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1
Turner, From Counterculture to Cyberculture.
2
Brand, “We Owe It All to the Hippies.”
3
Science Technology Engineering And Math (STEM) – àááðåâèàòóðà, îáîçíà÷àþùàÿ òåõíè÷åñêèå ñïåöèàëüíîñòè, â êîòîðûõ òðàäèöèîííî íàáëþäàëñÿ ãåíäåðíûé ïåðåâåñ. – Ïðèì. ïåð.
4
 Ðîññèè âûõîäèò íà êàíàëå ÍÒ ïîä íàçâàíèåì «Ñâîÿ èãðà». – Ïðèì. ðåä.
5
Dreyfus, What Computers Still Can’t Do.
6
Êåðíèãàí Á. Ðèò÷è Ä. ßçûê ïðîãðàììèðîâàíèÿ Ñ. – Ì.: Âèëüÿìñ, 2009.
7
Weizenbaum, “Eliza.”
8
Cerulo, Never Saw It Coming.
9
Miner et al., “Smartphone-Based Conversational Agents and Responses to Questions about Mental Health, Interpersonal Violence, and Physical Health.”
10
Bonnington, “Tacocopter.”
11
Silver et al., “Mastering the Game of Go with Deep Neural Networks and Tree Search,” 484.
12
Turing, “Computing Machinery and Intelligence.”
13
Searle, “Artificial Intelligence and the Chinese Room.”
14
Cox, Bloch, and Carter, “All of Inflation’s Little Parts.”
15
Hart, Robbins, and Teegardin, “How the Doctors & Sex Abuse Project Came About.”
16
Kestin and Maines, “Cops Hitting the Brakes – New Data Show Excessive Speeding Dropped 84 % since Investigation.”
17
Kunerth, “Any Way You Look at It, Florida Is the State of Weird.”
18
Pierson et al., “A Large-Scale Analysis of Racial Disparities in Police Stops across the United States.”
19
Angwin et al., “Machine Bias.”
20
Meyer, Precision Journalism, 14.
21
Lewis, “Journalism in an Era of Big Data”; Diakopoulos, “Accountability in Algorithmic Decision Making”; Houston, Computer-Assisted Reporting; Houston and Investigative Reporters and Editors, Inc., The Investigative Reporter’s Handbook.
22
Holovaty, “A Fundamental Way Newspaper Sites Need to Change.”
23
Waite, “Announcing Politifact.”
24
Holovaty, “In Memory of Chicagocrime.org.”
25
Daniel and Flew, “The Guardian Reportage of the UK MP Expenses Scandal”; Flew et al., “The Promise of Computational Journalism.”
26
Valentino-DeVries, Singer-Vine, and Soltani, “Websites Vary Prices, Deals Based on Users’ Information.”
27
Diakopoulos, “Algorithmic Accountability.”
28
Anderson, “Towards a Sociology of Computational and Algorithmic Journalism”; Schudson, “Four Approaches to the Sociology of News.”
29
Usher, Interactive Journalism.
30
Royal, “The Journalist as Programmer.”
31
Hamilton, Democracy’s Detectives.
32
Arthur, “Analysing Data Is the Future for Journalists, Says Tim Berners-Lee.”
33
Silver, The Signal and the Noise.
34
Ìèíèñòð îáðàçîâàíèÿ ÑØÀ
35
Pennsylvania System of School Assessment (PSSA). – Ïðèì. ïåð.
36
Lane, “What the AP U. S. History Fight in Colorado Is Really About.”
37
Broussard, “Why E-books Are Banned in My Digital Journalism Class”; Wästlund et al., “Effects of VDT and Paper Presentation on Consumption and Production of Information”; Noyes and Garland, “VDT versus Paper-Based Text”; Morineau et al., “The Emergence of the Contextual Role of the E-book in Cognitive Processes through an Ecological and Functional Analysis”; Noyes and Garland, “Computervs. Paper-Based Tasks”; Keim, “Why the Smart Reading Device of the Future May Be… Paper.”
38
Ames, “Translating Magic.”
39
Kraemer, Dedrick, and Sharma, “One Laptop per Child”; Purington, “One Laptop per Child.”
40
Broussard, “Why Poor Schools Can’t Win at Standardized Testing.”
41
School District of Philadelphia, “Budget Adoption Fiscal Year 2016–2017.”
42
Christian and Cabell, Initial Investigation into the Psychoacoustic Properties of Small Unmanned Aerial System Noise.
43
Martinez, “‘Drone Slayer’ Claims Victory in Court.”
44
Vincent, “Twitter Taught Microsoft’s AI Chatbot to Be a Racist Asshole in Less than a Day.”
45
Plautz, “Hitchhiking Robot Decapitated in Philadelphia.”
46
Unless otherwise indicated, quotes from Minsky in this section are taken from Minsky, “Web of Stories Interview.”
47
Brand, The Media Lab; Levy, Hackers.
48
Dormehl, “Why John Sculley Doesn’t Wear an Apple Watch (and Regrets Booting Steve Jobs).”
49
Lewis, “Rise of the Fembots”; LaFrance, “Why Do So Many Digital Assistants Have Feminine Names?”
50
Àâòîð ññûëàåòñÿ íà «îáúåäèíèòåëåé», ñîöèàëüíûõ àêòîðîâ, óïîìÿíóòûõ Ìàëêîëüìîì Ãëàäóýëëîì â êíèãå «Ïåðåëîìíûé ìîìåíò». – Ïðèì. ïåð.
51
Long Now Foundation – äîëãîñðî÷íàÿ êóëüòóðíàÿ èíèöèàòèâà, ïðîòèâîïîñòàâëÿþùàÿ ñåáÿ «áûñòðîé è äåøåâîé» êóëüòóðå è ñòîÿùàÿ íà èäåÿõ ðàçâèòèÿ äîëãîñðî÷íîé îòâåòñòâåííîñòè. – Ïðèì. ïåð.
52
Hillis, “Radioactive Skeleton in Marvin Minsky’s Closet.”
53
Alba, “Chicago Uber Driver Charged with Sexual Abuse of Passenger”; Fowler, “Reflecting on One Very, Very Strange Year at Uber”; Isaac, “How Uber Deceives the Authorities Worldwide.”
54
Copeland, “Summing Up Alan Turing.”
55
“The Leibniz Step Reckoner and Curta Calculators – CHM Revolution.”
56
Kroeger, The Suffragents; Shetterly, Hidden Figures; Grier, When Computers Were Human.
57
Wolfram, “Farewell, Marvin Minsky (1927–2016).”
58
Alcor Life Extension Foundation, “Official Alcor Statement Concerning Marvin Minsky.”
59
Brand, “We Are As Gods.”
60
Turner, From Counterculture to Cyberculture.
61
Brand, “We Are As Gods.”
62
Hafner, The Well.
63
Îò íàçâàíèÿ ñàáðåääèòà r/TheRedPill, ïîñâÿùåííîãî «ïðàâàì ìóæ÷èí». – Ïðèì. íàó÷. ðåä.
64
Borsook, Cyberselfish, 15.
65
Barlow, “A Declaration of the Independence of Cyberspace.”
66
Thiel, “The Education of a Libertarian.”
67
Taplin, Move Fast and Break Things.
68
Slovic, The Perception of Risk; Slovic and Slovic, Numbers and Nerves; Kahan et al., “Culture and Identity-Protective Cognition.”
69
Leslie et al., “Expectations of Brilliance Underlie Gender Distributions across Academic Disciplines,” 262.
70
Bench et al., “Gender Gaps in Overestimation of Math Performance,” 158. Also Ñì. Feltman, “Men (on the Internet) Don’t Believe Sexism Is a Problem in Science, Even When They Ñì. Evidence”; Williams, “The 5 Biases Pushing Women Out of STEM”; Turban, Freeman, and Waber, “A Study Used Sensors to Show That Men and Women Are Treated Differently at Work”; Moss-Racusin, Molenda, and Cramer, “Can Evidence Impact Attitudes?”; Cohoon, Wu, and Chao, “Sexism: Toxic to Women’s Persistence in CSE Doctoral Programs.”
71
Natanson, “A Sort of Everyday Struggle.”
72
Ñì. https://xkcd.com/1425, and note that the hidden text on the online version of the comic refers to a famous anecdote about Marvin Minsky.
73
Solon, “Roomba Creator Responds to Reports of ‘Poopocalypse.’”
74
È òî è äðóãîå íà àíãëèéñêîì èìåíóåòñÿ cells. – Ïðèì. ðåä.
75
Busch, “A Dozen Ways to Get Lost in Translation”; van Dalen, “The Algorithms behind the Headlines”; ACM Computing Curricula Task Force, Computer Science Curricula 2013.
76
IEEE Spectrum, “Tech Luminaries Address Singularity.”
77
Gomes, “Facebook AI Director Yann LeCun on His Quest to Unleash Deep Learning and Make Machines Smarter.”
78
Ïðèìåð àêòóàëåí äëÿ ÑØÀ. – Ïðèì. ðåä.
79
“machine, n.”
80
Butterfield and Ngondi, A Dictionary of Computer Science.
81
Pedregosa et al., “Scikit-Learn: Machine Learning in Python.”
82
Mitchell, “The Discipline of Machine Learning.”
83
Neville-Neil, “The Chess Player Who Couldn’t Pass the Salt.”
84
Russell and Norvig, Artificial Intelligence.
85
Î’Íèë Ê. Óáèéñòâåííûå áîëüøèå äàííûå. – Ì.: ÀÑÒ, 2018.
86
O’Neil, Weapons of Math Destruction.
87
Grazian, Mix It Up.
88
Blow, Fire Shut Up in My Bones.
89
Tversky and Kahneman, “Availability.” Ñì. also Kahneman, Thinking, Fast and Slow; Slovic, The Perception of Risk; Slovic and Slovic, Numbers and Nerves; Fischhoff and Kadvany, Risk.
90
Ñì. https://www.datacamp.com for more on the Titanic data science tutorial. I’ve omitted some parts of the tutorial for readability.
91
Quach, “Facebook Pulls Plug on Language-Inventing Chatbots?”
92
Angwin et al. “A World Apart.”
93
Valentino-DeVries, Singer-Vine, and Soltani, “Websites Vary Prices, Deals Based on Users’ Information.”
94
Hannak et al., “Measuring Price Discrimination and Steering on E-Commerce Web Sites.”
95
Heffernan, “Amazon’s Prime Suspect.”
96
Angwin, Mattu, and Larson, “Test Prep Is More Expensive – for Asian Students.”
97
Brewster and Lynn, “Black-White Earnings Gap among Restaurant Servers.”
98
Sharkey, “The Destructive Legacy of Housing Segregation.”
99
Pasquale, The Black Box Society.
100
Lord, A Night to Remember; Brown, “Chronology – Sinking of S. S. TITANIC.”
101
Halevy, Norvig, and Pereira, “The Unreasonable Effectiveness of Data,” 8.
102
“Robot Car ‘Stanley’ designed by Stanford Racing Team.”
103
“Karel the Robot.”
104
Pomerleau, “ALVINN, an Autonomous Land Vehicle in a Neural Network”; Hawkins, “Meet ALVINN, the Self-Driving Car from 1989.”
105
Mundy, “Why Is Silicon Valley So Awful to Women?”
106
Oremus, “Terrifyingly Convenient.”
107
DARPA Public Affairs, “Toward Machines That Improve with Experience.”
108
National Highway Traffic Safety Administration and US Department of Transportation, “Federal Automated Vehicles Policy.”
109
Ñì.: Yoshida, “Nvidia Outpaces Intel in Robo-Car Race.” Yoshida may be referring to a different standards document, in which Level 2 is equivalent to the Level 3 quoted here. Again: language and standards matter a great deal in engineering.
110
Liu et al., “CAAD: Computer Architecture for Autonomous Driving”; Thrun, “Making Cars Drive Themselves”; Thrun, “Winning the DARPA Grand Challenge.”
111
Ñêåò÷ ìîæíî íàéòè íà YouTube, íàáðàâ â ïîèñêå More Cowbell – SNL. – Ïðèì. ðåä.
112
Ñì.: Singh, “Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey.” Åñëè âàì èíòåðåñíà òåìà, îáðàòèòå âíèìàíèå íà ðàáîòû Best, Damned Lies and Statistics. Ñòàòèñòèêà – ýòî îäèí èç ñïîñîáîâ èíòåðïðåòàöèè ñîöèàëüíûõ ïðîáëåì, è íåðåäêî îíà ïîìîãàåò âûÿâèòü ïðîáëåìû. Òàê, íàïðèìåð, Mothers Against Drunk Driving ïðè ïîìîùè ñòàòèñòèêè ñïîñîáñòâîâàëè òðàíñôîðìàöèè íîðì îòíîñèòåëüíî âîæäåíèÿ â íåòðåçâîì âèäå. Ñåãîäíÿ áîëüøèíñòâî ñîãëàñíî ñ òåì, ÷òî íå ñòîèò ñàäèòüñÿ íåòðåçâûìè çà ðóëü. Îäíàêî óòâåðæäåíèå, ÷òî ëþäè, â îòëè÷èå îò ìàøèí, íå äîëæíû âîäèòü, – ýòî ñîâñåì äðóãàÿ èñòîðèè.
113
Chafkin, “Udacity’s Sebastian Thrun, Godfather of Free Online Education, Changes Course.”
114
Marantz, “How ‘Silicon Valley’ Nails Silicon Valley.”
115
Dougherty, “Google Photos Mistakenly Labels Black People ‘Gorillas.’”
116
Evtimov et al., “Robust Physical-World Attacks on Deep Learning Models.”
117
Hill, “Jamming GPS Signals Is Illegal, Dangerous, Cheap, and Easy.”
118
Ñì. Harris, “God Is a Bot, and Anthony Levandowski Is His Messenger”; Marshall, “Uber Fired Its Robocar Guru, But Its Legal Fight with Google Goes On.” Harris also writes that Levandowski founded a religious organization, Way of the Future, in an attempt to “develop and promote the realization of a Godhead based on Artificial Intelligence.”
119
Vlasic and Boudette, “Self-Driving Tesla Was Involved in Fatal Crash, U. S. Says.”
120
Tesla, Inc., “A Tragic Loss.”
121
Lowy and Krisher, “Tesla Driver Killed in Crash While Using Car’s ‘Autopilot.’”
122
Liu et al., “CAAD: Computer Architecture for Autonomous Driving”
123
Ôðàíöóçñêèé ðåñòîðàí ìîðñêîé êóõíè íà Ìàíõýòòåíå, èìååò òðè çâåçäû Ìèøëåí. – Ïðèì. ðåä.
124
Sorrel, “Self-Driving Mercedes Will Be Programmed to Sacrifice Pedestrians to Save the Driver.”
125
Taylor, “Self-Driving Mercedes-Benzes Will Prioritize Occupant Safety over Pedestrians.”
126
Been, “Jaron Lanier Wants to Build a New Middle Class on Micropayments.”
127
Pickrell and Li, “Driver Electronic Device Use in 2015.”
128
Dadich, “Barack Obama Talks AI, Robo Cars, and the Future of the World.”
129
Newman, “What Is an A-Hed?”
130
US Bureau of Labor Statistics, “Newspaper Publishers Lose over Half Their Employment from January 2001 to September 2016.”
131
Æèòåëè Èëëèíîéñà íàçûâàþ òàê ñâîé øòàò, ïîñêîëüêó èìåííî îòòóäà íà÷àëàñü ïîëèòè÷åñêàÿ êàðüåðà À. Ëèíêîëüíà. – Ïðèì. ðåä.
132
Îäíî èç óâàæèòåëüíûõ ïðîçâèù À. Ëèíêîëüíà. – Ïðèì. ðåä.
133
Pasquale, The Black Box Society; Gray, Bounegru, and Chambers, The Data Journalism Handbook; Diakopoulos, “Algorithmic Accountability”; Diakopoulos, “Accountability in Algorithmic Decision Making”; boyd and Crawford, “Critical Questions for Big Data”; Hamilton and Turner, “Accountability through Algorithm”; Cohen, Hamilton, and Turner, “Computational Journalism”; Houston, Computer-Assisted Reporting.
134
Angwin et al., “Machine Bias.”
135
California Department of Corrections and Rehabilitation, “Fact Sheet.”
136
Angwin and Larson, “Bias in Criminal Risk Scores Is Mathematically Inevitable, Researchers Say”; Kleinberg, Mullainathan, and Raghavan, “Inherent Trade-Offs in the Fair Determination of Risk Scores.”
137
Bogost, “Why Nothing Works Anymore”; Brown and Duguid, The Social Life of Information.
138
Hempel, “Melinda Gates Has a New Mission.”
139
Somerville and May, “Use of Illicit Drugs Becomes Part of Silicon Valley’s Work Culture.”
140
Alexander and West, The New Jim Crow.
141
Hern, “Silk Road Successor DarkMarket Rebrands as OpenBazaar.”
142
Brown, “Nearly a Third of Millennials Have Used Venmo to Pay for Drugs.”
143
Newman, “Who’s Buying Drugs, Sex, and Booze on Venmo? This Site Will Tell You.”
144
Êðèñòåíñåí Ê. Äèëåììà èííîâàòîðà. – Ì.: Àëüïèíà Ïàáëèøåð, 2019.
145
“Jeremy Corbyn, Entrepreneur.”
146
Terwiesch and Xu, “Innovation Contests, Open Innovation, and Multiagent Problem Solving.”
147
 Ðîññèè âûõîäèò ïîä íàçâàíèåì «Ïîñëåäíèé ãåðîé». – Ïðèì. ðåä.
148
Îò àíãë. ñëîâ bus – àâòîáóñ è entrepreneur – ïðåäïðèíèìàòåëü. – Ïðèì. ðåä.
149
Êîìàíäíûé âèä ñïîðòà ñ ëåòàþùèì äèñêîì. – Ïðèì. ðåä.
150
Îò àíãë. vapor – òóìàí, ware – ïðîäóêò. – Ïðèì. ðåä.
151
Morais, “The Unfunniest Joke in Technology.”
152
Tufte, The Visual Display of Quantitative Information.
153
Éîòòàáàéò ðàâåí òðèëëèîíó òåðàáàéòîâ. – Ïðèì. ðåä.
154
Ïðîïðèåòàðíûé ñîôò – ïðîãðàììíîå îáåñïå÷åíèå, ÿâëÿþùååñÿ ÷àñòíîé ñîáñòâåííîñòüþ. Òàêæå èçâåñòåí êàê êîììåð÷åñêèé ñîôò.  äàííîì ñëó÷àå ýòî îçíà÷àåò, ÷òî êîìàíäå óäàëîñü äîãîâîðèòüñÿ î ôèíàíñîâîé ïîääåðæêå ñî ñòîðîíû è ôàêòè÷åñêè ñîçäàòü ðàáî÷èé ïðîåêò, êîòîðûé îíè ïðåäñòàâèëè ðåàëüíîìó èíâåñòîðó, à íå ñóäüÿì â ðàìêàõ êîíêóðñà, ò. å. èì óäàëîñü ñîçäàòü íàñòîÿùóþ êîìïàíèþ è ïðîäàòü ïðîåêò. – Ïðèì. ïåð.
155
Seife, Proofiness; Kovach and Rosenstiel, Blur.
156
Broussard, “Artificial Intelligence for Investigative Reporting.”
157
Îðãàíèçàöèè, ÷üÿ ôèíàíñîâàÿ äåÿòåëüíîñòü ðåãóëèðóåòñÿ ñîîòâåòñòâåííî 527-ì è 501-ì ïàðàãðàôàìè Íàëîãîâîãî êîäåêñà. – Ïðèì. ðåä.
158
Mayer, Dark Money; Smith and Powell, Dark Money, Super PACs, and the 2012 Election.
159
Bump, “Donald Trump’s Campaign Has Spent More on Hats than on Polling.”
160
Donn, “Eric Trump Foundation Flouts Charity Standards.”
161
Sheivachman, “Clinton vs. Trump.”
162
Fletcher and Zuckerman, “Hedge Funds Battle Losses.”
163
Russell and Vinsel, “Let’s Get Excited about Maintenance!”
164
Crawford, “Artificial Intelligence’s White Guy Problem”; Crawford, “Artificial Intelligence – With Very Real Biases”; Campolo et al., “AI Now 2017 Report”; boyd and Crawford, “Critical Questions for Big Data.”
165
boyd, Keller, and Tijerina, “Supporting Ethical Data Research”; Zook et al., “Ten Simple Rules for Responsible Big Data Research”; Elish and Hwang, “Praise the Machine! Punish the Human! The Contradictory History of Accountability in Automated Aviation.”
166
Chen, “The Laborers Who Keep Dick Pics and Beheadings Out of Your Facebook Feed.”
167
Fairness and Transparency in Machine Learning, “Principles for Accountable Algorithms and a Social Impact Statement for Algorithms.”
168
Data Privacy Lab, “Mission Statement”; Sweeney, “Foundations of Privacy Protection from a Computer Science Perspective.”
169
Pilhofer, “A Note to Users of DocumentCloud.”
170
Äàåò âîçìîæíîñòü ñòóäåíòàì ïîïðîáîâàòü ñåáÿ â ðîëè êîíãðåññìåíîâ ÑØÀ. – Ïðèì. ðåä.
 Âåðíóòüñÿ ê ïðîñìîòðó êíèãè
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