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This book brings together the work of historians and sociologists with perspectives from media studies, communication studies, cultural studies, and information studies to address the origins, practices, and possible futures of contemporary machine learning. From its foundations in 1950s and 1960s pattern recognition and neural network research to the modern-day social and technological dramas of DeepMind’s AlphaGo, predictive political forecasting, and the governmentality of extractive logistics, machine learning has become controversial precisely because of its increased embeddedness and agency in our everyday lives. How can we disentangle the history of machine learning from conventional histories of artificial intelligence? How can machinic agents’ capacity for novelty be theorized? Can reform initiatives for fairness and equity in AI and machine learning be realized, or are they doomed to cooptation and failure? And just what kind of “learning” does machine learning truly represent? We empirically address these questions and more to provide a baseline for future research. Chapter 2 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
In this ground-breaking book, Cambridge-trained sociologist Anthony Elliott argues that much of what passes for conventional wisdom about artificial intelligence is either ill-considered or plain wrong. The reason? The AI revolution is not so much about cyborgs and super-robots in the future, but rather massive changes in the here-and-now of everyday life. In The Culture of AI, Elliott explores how intelligent machines, advanced robotics, accelerating automation, big data and the Internet of Everything impact upon day-to-day life and contemporary societies. With remarkable clarity and insight, Elliott’s examination of the reordering of everyday life highlights the centrality of AI to everything we do – from receiving Amazon recommendations to requesting Uber, and from getting information from virtual personal assistants to talking with chatbots. The rise of intelligent machines transforms the global economy and threatens jobs, but equally there are other major challenges to contemporary societies – although these challenges are unfolding in complex and uneven ways across the globe. The Culture of AI explores technological innovations from industrial robots to softbots, and from self-driving cars to military drones – and along the way provides detailed treatments of: The history of AI and the advent of the digital universe; automated technology, jobs and employment; the self and private life in times of accelerating machine intelligence; AI and new forms of social interaction; automated vehicles and new warfare; and, the future of AI. Written by one of the world’s foremost social theorists, The Culture of AI is a major contribution to the field and a provocative reflection on one of the most urgent issues of our time. It will be essential reading to those working in a wide variety of disciplines including sociology, science and technology studies, politics, and cultural studies.
An examination of machine learning art and its practice in new media art and music. Over the past decade, an artistic movement has emerged that draws on machine learning as both inspiration and medium. In this book, transdisciplinary artist-researcher Sofian Audry examines artistic practices at the intersection of machine learning and new media art, providing conceptual tools and historical perspectives for new media artists, musicians, composers, writers, curators, and theorists. Audry looks at works from a broad range of practices, including new media installation, robotic art, visual art, electronic music and sound, and electronic literature, connecting machine learning art to such earlier artistic practices as cybernetics art, artificial life art, and evolutionary art. Machine learning underlies computational systems that are biologically inspired, statistically driven, agent-based networked entities that program themselves. Audry explains the fundamental design of machine learning algorithmic structures in terms accessible to the nonspecialist while framing these technologies within larger historical and conceptual spaces. Audry debunks myths about machine learning art, including the ideas that machine learning can create art without artists and that machine learning will soon bring about superhuman intelligence and creativity. Audry considers learning procedures, describing how artists hijack the training process by playing with evaluative functions; discusses trainable machines and models, explaining how different types of machine learning systems enable different kinds of artistic practices; and reviews the role of data in machine learning art, showing how artists use data as a raw material to steer learning systems and arguing that machine learning allows for novel forms of algorithmic remixes.
Artificial Intelligence (AI) serves as a catalyst for transformation in the field of digital teaching and learning by introducing novel solutions to revolutionize all dimensions of the educational process, leading to individualized learning experiences, teachers playing a greater role as mentors, and the automation of all administrative processes linked to education. AI and machine learning are already contributing to and are expected to improve the quality of the educational process by providing advantages such as personalized and interactive tutoring with the ability to adjust the content and the learning pace of each individual student while assessing their performance and providing feedback. These shifts in the educational paradigm have a profound impact on the quality and the way we live, interact with each other, and define our values. Thus, there is a need for an earnest inquiry into the cultural repercussions of this phenomenon that extends beyond superficial analyses of AI-based applications in education. Revolutionizing Education in the Age of AI and Machine Learning addresses the need for a scholarly exploration of the cultural and social impacts of the rapid expansion of artificial intelligence in the field of education including potential consequences these impacts could have on culture, social relations, and values. The content within this publication covers such topics as AI and tutoring, role of teachers, physical education and sports, interactive E-learning and virtual laboratories, adaptive curricula development, support critical thinking, and augmented intelligence and it is designed for educators, curriculum developers, instructional designers, educational software developers, education consultants, academicians, administrators, researchers, and professionals.
The never-more-necessary return of one of our most vital and eloquent voices on technology and culture, the author of the seminal Close to the Machine The last twenty years have brought us the rise of the internet, the development of artificial intelligence, the ubiquity of once unimaginably powerful computers, and the thorough transformation of our economy and society. Through it all, Ellen Ullman lived and worked inside that rising culture of technology, and in Life in Code she tells the continuing story of the changes it wrought with a unique, expert perspective. When Ellen Ullman moved to San Francisco in the early 1970s and went on to become a computer programmer, she was joining a small, idealistic, and almost exclusively male cadre that aspired to genuinely change the world. In 1997 Ullman wrote Close to the Machine, the now classic and still definitive account of life as a coder at the birth of what would be a sweeping technological, cultural, and financial revolution. Twenty years later, the story Ullman recounts is neither one of unbridled triumph nor a nostalgic denial of progress. It is necessarily the story of digital technology’s loss of innocence as it entered the cultural mainstream, and it is a personal reckoning with all that has changed, and so much that hasn’t. Life in Code is an essential text toward our understanding of the last twenty years—and the next twenty.
We commonly think of society as made of and by humans, but with the proliferation of machine learning and AI technologies, this is clearly no longer the case. Billions of automated systems tacitly contribute to the social construction of reality by drawing algorithmic distinctions between the visible and the invisible, the relevant and the irrelevant, the likely and the unlikely – on and beyond platforms. Drawing on the work of Pierre Bourdieu, this book develops an original sociology of algorithms as social agents, actively participating in social life. Through a wide range of examples, Massimo Airoldi shows how society shapes algorithmic code, and how this culture in the code guides the practical behaviour of the code in the culture, shaping society in turn. The ‘machine habitus’ is the generative mechanism at work throughout myriads of feedback loops linking humans with artificial social agents, in the context of digital infrastructures and pre-digital social structures. Machine Habitus will be of great interest to students and scholars in sociology, media and cultural studies, science and technology studies and information technology, and to anyone interested in the growing role of algorithms and AI in our social and cultural life.
A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole—and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.
In this pioneering book, David Beer redefines emergent algorithmic technologies as the new systems of knowing. He examines the acute tensions they create and how they are changing what is known and what is knowable.
The De Gruyter Handbook of Artificial Intelligence, Identity and Technology Studies examines the relationship of the social sciences to artificial intelligence, surveying the various convergences and divergences between science and technology studies on the one hand and identity transformations on the other. It provides representative coverage of all aspects of the AI revolution, from employment to education to military warfare, impacts on public policy and governance and the future of ethics. How is AI currently transforming social, economic, cultural and psychological processes? This handbook answers these questions by looking at recent developments in supercomputing, deep learning and neural networks, including such topics as AI mobile technology, social robotics, big data and digital research. It focuses especially on mechanisms of identity by defining AI as a new context for self-exploration and social relations and analyzing phenomena such as race, ethnicity and gender politics in human-machine interfaces.
This book offers a comprehensive approach to the question of how artificial intelligence (AI) impacts politics, economy, and the society today. In this view, it is quintessential for understanding the complex nature of AI and its role in today’s world. The book has been divided into three parts. Part one is devoted to the question of how AI will be used for security and defense purposes, including combat in war zones. Part two looks at the value added of AI and machine learning for decision-making in the fields of politics and business. Part three consists of case studies—covering the EU, the USA, Saudi Arabia, Portugal, and Poland—that discuss how AI is being used in the realms of politics, security and defense. The discussion in the book opens with the question of the nature of AI, as well as of ethics and the use of AI in combat. Subsequently, the argument covers issues as diverse as the militarization of AI, the use of AI in strategic studies and military strategy design. These topics are followed by an insight into AI and strategic communication (StratCom), including disinformation, as well as into AI and finance. The case-studies included in part 3 of the book offer a captivating overview of how AI is being employed to stimulate growth and development, to promote data- and evidence-driven policy-making, to enable efficient and inclusive digital transformation and other related issues. Written by academics and practitioners in an academically sound, yet approachable manner, this volume queries issues and topics that form the thrust of processes that transform world politics, economics and society. As such, this volume will serve as the primer for students, researchers, lectures and other professionals who seek to understand and engage with the variety of issues AI implicates.