This book argues that Marxist theory is essential for understanding the contemporary industrialization of the form of artificial intelligence (AI) called machine learning. It includes a political economic history of AI, tracking how it went from a fringe research interest for a handful of scientists in the 1950s to a centerpiece of cybernetic capital fifty years later. It also includes a political economic study of the scale, scope and dynamics of the contemporary AI industry as well as a labour process analysis of commercial machine learning software production, based on interviews with workers and management in AI companies around the world, ranging from tiny startups to giant technology firms. On the basis of this study, Steinhoff develops a Marxist analysis to argue that the popular theory of immaterial labour, which holds that information technologies increase the autonomy of workers from capital, tending towards a post-capitalist economy, does not adequately describe the situation of high-tech digital labour today. In the AI industry, digital labour remains firmly under the control of capital. Steinhoff argues that theories discerning therein an emergent autonomy of labour are in fact witnessing labour's increasing automation.

James Steinhoff is a Postdoctoral Fellow at the University of Toronto, Canada

Autorentext

James Steinhoff is a Postdoctoral Fellow at the University of Toronto, Canada.

Inhalt

1. Introduction: Automation, Autonomy and Artificial Intelligence

Your Means of Production

Revolutions

AI in the Real World

Machinery and Marxists The Central Argument

Computing Machinery

Recursion What this Book is Not

Chapter Outline

2. Labour, Capital, Machine: Marxist Theory and Technology Introduction

Political Economy

Marx on Value and Labour Marx on Machines

The Fragment on Machines

Marxism(s) Soviet Marxism

Western Marxism

Labour Process Theory The New Reading of Marx

Cybernetic Capitalism

Conclusion

3. Post-Operaismo and the New Autonomy of Immaterial Labour

   Introduction

From Operaismo to Post-operaismo Post-operaismo

Immaterial Labour Theory

Human-Machine Hybridization Abstract Cooperation

New Autonomy from Capital

The Technological Argument for New Autonomy Conclusion

4. Industrializing Intelligence: A Political Economic History of the AI Industry Introduction The Historical Context

The Advent of AI Research

The AI Winter Expert Systems: The First Era of the AI Industry

Strategic Computing: AI and the State Part I

The Decline of Expert Systems The Rise of Machine Learning

Deep Learning: The Second Era of the AI Industry

Conclusion

5. Machine Learning and Fixed Capital: The Contemporary AI Industry

   Introduction

Charting the AI Industry AI Capital Composition

AI Tech Giants

AI Dinosaurs

AI Startups

AI Think Tanks

National AI Strategies: AI and the State Part II

AI Capital Concentration

Open Source AI, Clouds, AI Chips Labour in the AI Industry

Labour Composition: Race and Gender

AI Labour Organization

Conclusion

6. A Dark Art: The Machine Learning Labour Process

   Introduction

The Machine Learning Labour Process

Stage 1: Data Processing

Stage 2: Model Building

Stage 3: Deployment

The Commodity Form of AI

Empirical Control

AI as Automation

The Automation of AI Work

Automated Machine Learning Synthetic Automation

Other Forms of Automation in Machine Learning

Conclusion

7.New Autonomy and Work in the AI Industry

Introduction

AI Work and Human-Machine Hybridization AI Work and Abstract Cooperation

AI Work and New Autonomy

Autonomy for What? Conclusion

8. Conclusion: Harry Braverman Overdrive Introduction Theoretical Synthesis

Automation on Steroids

Optimism and Agency Conclusion