Software has often been left in the margins of accounts of digital cultures and network societies. Although software is everywhere, it is hard to say what it actually is.

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.

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.

As individuals increasingly seek ways of accessing, understanding and sharing data about their own bodies, this book offers a critique of the popular claim that 'more information' equates to 'better health'. In a study that redefines the public, academic and policy related debates around health, bodies, information and data, the authors consider the ways in which the phenomenon of self-diagnosis has created alternative worlds of knowledge and practises which are often at odds with professional medical advice. With a focus on data that concerns significant life changes, this book explores the potential challenges related to people's changing relationships with traditional health systems as access to, and control over, data shifts.

What is at stake socially, culturally, politically, and economically when we routinely use technology to gather information about our bodies and environments? Today anyone can purchase technology that can track, quantify, and measure the body and its environment. Wearable or portable sensors detect heart rates, glucose levels, steps taken, water quality, genomes, and microbiomes, and turn them into electronic data. Is this phenomenon empowering, or a new form of social control? Who volunteers to enumerate bodily experiences, and who is forced to do so? Who interprets the resulting data? How does all this affect the relationship between medical practice and self care, between scientific and lay knowledge? Quantified examines these and other issues that arise when biosensing technologies become part of everyday life. The book offers a range of perspectives, with views from the social sciences, cultural studies, journalism, industry, and the nonprofit world. The contributors consider data, personhood, and the urge to self-quantify; legal, commercial, and medical issues, including privacy, the outsourcing of medical advice, and self-tracking as a "paraclinical" practice; and technical concerns, including interoperability, sociotechnical calibration, alternative views of data, and new space for design. Contributors Marc Boehlen, Geoffrey C. Bowker, Sophie Day, Anna de Paula Hanika, Deborah Estrin, Brittany Fiore-Gartland, Dana Greenfield, Judith Gregory, Mette Kragh-Furbo, Celia Lury, Adrian Mackenzie, Rajiv Mehta, Maggie Mort, Dawn Nafus, Gina Neff, Helen Nissenbaum, Heather Patterson, Celia Roberts, Jamie Sherman, Alex Taylor, Gary Wolf

What do the patented data structures embedded deep in the code of an online computer game or the massively complicated architecture of the latest supercomputer used to simulate nuclear explosions have to do with culture, life or meaning? Why does technology attract such wildly differing responses - from fervour to boredom to distrust? Transductions explores these questions by drawing on science and technology studies, contemporary critical theory and corporeal theory. An exploration of complex technologies such as online computer games, genomic databases and the global positioning system reveals how the borders between bodies and machines, between what counts as social and what counts as technical, are no less diverse and complicated than culture itself. Indeed, they constitute a crucial dimension of contemporary culture. Through a critical analysis of the widely accepted notion that technology speeds everything up, Transductions argues that there are only ever differences in speed. The question for us now is how can such differences be represented? Transductions was originally part of the Technologies: Studies in Culture and Theory series.