How robots will change our world Some fear that robots could do half our jobs and even wipe us out. But is that likely? Smart machines already make our cars and clean our homes. Soon they could drive us about, teach our children, and keep our parents company. While dealing with the ethical concerns about Artificial Intelligence, Bennie Mols and Nieske Vergunst reveal the history, present and future of robots. They show how moving AI could allow the lame to walk again, rescue survivors from collapsed buildings, and boost the global fight against hunger and pollution. Welcome to a vivid view of our robot future. With 60 colour photos. Topics From dolls to industrial workers, a history of robots How robots respond to their surroundings What robots learn about human speech Why self-driving cars are safer and greener The possibilities of robots in education Meet the 'cyborgs' who learn to walk again Why evolution designs the best robots Will rogue robots take over the world? Using robots as weapons and drones What the future holds: 2100, a Robot Odyssey Table of Contents 1 A short history of robots, from dolls to androids Machines as man throughout history Mechanical dolls: forerunners of the robot Enter the working humanoid robots The next step: android robots that look like you Uncanny valley: the problem with creepy robots 2. How do robots see their surroundings? Getting to grips with a new environment Seeing through the eyes of a robot Training robots to recognise objects Robots can see what a person cannot see Feeling with whiskers: sensing the way forward Robots use electronic ears to listen 3. How does a robot brain work? A robot must learn to think like a human Keeping it simple with an insect brain Machine learning is trial and error Robots can learn without supervision The football world cup for robots Developing robot emotional intelligence 4. Giving humans a helping hand Robots suck: doing the dirty jobs at home A robotic arm reaches deep into the supply chain Joseph Engelberger, father of car factory robots Co-bots will work alongside people Coping with variation is Amazon's challenge Building a robot car 5. Learning to speak to people The problem with machine talk SHRDLU! The first experiment in robot conversation Toilets are hidden: translation problems A robotic teacher never runs out of patience 6. Robots get emotional Emotional robots encourage humans to interact with them A robot can work out how you are feeling Why am I afraid? Understanding human emotions Help! My robot looks angry Establishing a bond with a robot 7. Humans need robots and robots need humans Meet the robot psychologists Under-promise and over-deliver performance Silicon Valley utopias vs calm technology What is the best ratio of robots to humans? In the rubble: the search and rescue robot The paradox of robotization 8. Humans need robots and robots need humans Meet the robot psychologists Under-promise and over-deliver performance * Silicon Valley utopias vs calm technology What is the best ratio of robots to humans? In the rubble: the search and rescue robot The paradox of robotization 9. Evolution designs the best robots How robots travel in a bumpy world Robothand has nature's grip A two-legged walking robot The first robot baby Working out the best path through evolution 10. Swarming robots show the wisdom of crowds The power of robots working together A robot swarm without a boss Goal is mapping a building about to collapse Predicting how a robot will behave Robot swarms in the real world A robotic swarm looks for a queen 11. The importance of building ethical robots Isaac Asimov's three rules about rogue robots When robots go wrong Responsible roboticists are planning for the future Robots and the UN's development goals How will robots change the human race? Killing machines: robots in the military 12. 2100 - A Robot Odyssey The future of work in a robotic world Fusing mind and body with soft robotics And then the smart robot became creative Was that move really creative? Will robots really take over the world?

Data Ethics of Power takes a reflective and fresh look at the ethical implications of transforming everyday life and the world through the effortless, costless, and seamless accumulation of extra layers of data. By shedding light on the constant tensions that exist between ethical principles and the interests invested in this socio-technical transformation, the book bridges the theory and practice divide in the study of the power dynamics that underpin these processes of the digitalization of the world. Gry Hasselbalch expertly draws on nearly two decades of experience in the field, and key literature, to advance a better understanding of the challenges faced by big data and AI developers. She provides an innovative ethical framework for studying and governing Big-Data and Artificial Intelligence. Offering both a historical account and a theoretical analysis of power dynamics and their ethical implications, as well as incisive ideas to guide future research and governance practices, the book makes a significant contribution to the establishment of an emerging data and AI ethics discipline. This timely book is a must-read for scholars studying AI, data, and technology ethics. Policymakers in the regulatory, governance, public administration, and management sectors will find the practical proposals for a human-centric approach to big data and AI to be a valuable resource for revising and developing future policies.

A fascinating look at the trailblazing companies using artificial intelligence to create new competitive advantage, from the author of the business classic, Competing on Analytics, and the head of Deloitte's US AI practice. Though most organizations are placing modest bets on artificial intelligence, there is a world-class group of companies that are going all-in on the technology and radically transforming their products, processes, strategies, customer relationships, and cultures. Though these organizations represent less than 1 percent of large companies, they are all high performers in their industries. They have better business models, make better decisions, have better relationships with their customers, offer better products and services, and command higher prices. Written by bestselling author Tom Davenport and Deloitte's Nitin Mittal, All-In on AI looks at artificial intelligence at its cutting edge from the viewpoint of established companies like Anthem, Ping An, Airbus, and Capital One. Filled with insights, strategies, and best practices, All-In on AI also provides leaders and their teams with the information they need to help their own companies take AI to the next level. If you're curious about the next phase in the implementation of artificial intelligence within companies, or if you're looking to adopt this powerful technology in a more robust way yourself, All-In on AI will give you a rare inside look at what the leading adopters are doing, while providing you with the tools to put AI at the core of everything you do.

This incisive Handbook offers novel theoretical and doctrinal insights alongside practical guidance on some of the most challenging issues in the field of artificial intelligence and intellectual property. Featuring all original contributions from a diverse group of international thought leaders, including top academics, judges, regulators and eminent practitioners, it offers timely perspectives and research on the relationship of AI to copyright, trademark, design, patent and trade secret law. The Handbook is divided into four thematic parts, beginning with topics that address the intersection of IP and AI broadly before focusing on issues associated with specific types of IP. Chapters tackle critical legal questions, from issues with protecting AI-generated ourput to the impact of AI on how trademarks are used, offering valuable lessons on technology regulation and how technological evolution will disrupt existing legal frameworks. Scholars and students of intellectual property law and its intersections with AI and related technologies will find this Handbook 's cutting-edge contributions to be a crucial read. Its guidance on the practical legal implications of technological advances will also be of interest to IP practitioners, as well as policymakers and regulators.

Based on 20 years of research, this book lays out a proven and tested method for reaching the goal of employee happiness, analyzing individuals' communication patterns, and making them self-aware by mirroring their behaviour back to them in a privacy-respecting way. In doing so, Peter A. Gloor introduces artificial intelligence-based methods to identify personality, moral values, and ethics of individuals based on their body language and interaction with others. In this book readers will: understand the basic concepts of groupflow - when teams collaborate at their best through intrinsic motivation and positive stress learn how to use artificial intelligence (AI), machine learning (ML) and social network analysis (SNA) to analyze communication by tracking emotions, social networks, morals, and tribes successfully use virtual mirroring to create entangled teams that work together in collaborative innovation networks (COINs) synchronized and in harmony for superior performance understand how to implement virtual mirroring using these technologies. Groundbreaking and innovative, Happimetrics will be an invaluable resource for scholars and students in the fields of business analytics, information systems and organizational innovation. It will also be useful for HR professionals and AI developers who are looking to use predictive analytics to measure workforce performance.

Elgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences, business and law, expertly written by the world's leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas. Providing a comprehensive overview of the current and future uses of Artificial Intelligence (AI) in healthcare, this Advanced Introduction discusses the issues surrounding the implementation, governance, impacts and risks of utilising AI in health organizations Key Features: Advises healthcare executives on how to effectively leverage AI to advance their strategies and plans and support digital transformation Discusses AI governance, change management, workforce management and the organization of AI experimentation and implementation Analyzes AI technologies in healthcare and their impacts on patient care, medical devices, pharmaceuticals, population health, and healthcare operations Provides risk mitigation approaches to address potential AI algorithm problems, liability and regulation Essential reading for policymakers, clinical executives and consultants in healthcare, this Advanced Introduction explores how to successfully integrate AI into healthcare organizations and will also prove invaluable to students and scholars interested in technological innovations in healthcare.

As the world has entered the era of big data, there is a need to give a semantic perspective to the data to find unseen patterns, derive meaningful information, and make intelligent decisions. This 2-volume handbook set is a unique, comprehensive, and complete presentation of the current progress and future potential explorations in the field of data science and related topics. Handbook of Data Science with Semantic Technologies provides a roadmap for a new trend and future development of data science with semantic technologies. The first volume serves as an important guide towards applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for both academic researchers and industry professionals. The second volume provides a roadmap for the deployment of semantic technologies in the field of data science that enables users to create intelligence through these technologies by exploring the opportunities while eradicating the current and future challenges. The set explores the optimal use of these technologies to provide the maximum benefit to the user under one comprehensive source. This set consisting of two separate volumes can be utilized independently or together as an invaluable resource for students, scholars, researchers, professionals, and practitioners in the field.

The Future of Copyright in the Age of Artificial Intelligence offers an extensive analysis of intellectual property and authorship theories and explores the possible impact artificial intelligence might have on those theories. The author makes compelling arguments via the exploration of authorship, ownership and artificial intelligence. First, the book advocates for a more holistic approach to authorship, arguing that there is no good reason to exclude computer-generated and artificial intelligence creations from copyright. Second, it conducts an open search for the right 'candidate' for ownership. In doing so, the book explores several possible legal frameworks, including assigning ownership to the programmer, the user, the AI itself and other alternatives such as the public domain or author-in-law approaches. Third, the book explores the concept of artificial intelligence as it has developed through the years in various fields, seeking to reframe the AI legal concept. For academic scholars and law students these pages frame the theoretical discussion about artificial intelligence creations and provide in depth discussion about artificial intelligence legal challenges.

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for wide'' data (p bigger than n), including multiple testing and false discovery rates.

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

Artificial Intelligence (AI) is being rapidly introduced into the workplace, creating debate around what AI means for our work and organizations. This book gives grounded counterweight to provocative newspaper headlines by using in-depth case studies of eight organizations' experiences of implementing and using AI, providing readers with a solid understanding of what is actually happening in practice. Critical yet constructive, the authors address the challenges of implementing AI: organizing for data, testing and validating, algorithmic brokering, and changing work. Using a combination of existing literature and thorough practical examples, they provide answers to questions such as: What data do I need? When is a system good enough to actually take over tasks? And how can my employees be prepared for working with AI? The book presents four recommendations for WISE management of AI, requiring work-related insights, interdisciplinary knowledge, sociotechnical change processes, and ethical awareness. Offering insight into the unique characteristics of AI in organizations, this book will be essential reading for scholars of business and management, data analytics and information systems, technology and innovation, and computer science. With practical recommendations for managing the challenges of AI, it will also provide business managers with reflections to improve their own AI development and implementation processes.

New and emerging technologies are reshaping justice systems and transforming the role of judges. The impacts vary according to how structural reforms take place and how courts adapt case management processes, online dispute resolution systems and justice apps. Significant shifts are also occurring with the development of more sophisticated forms of Artificial Intelligence that can support judicial work or even replace judges. These developments, together with shifts towards online court processes are explored in Judges, Technology and Artificial Intelligence. By considering how different jurisdictions are approaching current and future technological shifts and in particular by focusing on the different approaches in the US, UK, Australia and China and elsewhere, the author draws a rich comparative exploration of justice technology trends. Judicial commentary is considered as well as the growing scholarly discourse about these trends. Ethical and user centred design options are examined in the context of how responsive judges engage with supportive, replacement and disruptive technologies in courts. This book explores current issues regarding the responsiveness of the justice system in the pandemic era. In addition, how technology can respond and shift justice processes is a growing field of research, for judges, scholars, students and justice commentators. It provides a much-needed resource on an increasingly important topic.

This timely book presents a detailed analysis of the role of law and regulation in the utilisation of Artificial Intelligence (AI) in the media sector. As well as contributing to the wider discussion on law and AI, the book also digs deeper by exploring pressing issues at the intersections of AI, media, and the law. Chapters critically re-examine various rights and responsibilities from the perspectives of incentives for accountable utilisation of AI in the industry. Featuring chapters from leading scholars in the field, Artificial Intelligence and the Media provides a timely and in-depth research-based contribution to complex themes - especially at the interface of new technology (including AI) with media and regulation. Analysing both legislative and ethical solutions, chapters explore what "AI" and "accountability" mean in terms of media practices, principles, and power relations, as well as how to address the AI revolution with informed law and policy in order to incentivise accountable utilisation of AI and to reduce negative societal impacts. Offering ideas for further research in the area, this book is key reading for academics and researchers in the fields of information and media law, regulation, and technology law. It may also interest media law practitioners, with research-based guidance for everyday practices and tools to prepare for future developments in the area.

The Future of Creative Work provides a unique overview of the changing nature of creative work, examining how digital developments and the rise of intangible capital are causing an upheaval in the social institutions of work. It offers a profound insight into how this technological and social evolution will affect creative professions. Expert international contributors explore how robotics, artificial intelligence, blockchain, global digital platforms and autonomous systems will shape the design, production and consumption of culture. Taking a multidisciplinary approach incorporating creative industries studies, business, education and economics, the book analyses the technological drivers of disruption in the world of creative work. Chapters reveal how these changes will create new axes of power and inequality in the global sphere of creative work, predicting that conventional creative professions will be challenged and different species of creative work will evolve as a result. By charting the impact of digital and technological developments, The Future of Creative Work challenges traditional views of creative work, careers and education. This book will be a valuable resource for students and researchers undertaking creative industries studies. Its discussion of the application of creative careers across the economy will also be beneficial for scholars and practitioners interested in business, economics, and advertising and marketing studies.

Artificial Intelligence for Sustainable Value Creation provides a detailed and insightful exploration of both the possibilities and the challenges that accompany widespread Artificial Intelligence. Providing a cutting edge analysis of the impact of AI in business and society, the editors offer an opportunity to assess what is known about managing other forms of information systems, strategy, and marketing, and to re-examine this knowledge in situations involving AI. This comprehensive book explores how human- centric AI systems create value inside organizations, distinguishing three main components: ethical value, societal value, and business value. Using a multidisciplinary perspective, this discerning book addresses the interests of a wide spectrum of practitioners, students, and researchers alike who are interested in identifying the value generated by AI systems in management.

Based on 20 years of research, this book lays out a proven and tested method for reaching the goal of employee happiness, analyzing individuals' communication patterns, and making them self-aware by mirroring their behaviour back to them in a privacy-respecting way. In doing so, Peter A. Gloor introduces artificial intelligence-based methods to identify personality, moral values, and ethics of individuals based on their body language and interaction with others. In this book readers will: understand the basic concepts of groupflow - when teams collaborate at their best through intrinsic motivation and positive stress learn how to use artificial intelligence (AI), machine learning (ML) and social network analysis (SNA) to analyze communication by tracking emotions, social networks, morals, and tribes successfully use virtual mirroring to create entangled teams that work together in collaborative innovation networks (COINs) synchronized and in harmony for superior performance understand how to implement virtual mirroring using these technologies. Groundbreaking and innovative, Happimetrics will be an invaluable resource for scholars and students in the fields of business analytics, information systems and organizational innovation. It will also be useful for HR professionals and AI developers who are looking to use predictive analytics to measure workforce performance.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

This incisive book provides a much-needed examination of the legal issues arising from the data economy, particularly in the light of the expanding role of algorithms and artificial intelligence in business and industry. In doing so, it discusses the pressing question of how to strike a balance in the law between the interests of a variety of stakeholders, such as AI industry, businesses and consumers. Investigating issues at the intersection of trade secrets and personal data as well as the potential legal conflicts to which this can give rise, Gintare Surblyte-Namaviciene examines what kinds of changes to the legal framework the growing data economy may require. Through an analysis of the way in which EU competition law may tackle algorithm-related problems the book also identifies a regulatory gap in the case of algorithmic manipulation in the business-to-consumer relationship. The book further argues that control by public bodies over terms and conditions often used in the data economy may be necessary for the sake of consumer protection. Scholars in competition law and regulatory governance, particularly those with an interest in the impacts of technology, will find this to be critical reading. It will also be beneficial to practitioners and policy makers working at the intersections of regulation and technology.

An edge-of-your-seat psychological thriller from the acclaimed author of Every Line of You - A Good Girl's Guide to Murder meets virtual reality ... 'A pacy, intelligent novel that will have readers racing to get to the final page.' THE BOOKSELLER 'A pixelated whirlwind of a ride through the darkest secrets of the human mind, explored through the lens of virtual reality gaming!' LEE NEWBERY, AUTHOR OF THE LAST FIREFOX Lola's been selected to play a new virtual reality game - Better Than Life. In her game, she's loved. Is beautiful. Can do whatever she wants. But she quickly breaks the one rule: not to recreate people or places from real life. She recreates her house and a boy she fancies. Soon she's skipping school to play. But Lola has secrets - dark ones that begin to surface inside her game - and the more she tries to fix her problems, the more she overlooks a much bigger threat ... An original, high-concept psychological thriller - Holly Jackson meets VR horror Set in an exclusive boarding school, this rollercoaster ride of a story involves an unreliable narrator,immersive gaming, a dark secret and a huge twist Perfect for fans of Cynthia Murphy and Kathryn Foxfield PRAISE FOR EVERY LINE OF YOU: 'What a debut. So tense - and it didn't lead where I thought it was going.' SUE WALLMAN, author of YOUR TURN TO DIE 'Frankenstein meets Heathers. Bonnie and Clyde for the digital age, Every Line of You is a gripping thriller about the power of AI and a fresh twist on the intensity of first love.' AMY MCCAW, author of MINA AND THE UNDEAD

Elgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences and law, expertly written by the world's leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas. Woodrow Barfield and Ugo Pagallo present a succinct introduction to the legal issues related to the design and use of artificial intelligence (AI). Exploring human rights, constitutional law, data protection, criminal law, tort law, and intellectual property law, they consider the laws of a number of jurisdictions including the US, the European Union, Japan, and China, making reference to case law and statutes. Key features include: a critical insight into human rights and constitutional law issues which may be affected by the use of AI discussion of the concept of legal personhood and how the law might respond as AI evolves in intelligence an introduction to current laws and statutes which apply to AI and an identification of the areas where future challenges to the law may arise. This Advanced Introduction is ideal for law and social science students with an interest in how the law applies to AI. It also provides a useful entry point for legal practitioners seeking an understanding of this emerging field.

This provocative book investigates the relationship between law and artificial intelligence (AI) governance, and the need for new and innovative approaches to regulating AI and big data in ways that go beyond market concerns alone and look to sustainability and social good. Taking a multidisciplinary approach, the contributors demonstrate the interplay between various research methods, and policy motivations, to show that law-based regulation and governance of AI is vital to efforts at ensuring justice, trust in administrative and contractual processes, and inclusive social cohesion in our increasingly technologically-driven societies. The book provides valuable insights on the new challenges posed by a rapid reliance on AI and big data, from data protection regimes around sensitive personal data, to blockchain and smart contracts, platform data reuse, IP rights and limitations, and many other crucial concerns for law's interventions. The book also engages with concerns about the 'surveillance society', for example regarding contact tracing technology used during the Covid-19 pandemic. The analytical approach provided will make this an excellent resource for scholars and educators, legal practitioners (from constitutional law to contract law) and policy makers within regulation and governance. The empirical case studies will also be of great interest to scholars of technology law and public policy. The regulatory community will find this collection offers an influential case for law's relevance in giving institutional enforceability to ethics and principled design.

The hidden costs of artificial intelligence-from natural resources and labor to privacy, equality, and freedom "This study argues that [artificial intelligence] is neither artificial nor particularly intelligent. . . . A fascinating history of the data on which machine-learning systems are trained."-New Yorker "A valuable corrective to much of the hype surrounding AI and a useful instruction manual for the future."-John Thornhill, Financial Times "It's a masterpiece, and I haven't been able to stop thinking about it."-Karen Hao, senior editor, MIT Tech Review What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? Drawing on more than a decade of research, award-winning scholar Kate Crawford reveals how AI is a technology of extraction: from the minerals drawn from the earth, to the labor pulled from low-wage information workers, to the data taken from every action and expression. This book reveals how this planetary network is fueling a shift toward undemocratic governance and increased inequity. Rather than taking a narrow focus on code and algorithms, Crawford offers us a material and political perspective on what it takes to make AI and how it centralizes power. This is an urgent account of what is at stake as technology companies use artificial intelligence to reshape the world.

Elgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences, business and law, expertly written by the world's leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas. Providing a comprehensive overview of the current and future uses of Artificial Intelligence (AI) in healthcare, this Advanced Introduction discusses the issues surrounding the implementation, governance, impacts and risks of utilising AI in health organizations Key Features: Advises healthcare executives on how to effectively leverage AI to advance their strategies and plans and support digital transformation Discusses AI governance, change management, workforce management and the organization of AI experimentation and implementation Analyzes AI technologies in healthcare and their impacts on patient care, medical devices, pharmaceuticals, population health, and healthcare operations Provides risk mitigation approaches to address potential AI algorithm problems, liability and regulation Essential reading for policymakers, clinical executives and consultants in healthcare, this Advanced Introduction explores how to successfully integrate AI into healthcare organizations and will also prove invaluable to students and scholars interested in technological innovations in healthcare.

This textbook offers a tutorial introduction to robotics and Computer Vision which is light and easy to absorb. The practice of robotic vision involves the application of computational algorithms to data. Over the fairly recent history of the fields of robotics and computer vision a very large body of algorithms has been developed. However this body of knowledge is something of a barrier for anybody entering the field, or even looking to see if they want to enter the field - What is the right algorithm for a particular problem?, and importantly: How can I try it out without spending days coding and debugging it from the original research papers? The author has maintained two open-source MATLAB Toolboxes for more than 10 years: one for robotics and one for vision. The key strength of the Toolboxes provide a set of tools that allow the user to work with real problems, not trivial examples. For the student the book makes the algorithms accessible, the Toolbox code can be read to gain understanding, and the examples illustrate how it can be used -instant gratification in just a couple of lines of MATLAB code. The code can also be the starting point for new work, for researchers or students, by writing programs based on Toolbox functions, or modifying the Toolbox code itself. The purpose of this book is to expand on the tutorial material provided with the toolboxes, add many more examples, and to weave this into a narrative that covers robotics and computer vision separately and together. The author shows how complex problems can be decomposed and solved using just a few simple lines of code, and hopefully to inspire up and coming researchers. The topics covered are guided by the real problems observed over many years as a practitioner of both robotics and computer vision. It is written in a light but informative style, it is easy to read and absorb, and includes a lot of Matlab examples and figures. The book is a real walk through the fundamentals light and color, camera modelling, image processing, feature extraction and multi-view geometry, and bring it all together in a visual servo system. "An authoritative book, reaching across fields, thoughtfully conceived and brilliantly accomplished Oussama Khatib, Stanford

Data is humanity's most important new resource. It has the capacity to provide insight into every aspect of our lives, the planet and the universe at large; it changes not only what we know but also how we know it. Exploiting the value of data could improve our existence as much as - if not more than - previous technological revolutions. Yet data without empathy is useless. There is a tendency in data science to forget about the human needs and feelings of the people who make up the data, the people who work with the data, and those expected to understand the results. Without empathy, this precious resource is at best underused, at worst misused. Data: A Guide to Humans will help you understand how to properly exploit data, why this is so important, and how companies and governments are currently using data. It makes a compelling case for empathy as the crucial factor in elevating our understanding of data to something which can make a lasting and essential contribution to your business, your life and maybe even the world.