Machine Learners: Archaeology of a Data Practice

by Adrian Mackenzie

Overview

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.

Product Details

ISBN-13:	9780262537865
Publisher:	MIT Press
Publication date:	12/08/2017
Series:	The MIT Press
Edition description:	Reprint
Pages:	272
Sales rank:	805,788
Product dimensions:	7.06(w) x 9.06(h) x 0.54(d)
Age Range:	18 Years

About the Author

Adrian Mackenzie is Professor of Technological Cultures in the Department of Sociology at Lancaster University and the author of Wirelessness: Radical Empiricism in Network Cultures (MIT Press).

Table of Contents

List of Figures vii

List of Tables ix

Preface xi

Acknowledgments xv

1 Introduction: Into the Data 1

2 Diagramming Machines 21

3 Vectorization and Its Consequences 51

4 Machines Finding Functions 75

5 N = ∀X: Probabilization and the Taming of Machines 103

6 Patterns and Differences 125

7 Regularizing and Materializing Objects 151

8 Propagating Subject Positions 179

9 Conclusion: Out of the Data 209

Glossary 219

Bibliography 223

Index 243

What People are Saying About This

Jussi Parikka

Adrian Mackenzie's insightful book details the many technical aspects of machine learning while also bringing it into conversation with cultural theory and science and technology studies.

Endorsement

This book breaks remarkable ground in offering a situated and deeply empirical account of contemporary analytic practices using big data. Mackenzie produces a novel and nuanced analysis of how population, knowledge, and power are being transformed through statistical modes of machine learning.Heavily researched, compelling in its arguments, and unique for interrogating the power relations inherent within machine learning, Mackenzie provides not only a path to understanding the new relationships between big data and machine learning that are transforming our contemporary world, but also a guidebook to tactics, methods, and practices that might allow concerned practitioners in many fields from the humanities to the computational sciences to rethink naturalized practices and to reimagine what both learning and data might become.

—Orit Halpern, Associate Professor, Department of Sociology and Anthropology, Concordia University

From the Publisher

Adrian Mackenzie's insightful book details the many technical aspects of machine learning while also bringing it into conversation with cultural theory and science and technology studies.

—Jussi Parikka, Professor, Technological Culture & Aesthetics, and Director, Archaeologies of Media and Technology, Winchester School of Art, University of Southampton

This book breaks remarkable ground in offering a situated and deeply empirical account of contemporary analytic practices using big data. Mackenzie produces a novel and nuanced analysis of how population, knowledge, and power are being transformed through statistical modes of machine learning. Heavily researched, compelling in its arguments, and unique for interrogating the power relations inherent within machine learning, Mackenzie provides not only a path to understanding the new relationships between big data and machine learning that are transforming our contemporary world, but also a guidebook to tactics, methods, and practices that might allow concerned practitioners in many fields from the humanities to the computational sciences to rethink naturalized practices and to reimagine what both learning and data might become.

—Orit Halpern, Associate Professor, Department of Sociology and Anthropology, Concordia University

Orit Halpern

This book breaks remarkable ground in offering a situated and deeply empirical account of contemporary analytic practices using big data. Mackenzie produces a novel and nuanced analysis of how population, knowledge, and power are being transformed through statistical modes of machine learning.Heavily researched, compelling in its arguments, and unique for interrogating the power relations inherent within machine learning, Mackenzie provides not only a path to understanding the new relationships between big data and machine learning that are transforming our contemporary world, but also a guidebook to tactics, methods, and practices that might allow concerned practitioners in many fields from the humanities to the computational sciences to rethink naturalized practices and to reimagine what both learning and data might become.