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Sponsoring Committee: Professor Lisa Gitelman, Chairperson Professor Alexander Galloway Associate Professor Mara Mills Associate Professor Erica Robles-Anderson Sponsoring Committee: Professor Lisa Gitelman, Chairperson Professor Alexander Galloway Associate Professor Mara Mills Associate Professor Erica Robles-Anderson DIVINATION ENGINES: A MEDIA HISTORY OF TEXT PREDICTION Xiaochang Li Program in Media, Culture, and Communication Department of Media, Culture, and Communication Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Steinhardt School of Culture, Education, and Human Development New York University 2017 ProQuest Number:10623544 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. ProQuest 10623544 Published by ProQuest LLC ( 2017). Copyright of the Dissertation is held by the Author. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code Microform Edition © ProQuest LLC. ProQuest LLC. 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, MI 48106 - 1346 Copyright © 2017 Xiaochang Li ACKNOWLEDGEMENTS I owe an enormous debt of gratitude the former members of the IBM CSR group who took time out of their busy schedules to speak to me about their work and experiences, including Lalit Bahl, Peter Brown, Stephen and Vincent Della Pietra, and Robert Mercer. I am additionally indebted to Janet Baker, who generously shared recorded interviews and other materials from her own collection. Immense thanks is also owed to the archivists who aided my research, including Arvid Nelson at the Charles Babbage Institute, Dawn Stanford at the IBM Corporate Archives, George Kupczak at AT&T Archive in Warren, NJ, and the wonderful staff at the National Museum of American History Archive Center and the University of Washington Libraries Special Collections. I have had the immense fortune of working with a committee whose intellectual generosity and steadfast guidance have been nothing short of wondrous. I sincerely could not imagine a more brilliant and supportive committee. Lisa Gitelman’s feedback, unwavering support, and endless patience throughout this project provided a much-needed anchor. As a mentor, her expansive insights allowed me think more boldly, while her pragmatic counsel ensured that I could hold the course. As an advisor, her responsiveness and calm !iii demeanor have been a constant source of reassurance in all the stress and chaos. Alex Galloway pushed me to be more confident and meticulous as a thinker and a writer, and his feedback, particularly in the early stages, was pivotal in shaping the prominent themes of this research. Mara Mills has been an unending source of what I can only describe as rigorous encouragement, a particular combination of cheerful enthusiasm and incisive critique that always energized me to dive back into the work. She has also been incredibly generous in sharing resources and opportunities, which has been invaluable throughout in shaping both my research and my scholarly progress. Erica Robles-Anderson has been guiding this project since it was just a vague notion my first year, urging me to trust my instincts and dig into the work. I simply cannot imagine where this project would be without her many contributions, especially her uncanny ability to transform my barely- coherent ramblings into thoughtful, provocative questions that never failed to unsettle my thinking in the most productive ways. Finally, I am enormously grateful to my outside readers, Kate Crawford and Lev Manovich, for the time and care they took in reviewing this work and providing so much detailed feedback and encouragement. I could not have done this without my amazing cohort (one might even call them The AwesomestCohort™)—Shane Brennan, Wendy Chen, Jess Feldman, Liz Koslov, and Tim Wood. Particular thanks is owed to Liz, who kept me company while I wrote, and Tim, who kept me company while I whined about !iv writing. I cannot express how fortunate I feel to have been a part of the extraordinary community of PhD students at MCC, with whom I have enjoyed so many inspiring conversations and collaborations. Particular thanks goes to Carlin Wing, Matthew Hockenberry, Kouross Esmaeli, Tamara Kneese, Jacob Gaboury, Kari Hensley, Seda Gürses, and Solon Barocas, for sharing their brilliant insights and sage advice. Thanks also to the many incredible faculty at MCC and across NYU who generously took the time to read my work and discuss ideas, including Arjun Appadurai, Finn Brunton, Lily Chumley, Allen Feldman, Ben Kafka (who also let me have his table at McNally’s that one time), Randy Martin, Sue Murray, Helen Nissenbaum, Martin Scherzinger, Natasha Schüll, Nicole Starosielski, Marita Sturken, and Torsten Suel. I am also grateful to the many scholars outside NYU who have offered invaluable feedback, resources, and access, including Karin Bijsterveld, Carolyn Birdsall, danah boyd, Henry Jenkins, Mike Karlesky, Karen Levy, Christine Mitchell, Viktoria Tkaczyk, and William Uricchio. My research was additionally supported by the Phyllis and Gerald Leboff Dissertation Fellowship and the NYU Center for the Humanities, which provided not only financial support, but also a brilliant and engaging cohort of faculty and student fellows. I am immensely thankful to my wonderful friends and family for all the much-needed encouragement and reality checks these past few years. Particular gratitude is owed to Katie Phelan, for supplying homemade croissants across two !v continents and three separate degrees, and Shannon Starkey, for supplying many words of support and reassurance, despite such gestures being entirely contrary to his nature. Finally, this dissertation is dedicated to my parents, whose unimaginable work and sacrifice are responsible for everything I have accomplished and will ever accomplish. And to Trystan, whose ferocious talent and unflinching sense of purpose will always inspire, and forever be missed. !vi TABLE OF CONTENTS ACKNOWLEDGMENTS iii LIST OF FIGURES ix CHAPTER I. INTRODUCTION 1 Chapter Outline 16 II. THE ARTFUL DECEIT 20 Speech Recognition and the Human-Computer 28 Imagination The Astonishing Mechanism 35 Remaking Speech 43 From Signal to Symbol 56 Artificial Intelligence and Expert Systems 67 Airplanes Don’t Flap Their Wings 74 Noise in the Channel 85 III. THE IDEA OF DATA 91 A Technical Overview of ASR 97 The Statistical Nature of Speech 102 The Statistical Nature of Language 118 Hiding Knowledge, Maximizing Likelihood 126 No Data Like More Data 142 The Way of the Machine 150 continued !vii IV. THE DISCOVERY OF KNOWLEDGE 177 From Speech Recognition to Language Processing 183 The Crude Force of Computing 194 “Big-Data-Small-Program” 209 Data’s Rising Tide 222 V. CONCLUSION: THE BLACKEST BOXES 229 WORKS CITED 235 !viii LIST OF FIGURES 1 Three diagram drawings by Pierce 54 2 Photograph of the Automatic Digit Recognizer, or “Audrey” 58 3 The structure of the HWIM (“Hear What I Mean”) 71 4 Block diagrams comparing the standard view CSR with the “noisy 86 channel” model 5 Representation of the acoustic processor’s signal quantization 98 process 6 Illustration of the operation of the logograph with a detail 108 rendering of the print output below 7 Three images of depicting the design and output of John B. 109 Flowers’ Phonoscribe 8 Block schematic of Audrey 110 9 Photographs of simplified speech signal sample traces for Audrey. 110 10 Simplified representation of the quantization process using digit 7 112 detail from figure 9 11 Graph of formant frequency results 117 12 Block diagram of the automatic phoneme recognizer 122 13 Model of the New Raleigh Language. 132 14 Color quantization using K-means clustering 170 !ix CHAPTER I INTRODUCTION “There is nothing more natural, moreover, than the relation thus expressed between divination and the classification of things. Every divinatory rite, however simple it may be, rests on a pre-existing sympathy between certain beings, and on a traditionally admitted kinship between a certain sign and a certain future event . At the basis of a system of divination there is thus, at least implicitly, a system of classification.” Émile Durkheim and Marcel Mauss, Primitive Classification (1903, trans. 1967)1 “[P]rediction is fundamentally a type of information processing activity.” Nate Silver, The Signal and the Noise (2015)2 In November of 2016, during a press event held at Google’s London office, London Mayor Sadiq Khan introduced Google CEO Sundar Pichai with a small quip: “A friend, he began, had recently told him he reminded him of Google. ‘Why, because I know all the answers?’ the mayor asked. ‘No,’ the friend replied, ‘because you’re always trying to finish my sentences.’”3 The joke, 1 Emile Durkheim and Marcel Mauss, Primitive Classification, trans. Rodney Needham (Chicago: University Of Chicago Press, 1967), 46. 2 Nate Silver, The Signal and the Noise: Why So Many Predictions Fail, But Some Don’t (New York: Penguin, 2015), 266. 3 Gideon Lewis-Kraus, “The Great A.I. Awakening,” The New York Times, December 14, 2016, sec. Sunday Magazine Supplement, https://www.nytimes.com/2016/12/14/ magazine/the-great-ai-awakening.html. !1 recounted in The New York Times, plays upon a misrecognition between Google Search’s core operation, which retrieves and ranks web content in response to typed queries, and the search engine’s decidedly less consistent, if rather more conspicuous “Autocomplete” function, an interface design feature that aims to predict the text of search queries as they’re typed. The Mayor believes he’s proffering knowledge; his friend knows that he’s merely guessing at words. Though played for laughs, this ready confusion between predicting text and procuring information was reinforced through the feature design itself. In 2010, the same year text completion was officially rebranded as Autocomplete, Google rolled out Instant search, a new feature that generated search results as the user typed.
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