Social machines are a type of network connected by interactive digital devices made possible by the ubiquitous adoption of technologies such as the Internet, the smartphone, social media and the read/write World Wide Web, connecting people at scale to document situations, cooperate on tasks, exchange information, or even simply to play. Existing social processes may be scaled up, and new social processes enabled, to solve problems, augment reality, create new sources of value, and disrupt existing practice. This book considers what talents one would need to understand or build a social machine, describes the state of the art, and speculates on the future, from the perspective of the EPSRC project SOCIAM – The Theory and Practice of Social Machines. The aim is to develop a set of tools and techniques for investigating, constructing and facilitating social machines, to enable us to narrow down pragmatically what is becoming a wide space, by asking ‘when will it be valuable to use these methods on a sociotechnical system?’ The systems for which the use of these methods adds value are social machines in which there is rich person-to-person communication, and where a large proportion of the machine’s behaviour is constituted by human interaction.

Tracking the evolution of digital technology is no easy task; changes happen so fast that keeping pace presents quite a challenge. This is, nevertheless, the aim of the Digital Enlightenment Yearbook. This book is the third in the series which began in 2012 under the auspices of the Digital Enlightenment Forum. This year, the focus is on the relationship of individuals with their networks, and explores “Social networks and social machines, surveillance and empowerment”. In what is now the well-established tradition of the yearbook, different stakeholders in society and various disciplinary communities (technology, law, philosophy, sociology, economics, policymaking) bring their very different opinions and perspectives to bear on this topic. The book is divided into four parts: the individual as data manager; the individual, society and the market; big data and open data; and new approaches. These are bookended by a Prologue and an Epilogue, which provide illuminating perspectives on the discussions in between. The division of the book is not definitive; it suggests one narrative, but others are clearly possible. The 2014 Digital Enlightenment Yearbook gathers together the science, social science, law and politics of the digital environment in order to help us reformulate and address the timely and pressing questions which this new environment raises. We are all of us affected by digital technology, and the subjects covered here are consequently of importance to us all.

Today, modern business is adrift in a sea of connectivity and potential. Where once an organization’s IT needs could be met from within its four walls, we now see many mission-critical systems reaching out beyond an enterprise’s traditional boundaries. This is pushing solutions design well beyond the comfort zone of Enterprise Architecture and out into a world of hyper-enterprise systems. This is a world of complexity and scale, where the changing status quo demands that organizations keep up or die. To survive, their IT systems must also evolve as the context of their environment(s) ebbs and flows. The result is a dynamic, interconnected web of critical business advantage, balanced against uncertainty and risk, and infused with AI. This is the world of Ecosystems Architecture. This book will introduce you to the challenges of designing hyper-enterprise IT systems and the tools you will need as the era of Ecosystems Architecture dawns. It explains the proximity of new thinking to long-held architectural tenets and outlines how and why thinking has moved on. It also reviews existing frameworks and explains how and why they fit into the broader landscape of architectural thinking. About The Open Group Press The Open Group Press is an imprint of The Open Group for advancing knowledge of information technology by publishing works from individual authors within The Open Group membership that are relevant to advancing The Open Group mission of Boundaryless Information FlowTM. The key focus of The Open Group Press is to publish high-quality monographs, as well as introductory technology books intended for the general public, and act as a complement to The Open Group Standards, Guides, and White Papers. The views and opinions expressed in this book are those of the authors, and do not necessarily reflect the consensus position of The Open Group members or staff.

If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought? Machine learning—programming computers to learn from data—has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking. Mackenzie focuses on machine learners—either humans and machines or human-machine relations—situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms—writing code and writing about code—and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures. Mackenzie's account of machine learning locates places in which a sense of agency can take root. His archaeology of the operational formation of machine learning does not unearth the footprint of a strategic monolith but reveals the local tributaries of force that feed into the generalization and plurality of the field.

The goal of the book is to present the latest research on the new challenges of data technologies. It will offer an overview of the social, ethical and legal problems posed by group profiling, big data and predictive analysis and of the different approaches and methods that can be used to address them. In doing so, it will help the reader to gain a better grasp of the ethical and legal conundrums posed by group profiling. The volume first maps the current and emerging uses of new data technologies and clarifies the promises and dangers of group profiling in real life situations. It then balances this with an analysis of how far the current legal paradigm grants group rights to privacy and data protection, and discusses possible routes to addressing these problems. Finally, an afterword gathers the conclusions reached by the different authors and discuss future perspectives on regulating new data technologies.

The book focuses on Social Collective Intelligence, a term used to denote a class of socio-technical systems that combine, in a coordinated way, the strengths of humans, machines and collectives in terms of competences, knowledge and problem solving capabilities with the communication, computing and storage capabilities of advanced ICT. Social Collective Intelligence opens a number of challenges for researchers in both computer science and social sciences; at the same time it provides an innovative approach to solve challenges in diverse application domains, ranging from health to education and organization of work. The book will provide a cohesive and holistic treatment of Social Collective Intelligence, including challenges emerging in various disciplines (computer science, sociology, ethics) and opportunities for innovating in various application areas. By going through the book the reader will gauge insight and knowledge into the challenges and opportunities provided by this new, exciting, field of investigation. Benefits for scientists will be in terms of accessing a comprehensive treatment of the open research challenges in a multidisciplinary perspective. Benefits for practitioners and applied researchers will be in terms of access to novel approaches to tackle relevant problems in their field. Benefits for policy-makers and public bodies representatives will be in terms of understanding how technological advances can support them in supporting the progress of society and economy.

A multidisciplinary introduction to the field of computational creativity, analyzing the impact of advanced generative technologies on art and music. As algorithms get smarter, what role will computers play in the creation of music, art, and other cultural artifacts? Will they be able to create such things from the ground up, and will such creations be meaningful? In Beyond the Creative Species, Oliver Bown offers a multidisciplinary examination of computational creativity, analyzing the impact of advanced generative technologies on art and music. Drawing on a wide range of disciplines, including artificial intelligence and machine learning, design, social theory, the psychology of creativity, and creative practice research, Bown argues that to understand computational creativity, we must not only consider what computationally creative algorithms actually do, but also examine creative artistic activity itself.

In recent times, physicists have come to appreciate information’s central role in the universe’s grand plan. That and the fact that an explicit understanding of the informational relationships involved may well be key to unlocking many of the universe’s deepest secrets. That makes the birth of both Computer and Information Science not only essential to the explosion of modern technological success, but also to our understanding of reality itself. In recognizing that, what unfolds is a story not only about Alan Turing and his pioneering colleagues, but also great thinkers like Albert Einstein, Michael Faraday, Ludwig Wittgenstein and others. It therefore pulls in much of modern history and touches on seminal events like the birth of the atomic bomb. It also hints at the reasons behind the various social and political divides we see in the world today. So, in many ways, the story of how we became more informed about information is also the story of the modern age. What you will read of here is the role that information plays in that ongoing saga and many of the twists and turns that have brought us to where we are with information today. In it you will learn that, unbeknown to Turing and others, their work would not only help overthrow the Nazis and thaw the chilling atmosphere of the Cold War to come, but also echo down the ages to remain relevant in a conflict still raging today. That sees the Computer and Information Scientists at loggerheads as they fight to find a right and justifiable place for meaning in information’s definition. About The Open Group Press The Open Group Press is an imprint of The Open Group for advancing knowledge of information technology by publishing works from individual authors within The Open Group membership that are relevant to advancing The Open Group mission of Boundaryless Information FlowTM. The key focus of The Open Group Press is to publish high-quality monographs, as well as introductory technology books intended for the general public, and act as a complement to The Open Group standards, guides, and white papers. The views and opinions expressed in this book are those of the authors, and do not necessarily reflect the consensus position of The Open Group members or staff.

This book presents the latest research on the software crowdsourcing approach to develop large and complex software in a cloud-based platform. It develops the fundamental principles, management organization and processes, and a cloud-based infrastructure to support this new software development approach. The book examines a variety of issues in software crowdsourcing processes, including software quality, costs, diversity of solutions, and the competitive nature of crowdsourcing processes. Furthermore, the book outlines a research roadmap of this emerging field, including all the key technology and management issues for the foreseeable future. Crowdsourcing, as demonstrated by Wikipedia and Facebook for online web applications, has shown promising results for a variety of applications, including healthcare, business, gold mining exploration, education, and software development. Software crowdsourcing is emerging as a promising solution to designing, developing and maintaining software. Preliminary software crowdsourcing practices and platforms, including Apple's App Store and TopCoder, demonstrate the advantages of crowdsourcing in terms of software ecosystem expansion and product quality improvement.

Offers a clear view of the utility and place for survey data within the broader Big Data ecosystem This book presents a collection of snapshots from two sides of the Big Data perspective. It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations. It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data. Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more sources of data, with numerous chapters providing consistent illustrations and examples of survey data enriching the evaluation of Big Data sources. Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented. Topics covered include: Total Error Frameworks for Found Data; Performance and Sensitivities of Home Detection on Mobile Phone Data; Assessing Community Wellbeing Using Google Street View and Satellite Imagery; Using Surveys to Build and Assess RBS Religious Flag; and more. Presents groundbreaking survey methods being utilized today in the field of Big Data Explores how machine learning methods can be applied to the design, collection, and analysis of social science data Filled with examples and illustrations that show how survey data benefits Big Data evaluation Covers methods and applications used in combining Big Data with survey statistics Examines regulations as well as ethical and privacy issues Big Data Meets Survey Science: A Collection of Innovative Methods is an excellent book for both the survey and social science communities as they learn to capitalize on this new revolution. It will also appeal to the broader data and computer science communities looking for new areas of application for emerging methods and data sources.