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Author: Jonathan Roberge
Publisher: Springer Nature
ISBN: 3030562867
Category : Social Science
Languages : en
Pages : 298
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Book Description
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.
Author: Jonathan Roberge
Publisher: Springer Nature
ISBN: 3030562867
Category : Social Science
Languages : en
Pages : 298
Get Book Here
Book Description
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.
Author: Stefka Hristova
Publisher: Rowman & Littlefield
ISBN: 1793635749
Category : Social Science
Languages : en
Pages : 219
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Book Description
Algorithmic Culture: How Big Data and Artificial Intelligence are Transforming Everyday Life explores the complex ways in which algorithms and big data, or algorithmic culture, are simultaneously reshaping everyday culture while perpetuating inequality and intersectional discrimination. Contributors situate issues of humanity, identity, and culture in relation to free will, surveillance, capitalism, neoliberalism, consumerism, solipsism, and creativity, offering a critique of the myriad constraints enacted by algorithms. This book argues that consumers are undergoing an ontological overhaul due to the enhanced manipulability and increasingly mandatory nature of algorithms in the market, while also positing that algorithms may help navigate through chaos that is intrinsically present in the market democracy. Ultimately, Algorithmic Culture calls attention to the present-day cultural landscape as a whole as it has been reconfigured and re-presented by algorithms.
Author: Anthony Elliott
Publisher: Routledge
ISBN: 1315387166
Category : Social Science
Languages : en
Pages : 256
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Book Description
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.
Author: Sofian Audry
Publisher: MIT Press
ISBN: 0262367106
Category : Art
Languages : en
Pages : 215
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Book Description
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.
Author: Habib, Maki K.
Publisher: IGI Global
ISBN: 1522577947
Category : Education
Languages : en
Pages : 278
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Book Description
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.
Author: Massimo Airoldi
Publisher: John Wiley & Sons
ISBN: 1509543295
Category : Social Science
Languages : en
Pages : 200
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Book Description
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.
Author: Joaquin Quinonero-Candela
Publisher: MIT Press
ISBN: 0262170051
Category : Computers
Languages : en
Pages : 246
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Book Description
An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions. Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Brückner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert Müller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schölkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama
Author: David Beer
Publisher: Policy Press
ISBN: 1529212901
Category : Computers
Languages : en
Pages : 152
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Book Description
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.
Author: Anthony Elliott
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110721759
Category : Social Science
Languages : en
Pages : 316
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Book Description
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.
Author: Ranjodh Singh Dhaliwal
Publisher: U of Minnesota Press
ISBN: 1452970491
Category : Social Science
Languages : en
Pages : 158
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Book Description
A critical examination of the figure of the neural network as it mediates neuroscientific and computational discourses and technical practices Neural Networks proposes to reconstruct situated practices, social histories, mediating techniques, and ontological assumptions that inform the computational project of the same name. If so-called machine learning comprises a statistical approach to pattern extraction, then neural networks can be defined as a biologically inspired model that relies on probabilistically weighted neuron-like units to identify such patterns. Far from signaling the ultimate convergence of human and machine intelligence, however, neural networks highlight the technologization of neurophysiology that characterizes virtually all strands of neuroscientific and AI research of the past century. Taking this traffic as its starting point, this volume explores how cognition came to be constructed as essentially computational in nature, to the point of underwriting a technologized view of human biology, psychology, and sociability, and how countermovements provide resources for thinking otherwise.
