Index

• Abacus, 75

• Ability beliefs, 83
• Academy at Palumbo, 56–57
• Ackerman, Arlene, 58–59
• Activism, cyberspace, 82–83
• Adair, Bill, 45
• AI Now Institute, 194–195
• AirBnB, 168
• Albrecht, Steve, 159
• Alda, Alan, 70
• Alexa, 38–39, 72
• Alexander, Michelle, 159
• Algorithmic accountability reporting, 7, 43–44, 65–66
• Algorithms
  • bias in, 44, 150, 155–157, 195
  • defined, 7, 94
  • elevator, 157
  • function of, 43–44
  • risk, 44, 155–156
  • tic-tac-toe, 34
• Alphabet, 96
• AlphaGo, 33–37
• Amazon, 115, 158
• Analytical engine, 76
• Anarcho-capitalism, 83
• Anderson, C. W., 46–47
• Angwin, Julia, 154–156
• App hackathons, 165–174
• Apple Watch, 157
• Artificial intelligence (AI)
  • beginnings, 69–73
  • expert systems, 52–53, 179
  • fantasy of, 132
  • in film, 31, 32, 198
  • foundations of, 9
  • future of, 194–196
  • games and, 33–37
  • general, 10–11, 32
  • narrow, 10–11, 32–33, 97
  • popularity of, 90
  • real vs. imagined, 31–32
  • research, women in, 158
  • sentience challenge in, 129
• Asimov, Isaac, 71
• Assembly language, 24
• Association for Computing Machinery (ACM), 145
• Astrolabe, 76
• Asymmetry, positive, 28
• Automation technology, 176–177
• Autopilot, 121
• Availability heuristic, 96

• Babbage, Charles, 76–77
• Bailiwick (Broussard), 182–185, 190–191, 193
• Barlow, John Perry, 82–83
• Bell Labs, 13
• Bench, Shane, 84
• Ben Franklin Racing Team (Little Ben), 122–127
• Berkman Klein Center (Harvard), 195
• Berners-Lee, Tim, 4–5, 47
• Bezos, Jeff, 73, 115
• Bias
  • in algorithms, 44, 150, 155–157
  • in algorithms, racial, 44, 155–156
  • genius myth and, 83–84
  • programmers and, 155–158
  • in risk ratings, 44, 155–156
  • in STEM fields, 83–84
• Bill & Melinda Gates Foundation, 60–61, 157
• Bipartisan Campaign Reform Act, 180
• Bitcoin, 159
• Bizannes, Elias, 165, 166, 171
• Blow, Charles, 95
• Boggs, David, 67–68
• Boole, George, 77
• Boolean algebra, 77
• Borden, Brisha, 154–155
• Borsook, Paulina, 82
• Bowhead Systems Management, 137
• boyd, danah, 195
• Bradley, Earl, 43
• Brains 19–20, 95, 128–129, 132, 144, 150
• Brand, Stewart, 5, 29, 70, 73, 81–82
• Brin, Sergei, 72, 151
• Brown, Joshua D., 140, 142
• Bump, Philip, 186
• Burroughs, William S., 77
• Burroughs, William Seward, 77

• Calculation vs. consciousness, 37
• Cali-Fame, 186
• California, drug use in, 158–159
• Cameron, James, 95
• Campaign finance, 177–186, 191
• Čapek, Karel, 129
• Caprio, Mike, 170–171
• Carnegie Mellon University, autonomous vehicle research
  • ALVINN, 131
  • University Racing Team (Boss), 124, 126–127, 130–131
• Cars
  • deaths associated with, 136–138, 146
  • distracted driving of, 146
  • human-centered design for, 147
• Cars, self-driving
  • 2005 Grand Challenge, 123–124
  • 2007 Grand Challenge, 122–127
  • algorithms in, 139
  • artificial intelligence in, 129–131, 133
  • deaths in, 140
  • driver-assistance technology from, 135, 146
  • economics of, 147
  • experiences in, 121–123, 125–126, 128
  • fantasy of, 138, 142, 146
  • GPS hacking, 139
  • LIDAR guidance system, 139
  • machine ethics, 144–145, 147
  • nausea in, 121–123
  • NHTSA categories for, 134
  • problems/limitations, 138–140, 142–146
  • research funding, 133
  • SAE standards for levels of automation, 134–135
  • safety, 136–137, 140–142, 143, 146
  • sentience in, 132
  • Uber’s use of, 139
  • Udacity open-source car competition, 135
  • Waymo technology, 136
• CERN, 4–5
• Cerulo, Karen A., 28
• Chess, 33
• Children’s Online Privacy Protection Act (COPPA), 63–64
• Chinese Room argument, 38
• Choxi, Heteen, 122
• Christensen, Clayton, 163
• Chrome, 25, 26
• Citizens United, 177, 178, 180
• Clarke, Arthur C., 71–72
• Client-server model, 27
• Clinkenbeard, John, 172
• Cloud computing, 26, 52, 196
• Cohen, Brian, 56–57
• Collins, John, 117
• Common Core State Standards, 60–61
• Communes, 5, 10
• Computer ethics, 144–145
• Computer Go, 34–36
• Computers
  • assumptions about vs. reality of, 8
  • components, identifying, 21–22
  • consciousness, 17
  • early, 196–199
  • human, 77–78, 198
  • human brains vs., 19–20, 128–129, 132, 144, 150
  • human communication vs., 169–170
  • human mind vs., 38
  • imagination, 128
  • limitations, 6–7, 27–28, 37–39
  • memory, 131
  • modern-day, development of, 75–79
  • operating systems, 24–25
  • in schools, 63–65
  • sentience, 17, 129
• Computer science
  • bias in, 79
  • ethical training, 145
  • explaining the world through, 118
  • women in, 5
• Consciousness vs. calculation, 37
• Constants in programming, 88
• Content-management system (CMS), 26
• Cooper, Donna, 58
• Copeland, Jack, 74–75
• Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), 44, 155–156
• Cortana, 72
• Counterculture, 5, 81–82
• Cox, Amanda, 41–42
• Crawford, Kate, 194
• Crime reporting, 154–155
• CTB/McGraw-Hill, 53
• Cumberbatch, Benedict, 74
• Cyberspace activism, 82–83

• DarkMarket, 159
• Dark web, 82
• Data
  • on campaign finance, 178–179
  • computer-generated, 18–19
  • defined, 18
  • dirty, 104
  • generating, 18
  • people and, 57
  • social construction of, 18
  • unreasonable effectiveness of, 118–119, 121, 129
• Data & Society, 195
• DataCamp, 96
• Data density theory, 169
• Data journalism, 6, 43–47, 196
• Data Journalism Awards, 196
• Data journalism stories
  • cost-benefit of, 47
  • on inflation, 41–42
  • Parliament members’ expenses, 46
  • on police speeding, 43
  • on police stops of people of color, 43
  • price discrimination, 46
  • on sexual abuse by doctors, 42–43
• Data Privacy Lab (Harvard), 195
• Data Recognition Corporation (DRC), 53
• Datasets in machine learning, 94–95
• Data visualizations, 41–42
• Deaths
  • distracted driving accidents, 146
  • from poisoning, 137
  • from road accidents, 136–138
  • in self-driving cars, 140
• Decision making
  • computational, 12, 43, 150
  • data-driven, 119
  • machine learning and, 115–116, 118–119
  • subjective, 150
• Deep Blue (IBM), 33
• Deep learning, 33
• Defense Advanced Research Projects Agency (DARPA) Grand Challenge, 123, 131, 133, 164
• Desmond, Matthew, 115
• Detroit race riots story, 44
• Dhondt, Rebecca, 58
• Diakopoulos, Nicholas, 46
• Difference engine, 76
• Differential pricing and race, 116
• Digital age, 193
• Digital revolution, 193–194
• Dinakar, Karthik, 195
• Django, 45, 89
• DocumentCloud, 52, 196
• Domino’s, 170
• Drone technology, 67–68
• Drug marketplace, online, 159–160
• Drug use, 80–81, 158–160
• Duncan, Arne, 51
• Dunier, Mitchell, 115

• Edison, Thomas, 77
• Education
  • change, implementing in, 62–63
  • Common Core State Standards, 60–61
  • competence bar in, 150
  • computers in schools, 63–65
  • equality in, 77–78
  • funding, 60
  • supplies, availability of, 58
  • technochauvinist solutions for, 63
  • textbook availability, 53–60
  • unpredictability in, 62
• 18F, 178–179
• Electronic Frontier Foundation, 82
• Elevators, 156–157
• Eliza, 27–28
• Emancipation Proclamation, 78
• Engelbart, Doug, 25, 80–81
• Engineers, ethical training, 145
• ENIAC, 71, 194, 196–199
• Equality
  • in education, 77–78
  • techno hostility toward, 83
  • technological, creating, 87
  • technology vs., 115, 156
  • for women, 5, 77–78, 83–85, 158
• Essa, Irfan, 46
• Ethics, 144–145, 147
• EveryBlock, 46
• Expertise, cognitive fallacies associated, 83
• Expert systems, 52–53, 179

• Facebook, 70, 83, 152, 158, 197
• Facial recognition, 157
• Fact checking, 45–46
• Fake news, 154
• Family Educational Rights and Privacy Act (FERPA), 63–64
• FEC, McCutcheon v., 180
• FEC, Speechnow.org v., 180
• FEC.gov, 178–179
• Film, AI in, 31, 32, 198
• FiveThirtyEight.com, 47
• Foote, Tully, 122–123, 125
• Ford Motor Company, 140
• Fowler, Susan, 74
• Fraud
  • campaign finance, 180
  • Internet advertising, 153–154
• Free press, role of, 44
• Free speech, 82
• Fuller, Buckminster, 74
• Futurists, 89–90

• Games, AI and, 33–37
• Gates, Bill, 61
• Gates, Melinda, 157–158
• Gawker, 83
• Gender equality, hostility toward, 83
• Gender gap, 5, 84–85, 115, 158
• Genius, cult of, 75
• Genius myth, 83–84
• Ghost-in-the-machine fallacy, 32, 39
• Giffords, Gabby, 19–20
• GitHub, 135
• Go, 33–37
• Good Old-Fashioned Artificial Intelligence (GOFAI), 10
• Good vs. popular, 149–152, 160
• Google, 72
• Google Docs, 25
• Google Maps API, 46
• Google Street View, 131
• Google X, 138, 151, 158
• Government
  • campaign finance, 177–186, 191
  • cyberspace activism, antigovernment ideology, 82–83
  • tech hostility toward, 82–83
• Graphical user interface (GUI), 25, 72
• Greyball, 74
• Guardian, 45, 46

• Hackathons, 165–174
• Hackers, 69–70, 82, 153–154, 169, 173
• Halevy, Alon, 119
• Hamilton, James T., 47
• Harley, Mike, 140
• Harris, Melanie, 58–59
• Harvard, Andrew, 184
• Harvard University
  • Berkman Klein Center, 195
  • Data Privacy Lab, 195
  • mathematics department, 84
• “Hello, world” program, 13–18
• Her, 31
• Hern, Alex, 159
• Hernandez, Daniel, Jr., 19
• Heuristics, 95–96
• Hillis, Danny, 73
• Hippies, 5, 82
• HitchBOT, 69
• Hite, William, 58
• Hoffman, Brian, 159
• Holovaty, Adrian, 45–46
• Home Depot, 46, 115, 155
• Hooke, Robert, 88
• Houghton Mifflin Harcourt (HMH)
• HP, 157
• Hugo, Christoph von, 145
• Human-centered design, 147, 177
• Human computers, 77–78, 198
• Human error, 136–137
• Human-in-the-loop systems, 177, 179, 187, 195
• Hurst, Alicia, 164

• Illinois quarter, 153–154
• Imagination, 89–90, 128
• Imitation Game, The (film), 74
• Information industry, annual pay, 153
• Injury mortality, 137
• Innovation
  • computational, 25
  • disruptive, 163, 171
  • funding, 172–173
  • hackathons and, 166
• Instacart, 171
• Intelligence in machine learning
• Interestingness threshold, 188
• International Foundation for Advanced Study, 81
• Internet
  • advertising model, 151
  • browsers, 25, 26
  • careers, annual pay rates, 153
  • core values, 150
  • drug marketplace, 159–160
  • early development of the, 5, 81
  • fraud, 153–154
  • online communities, technolibertarianism in culture of, 82–83
  • rankings, 72, 150–152
• Internet Explorer, 25
• Internet pioneers, inspiration for, 5, 81–82
• Internet publishing industry, annual pay, 153
• Internet search, 72, 150–152
• Ito, Joi, 147, 195

• Jacquard, Joseph Marie, 76
• Java, 89
• JavaScript, 89
• Jobs, Steve, 25, 70, 72, 80, 81
• Jones, Paul Tudor, 187–188
• Journalism. See also Data journalism
  • algorithmic accountability reporting, 7, 43–44, 65–66
  • artificial intelligence for, 52–53
  • computational, 7, 46–47, 190
  • computer-assisted reporting, 44–45
  • machine learning in, 52
  • precision reporting, 44
  • social science applied to, 44

• Kaggle, 96
• Kalanick, Travis, 74, 139
• Kamal, Fawzi, 139
• Karel the Robot, 129–130
• Karpathy, Andrej, 149
• Kay, Alan, 25, 72
• Ke Jie, 33
• Kernighan, Brian, 13
• Kesey, Ken, 81
• Kilgore, Barney, 152
• Kinect, 157
• Kleinberg, Jon, 155–156
• Krafcik, John, 136, 137
• Kroeger, Brooke, 78
• Kubrick, Stanley, 71
• Kunerth, Jeff, 43
• Kurzweil, Raymond, 73, 89, 90
• Kushleyev, Alex, 124–125

• Language
  • computational communication problems, 87–89
  • fluidity of, 91
  • human vs. mathematical, 88
  • naming problem in, 88–89
• Lanier, Jaron, 145–146
• Lazer, David, 115
• Leadership gender gap, 158
• Learning, 89
• LeCun, Yann, 90
• Lee, Dan, 123
• Leibniz, Gottfried, 75, 76, 77
• Lench, Heather, 84
• Leslie, S. J., 83
• Lessig, Lawrence, 194
• Levandowski, Anthony, 139–140
• Levy, Steven, 70
• Lexus, 123, 140
• Libertarianism, 82–83, 138, 159–160
• Libraries, 96–97
• Lightoller, Charles, 116
• Lincoln, Abraham, 78
• LinkedIn, 158
• Linux, 24–25
• Lipton, Zachary, 114
• Literacy, technological, 21
• Long, Milton, 117–118
• Long Now Foundation, 73
• Lord, Walter, 117–119
• Loughner, Jared Lee, 19
• Lovelace, Ada, 76
• LSD, 81
• Lucas, George, 70

• Machine intelligence, determining, 37–38
• Machine language, 24
• Machine learning
  • algorithms in, 94
  • defined, 11, 89, 91–92
  • doing DataCamp Titanic tutorial, 96–119
  • datasets in, 94–96
  • intelligence in, 92–93
  • in journalism, 52
  • limitations, 119
  • linguistic confusion, 89
  • reinforcement, 93
  • social decision making and, 115–116, 119
  • supervised, 93
  • training data in, 93–94
  • unsupervised, 93
• Machines, intelligence in, 33
• Mahfouz, Christl, 186
• Mapping, digital, 131
• Masch, Michael, 57
• Mathematical lookup tables, 77
• Mathematics
  • cult of genius, worship of, 75
  • developing machines for, 75–79
  • gender gap, 84–85
  • gender stereotypes associated with, 84
  • social culture of, 75
  • women in, 77–78
• May, Patrick, 158
• McCarthy, John, 70, 71
• McCutcheon v. FEC, 180
• McIntyre, Tim, 170
• McNamee, Roger, 138
• Mercedes, 144
• Merideth, Willie, 68
• Meyer, Philip, 43
• Microsoft, 25, 157
• Minimum viable product (MVP), 189–190
• Minsky, Margaret, 79
• Minsky, Marvin, 69–75, 79–80, 81, 84, 89, 129, 132, 145, 193
• MIT Artificial Intelligence Lab, 70
• Mitchell, Tom M., 92
• MIT Media Lab, 70, 72, 195
• MIT Tech Model Railroad Club (TMRC), 69–70
• Models, mathematical, 94
• Monty Python, 89
• Moore School of Engineering, 196–198
• Morais, Betsy, 167
• Mortensen, Dennis, 132
• Motor vehicle traffic-related injury mortality, 137–138
• Mullainathan, Sendhil, 155–156
• Munro, Randall, 87
• Murdoch, William, 116
• Musk, Elon, 142, 143–144

• Naming problem, 88–89
• Natanson, Hannah, 84
• National Highway Traffic Safety Association (NHTSA), 133–134
• Natural resources homework, 51–52
• Navy, U.S., 137
• Neumann, John von, 71
• Neural networks, 33
• Neville-Neil, George V., 92–93
• New Communalism movement, 5
• Newman, Barry, 152
• Newspapers, 152
• New York Times, 152
• NeXt cube, 5
• NICAR conference, 196
• Nineteenth Amendment, 78
• Northpointe, 155–156
• Norvig, Peter, 93, 118
• Nutter, Michael, 53
• NVIDIA, 140–141

• Obama Administration, 147, 194
• Object, 97
• O’Neil, Cathy, 94
• One Laptop Per Child (OLPC) initiative, 65
• oN-Line System (NLS), 25
• OpenBazaar, 159
• Operating systems, 24–25
• Opioid crisis, 158–160
• O’Reilly, Tim, 81
• OSX, 25
• Otto, 142
• Overview Project, 52

• Page, Carl Victor, Sr., 72–73
• Page, Larry, 72–73, 131, 151
• PageRank, 72, 151–152
• Palantir, 83
• Panama Papers, 196
• Pandas library, 97
• Paperclip theory, 89–90
• Papert, Seymour, 72, 73
• Pasquale, Frank, 115
• Pattis, Richard, 129
• PayPal, 83, 159
• Pearson, 53–54
• Penn and Teller, 70
• Pennsylvania System of School Assessment (PSSA), 52, 53–54
• Pereira, Fernando, 118
• Personal computer revolution, 5, 24
• Philadelphia School District, 53–60, 65–66
• Physicians, sexual abuse by, 42–43
• PillyPod, 173
• Pinker, Stephen, 90
• Pinkerton, Emma, 164
• Pizzafy, 165, 168–174
• Policing
  • quantitative methods to enhance, 155
  • racial disparities found by Stanford Open Policing Project, 43
  • speeding, 43
• PolitiFact, 45–46
• Popular vs. good, 149–152, 160
• Poverty and differential pricing, 116
• Prater, Vernon, 155
• Predictive analytics, 33
• Price discrimination, 46
• Price optimization, 114–115
• Privacy, right to, 68, 195
• Programmers
  • accountability for, 154
  • bias, 155–158
  • competence, developing, 169–170, 174
  • drug use, 158–159
  • ethical training, 145
  • income, 170–171
  • safety, attitudes toward, 73–74
  • social conventions, 74–75
• Programming. See also Software
  • anticipating the unexpected in, 28
  • artisanal small-batch model, 191
  • assembly language, 24
  • command line, 15
  • “Hello World
  • Karel the Robot exercise, 129–130
  • modularity in, 17–18
  • variables in, 88
  • wealth, impact on, 158
  • WHILE loops, 16
• ProPublica, 45
• Ptolemy, 77
• Public transportation, 138
• Pulitzer Prizes, 45
• Punch-card loom, 76
• Python, 14–17, 89, 92, 96

• Race and image recognition systems, 157
• Radiation safety, 73–74
• Radio broadcasting industry, annual pay, 153
• Raghavan, Manish, 155–156
• Rahwan, Iyad, 195
• Reddit, 82
• Reinhardt, Django, 89
• Repetti, Steve, 166, 171
• Reporting. See also Journalism
  • computer-assisted, 44–45
  • precision, 44
  • robot, 9–10
• Richardson, Kathleen, 72
• Risk, drawing conclusions about, 95–96
• Risk algorithms, 44–45, 155–156
• Ritchie, Dennis, 13, 24
• Road accident statistics, 136–138
• Roberts, Jerry, 74–75
• Robinett, Ricky, 171
• Robot-car technology, 123. See also Cars, self-driving
• Robot reporting, 9–10
• Robots, 3–4, 87–88, 129
• Rogers, Edwin, 164–165, 167, 172, 173
• Roomba, 88
• Royal, Cindy, 47
• Rudisch, Gloria, 79
• Russell, Stuart, 93

• Safari, 25, 26
• Science fiction, 71–72
• Scikit-learn, 92, 96
• Screet, 172
• Sculley, John, 72
• Searle, John, 38
• Selfies, 149
• SendGrid, 168
• Seneca Falls Convention, 78
• Sentience
  • computer, 17, 129
  • in self-driving cars, 132
• Sexual abuse, 42–43, 45
• Sexual harassment, culture of, 74
• Shar.ed, 171–172
• Sharkey, Pat, 115
• Shaw, Jennifer, 164–165, 167, 170–171, 173
• Sheivachman, Andrew, 187
• Shell programming language, 15
• Siegelmann, Hava, 133
• Silicon Valley, 166
• Silk Road, 159
• Silver, Nate, 47
• Singh, Santokh, 137
• Singularity theory, 89–90
• Siri, 28, 29, 72
• Slavery, 78
• Slovic, Paul, 83
• Smart games format (SGF), 35
• Smith, Dre, 167, 172
• Smith, Edward John, 117
• Snowden documents, 196
• Social decision making, 115–116, 119
• Social media, 158
• Society, impact of algorithmic accountability reporting, 65–66
• Society-in-the-loop machine learning, 195
• Software. See also Programming
  • autonomous systems, 187
  • in the cloud, 25–26
  • defined, 19, 22
  • development process problems, 190
  • human-in-the-loop systems, 177, 179, 187, 195
  • lifespan, 193
  • minimum viable product, 190
  • naming, 182
  • scope creep, 61
  • technical debt, 193
• Somerville, Heather, 158
• Space elevator, 71–72
• SPACES, 172–173
• Speechnow.org v. FEC, 180
• Spence, Stephen, 58–59
• Standardized testing, 52–55
• Stanford Racing Team (Junior), 124, 130–131
• Stanford Racing Team (Stanley), 123–124, 127
• Staples, 46, 115
• Star Trek: The Next Generation, 31
• Startup Bus, 163–174
• Startup House, 166
• Steiger, Paul, 45
• STEM fields, 5, 83–85, 158
• Step reckoner, 76
• Stewart, Alex, 122, 125–126
• Story Discovery Engine (Broussard), 178–180, 187, 188–191
• Survivor (television), 164
• Sweeney, Latanya, 195

• Tacocopter, 29–30
• Taplin, Jonathan, 83
• Tay Twitter bot, 69
• Teachers, underground economy, 57
• TechCrunch Disrupt hackathon, 166–167
• Tech culture
  • drug use in, 158–160
  • misogyny in, 167
  • money in, 171
• Tech Model Railroad Club (TMRC), 69–70
• Technochauvinism
  • assumptions from, 156
  • beliefs accompanying, 8
  • blaming drivers, 136
  • defined, 7–8
  • disruptive innovation and, 163
  • hallmarks of, 69
  • magical thinking of, 122
  • philosophical basis of, 75
• Technolibertarianism, 82–83
• Technology
  • breakage, 63, 156–157, 193
  • digital, uses for, 194
  • equality, creating, 87
  • gender gap, 158
  • human-centered design, 177
  • inclusive, need for, 154
  • inequality and, 83, 115, 156
  • libertarianism and, 82–83
  • limitations, 6–7, 176–177
  • mathematical, development of, 75–79
  • promises of, questioning the, 6
  • social consequences, negative, 67–69
  • white male bias in, 72, 79
• Terminal, 14–15
• Tesla, 121, 136, 139, 140–144
• Textbooks, 53–60, 63–65
• Texting while driving, 146–147
• Thayer, Jack, 117–118
• Thiel, Peter, 83, 159
• Thirteenth Amendment, 78
• Thrun, Sebastian, 124, 131, 135, 138
• Tic-tac-toe, 33–34
• Tilden Middle School, 59–60
• Titanic (disaster), 95–119
• Titanic (movie), 95
• Torvalds, Linus, 24
• Toyota, 140
• Trolley problem, 144, 147
• Trump, Donald, 83, 184–187, 194
• Tufte, Edward, 169
• Turing, Alan, 33, 74–75, 82, 83, 193
• Turing test, 33, 37–38
• Turner, Fred, 5, 81
• Twitter, 69
• 2001: A Space Odyssey (film), 31, 71, 198

• Uber, 74, 121, 138, 139–140, 142, 168
• Udacity, 135, 138
• Ukpeaġvik Iñupiat Corporation, 137
• Ulbricht, Ross, 159
• Unix, 24
• Unmanned autonomous systems (UAS), 137
• Urmson, Chris, 135
• Usher, Nikki, 47

• Vaporware, 166
• Vehicles, SAE definitions for automated, 134–135. See also Cars, self-driving
• Venmo, 159
• Vernaza, Paul, 122, 126
• Vicemo.com, 159
• Video games, 25
• Voice assistants, 28–29, 72
• Voice interfaces, 38–39
• Voting rights for women, 78

• Waite, Matt, 45
• Wall Street Journal, 45, 46, 152
• Waydo, Jaime, 135–136
• Waymo, 135–136, 139–140, 141
• Web search portals industry, annual pay, 153
• Web servers, 26–27
• Whittaker, Meredith, 194
• Whole Earth Catalog, 5, 73, 81–82
• Whole Earth eLectronic Link (WELL), 82
• Wiesner, Jerry, 71
• Wigner, Eugene, 118
• Wilkinson, Jim, 75
• Willis, Derek, 179
• Wilson, Christo, 115
• Winograd, Terry, 73
• Wired, 29, 81
• Wolfe, Tom, 81
• Wolfram, Stephen, 79
• Women’s suffrage, 78
• Worley, Cole, 171
• Wright brothers, 3, 131–132
• Writing, automated, 52

• Xerox PARC, 25, 72

• Yoshida, Junko, 135

• Zaneski, Eddie, 168–169
• Zimmerman, Henry, 71
• Zuckerberg, Mark, 31, 70