UNIVERSITY OF CAMBRIDGE Inventing Intelligence: On the History of Complex Information Processing and Artificial Intelligence in the United States in the Mid-Twentieth Century This thesis is submitted for the degree of Doctor of Philosophy In History and Philosophy of Science By Jonathan Nigel Ross Penn (‘Jonnie Penn’) Pembroke College, Cambridge Examiners: Professor Simon Schaffer, University of Cambridge Professor Jon Agar, University College London Supervisor: Dr. Richard Staley, University of Cambridge Advisor: Dr. Helen Anne Curry, University of Cambridge Word Count: 78,033 Date: 14 December 2020 This thesis is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. It is not substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. I further state that no substantial part of my thesis has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. It does not exceed the prescribed word limit for the Degree Committee of the Department of History and Philosophy of Science at the University of Cambridge. Copyright Jonnie Penn, 2020. All rights reserved. 2 Abstract Inventing Intelligence: On the History of Complex Information Processing and Artificial Intelligence in the United States in the Mid-Twentieth Century In the mid-1950s, researchers in the United States melded formal theories of problem solving and intelligence with another powerful new tool for control: the electronic digital computer. Several branches of western mathematical science emerged from this nexus, including computer science (1960s–), data science (1990s–) and artificial intelligence (AI). This thesis offers an account of the origins and politics of AI in the mid-twentieth century United States, which focuses on its imbrications in systems of societal control. In an effort to denaturalize the power relations upon which the field came into being, I situate AI’s canonical origin story in relation to the structural and intellectual priorities of the U.S. military and American industry during the Cold War, circa 1952 to 1961. This thesis offers a detailed and comparative account of the early careers, research interests, and key outputs of four researchers often credited with laying the foundations for AI and machine learning—Herbert A. Simon, Frank Rosenblatt, John McCarthy and Marvin Minsky. It chronicles the distinct ways in which each sought to formalise and simulate human mental behaviour using digital electronic computers. Rather than assess their contributions as discontinuous with what came before, as in mythologies of AI's genesis, I establish continuities with, and borrowings from, management science and operations research (Simon), Hayekian economics and instrumentalist statistics (Rosenblatt), automatic coding techniques and pedagogy (McCarthy), and cybernetics (Minsky), along with the broadscale mobilization of Cold War-era civilian-led military science generally. I assess how Minsky’s 1961 paper 'Steps Toward Artificial Intelligence' simultaneously consolidated and obscured these entanglements as it set in motion an initial research agenda for AI in the following two decades. I argue that mind-computer metaphors, and research in complex information processing generally, played an important role in normalizing the small- and large-scale structuring of social behaviour using mathematics in the United States from the second half of the twentieth century onward. By: Jonathan Penn 3 Table of Contents Title 1 Abstract 3 Table of Contents 4 Acknowledgements 6 Archive Collections 9 List of Figures 10 Chapter One – Introduction 11 Prehistories: Psychology, Cybernetics and Computing 18 Cybernetics: The Field that Was Not a Field 22 Computer Programming: Materiality, Mind and Machine 26 Historiography from Within: Popular Accounts of AI History by Practitioners 30 and their Kin Scholarly Histories: Classical and Alternative Histories of Computing 36 Summary 42 Chapter Two – On the Logic Behind the Logic Theory Machine 44 1930-40s: Simon’s Scientific Account of Administration 46 1952-55: Simon’s Account of Administration is Translated into Code at RAND 51 1955-56: Building the Logic Theory Machine 61 1956-58: Framing the Logic Theory Machine 69 Conclusion 80 Chapter Three – On Frank Rosenblatt’s Perceptron Theory, 1945-61 82 1945-56: Rosenblatt and Civilian-Led Military Science in Post-War America 83 1955-58: From the Statistics of Personality Research to the Statistics of 94 Brain Modelling 1958: Rosenblatt’s Statistical Approach to Brain Modelling 101 1958-71: On the ‘Second Direction’ of Brain Modelling and its Military Suitors 108 Conclusion 114 Chapter Four – Applied Epistemology: On John McCarthy’s Account of 116 Programmed Language as Intelligence, 1952-59 1944-55: The Road to Automata 118 1955-56: From Automata to Automatic Programming and AI 125 1956: The Dartmouth Summer Research Project for McCarthy 135 4 1956-59: Programming as Applied Epistemology 146 Conclusion 157 Chapter Five – Contextualising Marvin Minsky’s Agenda for 159 Artificial Intelligence, 1950-61 1944-54: Neural Networks as a ‘Plausible Analogy’ for Mental Behaviour 161 1954-56: A Change of Mind – Minsky’s Turn to Heuristic Programming 169 1956-61: Minsky Outlines a Research Agenda for AI 180 1961-1970s: AI and Military Infrastructure 191 Conclusion 197 Chapter Six – Conclusion 197 An Emerging Discipline 200 Fitting AI into Histories of American Social Science Research in the 213 Mid-Twentieth Century Bibliography 221 5 Acknowledgements ‘There is no power out of the church that could sustain slavery an hour, if it were not sustained in it.’ - Albert Barnes1 I have reflected at various stages of this project on why it is not customary in the tradition of the history and philosophy of science that I have been trained in to explore, in a formal capacity as in an Acknowledgements section, the distinct context and positionality that informs one’s historical research. While these factors are often (but not always) considered in the body of a work, or pointed to in footnotes, at conferences, in presentations or over drinks, there is no conventional expectation to include, say, a Reflexivity Statement to consider one’s own contextual orientation upfront. Learning how to relate to the cultural, political and social forces that shape both me and my research—as well as the priorities of Western academic research generally—has been an important and ongoing part of this professionalizing experience so I will include a view brief remarks here alongside a set of well- deserved Acknowledgements. One immediate consideration to name is that the University of Cambridge is currently closed due to the Covid-19 pandemic. To my knowledge, the last closure of this type was due to the Great Plague in the mid seventeenth century. The full impact of the virus remains to be felt. That two and a half billion people worldwide are currently in lockdown begs reflection on the extent to which academia challenges or is complicit in government systems that consistently fail to protect—and often to recognise—the vulnerable, even as they introduce such widescale compensatory measures. I deliver this PhD from a government-imposed quarantine in Western Canada, where I am with family to provide caring duties. For posterity, I will note that I am a dual Canadian-British, Caucasian, able-bodied, cis-male born as a settler in a settler colonial context. This positionality informs both the strengths and weaknesses of my current research project in ways that I continue to unpack. During the course of this dissertation, from 2016-20, I have called a number of locations home. The first and most important is the Department of the History and Philosophy of Science at the University of Cambridge. I would like to thank the Rausing, Williamson and Lipton trust for their financial support, without which these words would not have been written. A sincere thank you also goes to Richard Staley, my supervisor. Richard is a dedicated scholar and generous friend. I will cherish our long, rolling conversations together in his office on Free School Lane—where a hand drawn Einstein portrait hangs on the wall. I hope that I can live up to the quality of excellence he has helped me to appreciate and to enjoy pursuing. I am equally indebted to Helen Anne Curry, my advisor. Helen’s skilful critiques have renewed my confidence in the authority of the written word. I feel I must learn them all again now. Her sharp eye and patient ear have enriched this project considerably. Any remaining faults in what follows are irredeemably my own. Additional thanks are due to friends and colleagues at the University of Cambridge. I am grateful to Simon Schaffer, Anna Alexandrova, Joe Martin, Marina Velickovic, David Runciman, Andrew Buskell and Josh Nall for thoughtful guidance on various topics. Tamara Hug and her talented team made my archival work possible; I am grateful for these relationships too. A special thank you to Stephen Cave, Sarah Dillon, Susan K. Gowans and the 1 As cited in: Douglass, ‘The Meaning of July Fourth for the Negro’. 6 Leverhulme Centre for the Future of Intelligence, where I have had the opportunity to foster an international community of scholars oriented toward historical understandings of AI. I also want to acknowledge the University’s undergraduate community for insisting—often via civil disruption—that academic and government leaders take action to address mounting democratic and environmental crises. The University disentangles its debts to slavery and colonization at a snail’s pace. Unsustainable investments remain intact.