy, precision, and recall

AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers. Key Features: Using ML methods by itself doesn't ensure building classifiers that generalize well for new data Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experiments Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias Developing of SAS-based table-driven environment allows managing all meta-data related to the proposed AI framework and creating interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework are available on GitHub

This book examines the implications of disruptive technologies of the Fourth Industrial Revolution (4IR) on military innovation and the use of force. It provides an in-depth understanding of how both large and small militaries are seeking to leverage 4IR emerging technologies and the effects such technologies may have on future conflicts. The 4th Industrial Revolution (4IR), the confluence of disruptive changes brought by emerging technologies such as artificial intelligence, robotics, nanotechnologies, and autonomous systems, has a profound impact on the direction and character of military innovation and use of force. The core themes in this edited volume reflect on the position of emerging technologies in the context of previous Revolutions in Military Affairs; compare how large resource-rich states (US, China, Russia) and small resource-limited states (Israel, Sweden, Norway) are adopting and integrating novel technologies and explore the difference between various innovation and adaptation models. The book also examines the operational implications of emerging technologies in potential flashpoints such as the South China Sea and the Baltic Sea. Written by a group of international scholars, this book uncovers the varying 4IR defence innovation trajectories, enablers, and constraints in pursuing military-technological advantages that will shape the character of future conflicts. The chapters in this book were originally published as a special issue of the Journal of Strategic Studies.

* Fascinating cross-disciplinary work encompassing, AI, cognitive science, learning science, creative writing and thinking skills * Explores the role of the next wave of AI in creativity, education, literature and literacy * Written by experts in computing, education and creative writing * Explores the cutting edge and the limits of simulations of human creativity

Provides a comprehensive guide about how to use machine vision for Industry 4.0 applications like analysis of images for automated inspections, object detection, object tracking etc. Includes case studies of Robotics Internet of Things with its current and future applications in Healthcare, Agriculture, Transportation, etc. It highlights the inclusion of impaired people in industry, like intelligent assistant that helps deaf-mute people to transmit instructions and warnings in a manufacturing process. It examines the significant technological advancements in machine vision for industrial Internet of things and explores the commercial benefits using the real world applications from healthcare to transportation. Provides a conceptual framework of Machine vision for the various Industrial applications. Addresses scientific aspects for a wider audience such as senior and junior engineers, undergraduate and post-graduate students, researchers, and anyone else interested in the trends, development, and opportunities for the Machine Vision for Industry 4.0 applications.

'Rana el Kaliouby's vision for how technology should work in parallel with empathy is bold, inspired and hopeful' Arianna Huffington, founder and CEO of Thrive Global 'This lucid and captivating book by a renowned pioneer of emotion-AI tackles one of the most pressing issues of our time: How can we ensure a future where this technology empowers rather than surveils and manipulates us?' Max Tegmark, professor of physics at Massachusetts Institute of Technology and author of Life 3.0 We are entering an empathy crisis. Most of our communication is conveyed through non-verbal cues - facial expressions, tone of voice, body language - nuances that are completely lost when we interact through our smartphones and other technology. The result is a digital universe that's emotion-blind - a society lacking in empathy. Rana el Kaliouby discovered this when she left Cairo, a newly-married, Muslim woman, to take up her place at Cambridge University to study computer science. Many thousands of miles from home, she began to develop systems to help her better connect with her family. She started to pioneer the new field of Emotional Intelligence (EI). She now runs her company, Affectiva (the industry-leader in this emerging field) that builds EI into our technology and develops systems that understand humans the way we understand one another. In a captivating memoir, Girl Decoded chronicles el Kaliouby's mission to humanise technology and what she learns about humanity along the way.

This handbook provides an overview of research related to smart technologies and how they permeate the economic and social fabric. It covers a wide spectrum of topics and issues raised in the debate surrounding the increasing importance of smart technologies. It takes on a strongly multi- and interdisciplinary perspective, providing readers from different backgrounds and with varying knowledge of this topic with a comprehensive and comprehensible overview of the main upcoming technological trends from a scientifically eclectic viewpoint. This handbook draws together an international team of researchers from different scientific disciplines. The list of contributors comprises authors from Europe, North America, Australia and South Korea, and includes both internationally outstanding scientists and experts from a more policy-related and/or industry-related background.