comScore (company), 5
Consumers. See Shoppers
Cookies (on computers), 2
Corporations
electronic résumés for, 195
interest of, in collecting data about people, 134, 143–44, 150, 152
interest of, in collecting data about their employees, 18–40, 63–64, 97, 106, 150, 204
marketing research by, 13, 75
See also Names of specific corporations
Counterterrorism. See Terrorists
Counties, 85–86
Cows (fistulated), 169–75, 195, 198
Craver, Jalaire (author’s wife), 41, 61–62, 85, 86
and dating questionnaire, 182, 184, 190–91, 198–200
Credit (consumer), 4, 8, 12, 125–26, 183, 205, 224n
Criminals, 89, 133–41
“Crossing Guards” tribe, 82, 88
Cryptology, 128–30
Cumby, Chad, 47
Customer loyalty cards, 41, 43, 48–49
Cycorp project, 107–8
D
Danks, David, 227n
Dantzig, George, 31
Dantzig, Tobias, 7
“Dark cutters,” 173
Data, 221n
altering, 126
amount of, dealt with by human brain, 25
analyzing, 11, 15, 183, 201–3
collecting of, by casinos, 140–41
collecting of, by governments seeking terrorists, 123–53
collecting of, by political parties, 67–95, 225–26n
collecting of, by retail stores, 41, 43, 48, 49, 70, 125, 141, 183, 192
collecting of, from cell phones, 4, 5, 16, 195–99
collecting of, from cows, 169–75, 195
collecting of, on patients, 154–81, 205, 208, 227–29n
correcting, 229n
as empowering people, 199, 202–5, 216
geometry applied to, 118–21
individuals’ production of, 4–5, 18–19, 195–98
in online data services, 182–200
people as, 11–14, 207
“repurposing” of, 152
selling our own, 205
value of, 7
See also Algorithms; Behavior; Computers; Data mining; Privacy; Surveillance; Symbols
Data masters, 204–5
Data mining, 44, 57, 225–26n
of audio files, 145
of blogs, 99–116
dangers of, 116, 126–27, 141–42, 148–49, 151–52
of e-mail, 5, 18, 22, 35–37, 104, 106
“false positives” in, 89, 113, 126, 132–33, 151
by NSA, 124–31, 141
of photos, 124, 142–44, 198
Data serfs, 18, 40, 97, 204
Dating services (online), 182–200, 205
Democratic Party, 67–70, 73–86, 92–93
Demographics
and advertising, 10, 102–3
of bloggers, 98
vs. “buckets,” 54–55
data mining and, 124
of Internet survey respondents, 78
and online dating services, 186, 188, 192
and voting behavior, 70–74, 83–87, 92, 225n
See also Age; Gender
Deodorants, 101–2
Dietrich, Brenda, 222n
“Directors” (personality type), 189–91
Disciplines (academic)
algorithms as making study possible across, 14, 57–58
and mathematical modeling, 23, 35, 129, 142, 149–50, 207, 213–14
Dishman, Eric, 154–67, 174, 176, 178–80
DNA, 160, 202
behavior likened to, 22, 55–58, 92, 142, 145, 198
“Domain,” 114
Dopamine, 189
Dowd, Matthew, 68, 92
Driving sensors, 172
Dubrawski, Artur, 146–47
E
Eagle, Nathan, 195–99
“Edges” (in social networks), 147–48
“Eigenbehavior,” 197
Einhorn, Jack, 14–15, 210–13
Einhorn, Joe, 210–13
E-mail
data mining of, 5, 18, 22, 35–36, 104, 106
memory issues revealed in, 174–75, 177
See also Spam
Emotion analysis, 16, 92, 101, 144–45, 183
Engineers, 6, 29, 129
See also Numerati
Enigma machine, 129
Enron Corporation, 21–22, 36
Essex County (New Jersey), 85–86
Estrogen, 189, 190
Evans, David, 152
“Explorers” (personality type), 189–91, 200
F
Facebook. See Social networks
Facial recognition, 63–64, 142–44
Fair, Bill, 8, 93
Fair Isaac (company), 8, 93, 222n, 224n
Falls (by the elderly), 161, 162
“False positives” (in data mining), 89, 113, 126, 132, 151
FBI, 124
“Feedback loop,” 126
Finance, 21, 26–27
See also Credit (consumer)
Fisher, Helen, 182–85, 187–91, 198
Focus groups, 98
Ford Corporation, 226n
Fox, Michael J., 165
Friedman, Jerry, 126
Friends
finding, as a business, 197–99
and “Next Friend Analysis,” 146–48, 211
See also Dating services; Social networks
G
“Garbage in, garbage out,” 215, 229n
“Gatorade” tribe, 103
Gender, 100–101, 108–11, 115–16
Generations. See Age
Genetics, 225n, 227–28n
See also Biology and biologists; DNA
Geometry, 118–20
Germany
codes used by, 129
“smart carts” in, 48, 65–66
spies in, 4, 120
Ghani, Rayid, 44–51, 53–54, 57–64, 70
Gish, Herb, 144–45
Globalization, 10–11
Goldman, Neal, 208–13
“Gold standard,” 109, 111
Goldwater, Barry, 73
Google, 104, 106, 158, 159, 164, 199, 213–16
advertisements on, 117–18
amount of data handled by, 124
founders of, 207
“I’m Feeling Lucky” button on, 193
mathematics as basis of, 7, 145
and search engine optimization, 194
successes of, 13, 90–91
Gore, Al, 91, 95
Gotbaum, Joshua, 67–70, 74–84, 89–90, 93, 115, 126, 207
Governments
discrimination by, 202
and health care costs, 174
interest of, in identifying people, 13, 104, 106, 134–35, 143–44, 150, 152
See also Specific countries and intelligence organizations
Grocery store data. See Retail store data
Gross, Neil, 221n
Guantánamo Bay, 89, 175
H
Habeas corpus, 127
Haren, Pierre, 38–39
Health care costs, 157, 159, 161, 174, 222n
See also Insurance companies
Hearing, 165
See also Voices
“Hearth Keepers” tribe, 78, 81, 93
Heckerman, David, 58
Henry, Mike, 93–94, 226n
HIV/AIDS, 58, 203
Hormones, 189–90
“Horse races” (statistical tests), 224n
Huber, Peter, 124
Hyperaggressive disorder, 179, 180
I
IBM (company)
and algorithms, 222–23n
behavior predicting by, 14, 188
computer simulations used by, 29
Entity Analytics group in, 135, 149
mathematical modeling of employees of, 20–33, 37–38, 223n
Watson Research Laboratory of, 20–22, 114
IBM Anonymous Resolution (encry
ption software), 152
IBM Global Services (company), 33
ICX Technology (company), 124
Identity
data collection about, 75
encryption of, 152
hiding of, 203–4
See also Facial recognition; Names; Privacy; Voices: recognition of
ILOG (company), 38
Images. See Photos
“I’m Feeling Lucky” button (on Google), 193
India, 108, 145
Inform Technologies, 14, 208, 210–13
“Inner Compass” tribe, 78, 82, 93
In-Q-Tel (company), 135
Insurance companies, 159, 172, 174, 202, 222n
Intel (company), 154–55, 159, 163–65, 167, 179, 205
Internet
blogging on, 96–122
collecting data about use of, 1–4, 42, 43
criminals’ use of, 134
dating services on, 182–200, 205
early days of, 158
government tapping of, 104, 124
journalism on, 17–18, 39–40, 211–13
privacy disclosures on, 205
social networks on, 97, 98, 104–6
surveys on, 78, 182–200
terrorists’ use of, 130
See also Advertisers; Computers; Data; Specific computer corporations
Iraq, 131
Ireland, 159
Isaac, Earl, 8, 93
Isoquant, 94
J
Jacks, Tyler, 168
Jackson’s Dilemma (Murdoch), 177
Jonas, Jeff, 131–37, 140–41, 149, 150–53, 208
Journalism, 17–18, 39–40, 211–13
K
Kaine, Tim, 93
Kansas State University, 169
Kaushansky, Howard, 99–101, 103, 109, 114–16, 212
Koch Cancer Institute, 167–68
Kremer, Ted, 109–10, 112–14, 126
Kumar, V., 51–53
L
Language Analysis Systems (company), 149
Las Vegas (Nevada), 131–41, 144, 150–53
Loans. See Credit (consumer)
Logistics, 65
See also Operations research
Lovers, 182–200, 202
M
Machine learning, 46, 59, 106–16, 122, 164, 191, 193–95, 202
See also Computers; Data mining
Macular degeneration, 227–28n
“Magic carpet” sensors, 156–58, 161–63, 168, 205
Malchow, Hal, 225–26n
Marketing. See Advertisers
“Massive redundancy,” 64
Mass-production, 10, 17–18
and retail, 42–43, 54
and “virtual assembly line,” 39, 40
Match.com, 192
Mathematical models (statistical models)
academic disciplines’ contributions to, 23, 35, 129, 149–50, 207, 213–14
of baseball players, 27–28
of bloggers, 99, 104, 108–11, 121–22
of criminals, 89
doubts about, 89, 132–33, 215–16
of journalism readers, 211
of lovers and friends, 148, 182–200, 202
Numerati’s use of, 12–13, 160
of patients, 154–81, 205, 208, 222n, 227–29n
people as more than, 11–12, 28, 30, 201–2
of people’s writing, 176–78
of shoppers, 46–66
of students, 195–97
of terrorists, 127, 132–33, 141, 148
of voters, 67–95, 202, 204–5, 207
of workers, 17–40, 202, 204
of World War II convoys, 30–31
See also Algorithms; Geometry
Mathematicians
competition over hiring of, 145–46
as making sense of data, 6, 9, 15, 21–22, 30, 175–77
myths about, 206–14
NSA’s employment of, 104, 123, 127–31, 146
See also Data; Mathematical models; Numerati; Symbols
Mayo Clinic, 201, 228–29n
McGraw-Hill (company), 210
Medical records, 180–81
Medical sensors, 155–59, 161–63, 166, 168, 172, 177, 205
Medications, 166–68, 222n
See also Health care costs
Memory lapses, 154, 156, 159–60, 174–78, 180
Metro (company), 48, 65–66
Microsoft (company), 14, 19, 48, 58, 145, 159
Microtargeting
by advertisers, 42, 51–55, 57, 91, 205, 224n
of voters, 67–95, 225n
Mills, Fred, 124
MIT, 167–68, 196
Mojsilovic, Aleksandra, 22–23
Money, 26
Monitor (Yankelovich publication), 76–77
Monitors. See Sensors
Montclair (New Jersey), 82, 86, 90
Morgan, David, 1–4, 9–11, 15–16, 19, 54, 187, 212, 228n
Multiple discriminate analysis, 87–88
Murdoch, Iris, 177
MySpace. See Social networks
N
Names
finding people by, 13, 132, 141, 149–50, 226n
on phone prompts, 176
protection of, in data mining, 152
NASA, 226–27n
National Cryptologic Museum, 123, 125, 127–30
National Science Foundation, 170
National Security Agency (NSA)
data mining by, 104, 142–46
mathematicians working for, 104, 123, 127–31, 145–46, 214
social network interpretation by, 35, 149–50
Natural language processing, 107–8
“Negotiators” (personality type), 189–91, 200
Netflix, 52, 125, 162
“Neural network” programs, 125–26
Newton, Isaac, 184
New York Times, 143
Next Friend Analysis, 146–50, 211
Nicaragua, 73
Nicolov, Nicolas, 113–14, 116, 118–21
Nielsen BuzzMetrics (company), 104, 121
9/11 terrorist attack, 104, 123–26, 130–31, 135, 171, 221n
“Nodes” (in social networks), 147
“Noise,” 5
No Place to Hide (O’Harrow), 75
NORA software, 133–35, 152
Norman (fistulated cow), 169–75, 195, 198
NSA. See National Security Agency
Numbers. See Data; Mathematical models; Numerati; Symbols
Numerati
defined, 9
earnings of, 15, 208–14
myths about, 206–14
search for hidden correlations among people by, 1–4, 11, 56–57, 116, 165, 205
successful methods of, 183
tools of, 13, 14, 30–32, 39, 57–58, 205, 206–7
See also Computer scientists; Mathematicians
O
O’Harrow, Robert, Jr., 75
Operations research, 30–33, 38, 43
Optimization, 32–33, 37, 194
Orcatech (Oregon Center for Aging and Technology), 175–78
Outliers, 37, 61, 127
P
Page, Larry, 207, 215
Palmisano, Sam, 29
Parkinson’s disease, 165–67
Patients, 20, 154–81, 205, 208, 227–29n
Pavel, Micha, 175–77
People
as bloggers, 96–122
data as empowering, 199, 202–5, 216
innocent, inadvertently caught by mathematical modeling (“false positives”), 89, 125, 126–27, 151
as lovers, 182–200, 202
mathematical modeling of, 12–13, 20–40, 44–46, 99–100, 201–16
as more than mathematical models, 11–12, 28, 30, 201–2
Numerati’s search for hidden correlations among, 1–4, 11, 56–57, 116, 165, 205
as patients, 20, 154–81, 205, 208, 227–29n
as shoppers, 20, 41–66, 97
as terrorists, 35, 123–53, 202
as voter
s, 12, 58, 67–95, 202, 204–5, 207
as workers, 17–40, 63–64, 97, 106, 150, 202, 204
See also Behavior; Data; Mathematical models; Social networks
Perot, H. Ross, 81–82
Philipose, Matthai, 163–65
Photos
data mining of, 124, 142–44, 198
on online dating service profiles, 185–87
on phone prompts, 176
sharing, via computers, 9
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