The Naked Future (36 page)

Neurotransmitters, and personality traits,
172
–74

New York City, predictive policing,
187
,
194

Nexus,
156

Ng, Andrew,
132
–34,
136
–40,
149
–50

Nieto, Enrique Peña,
196
–97

Nike+,
43
,
107
–8

Norvig, Peter,
138
–39

Nothelfer, Christine E.,
100

Nuclear accidents, Fukushima Daiichi nuclear plant,
9
–12

Numenta,
226

Obama, Barack,
79
,
170
–71,
209
,
220

OKCupid,
154
–57,
165

Olligschlaeger, Andreas,
184
–86,
188
,
191

O'Malley, Martin,
184

One Laptop per Child (OLPC) Association,
142
–47

One-to-one marketing at scale,
116

Online classes.
See
Education

Open Source Indicators (OSI),
197
–98

Operation Blue CRUSH (Crime Reduction Utilizing Statistical History),
190
–91

Operation SPOT,
204

Oroeco,
127

Osito,
xvii

Overfitting,
5

Owens, Emily G.,
192

Pacemakers, Bluetooth-enabled,
7

Pachube,
10
–12

Palantir Technologies,
218
–21

PalmPilot,
226

Pariser, Eli,
241

Parking Douche,
216

Path,
20
–21

Paul, Michael,
61
–65

PayPal,
156
,
218

Pearl, Judea,
23

Pentland, Alex “Sandy,”
167
–72,
174
,
182

Percifield, Leif,
12
–14,
239

Perry, Rick,
214

Personal data.
See also
Privacy issues

advertising use of,
106
–9

of consumers.
See
Consumers

data leakage,
20
–21

data resellers,
156

data trail, creating,
xv

health data sharing, pros/cons,
46
–48

self-tracking,
31
–37

Personality traits

based on brain chemistry,
172
–74

five-factor model,
173

Petterssen, Sverre,
71

Phillips, Norman,
74

Poindexter, John,
237
–38

Point-of-care tests (POCTs),
58
–59

Policing, predictive.
See
Predictive policing

Political demonstrations, prediction of,
196
–98

Polker players, visual cues,
169
–70

Post-traumatic stress disorder (PTSD),
180

PreCheck,
207
,
210

Prediction

Bayes theorem,
23
–25

and big data,
xiii
–xiv,
181
–82

brain, systems modeled on,
227
–36

of consumer behavior,
120
–21

of crime, predictive policing,
183
–201

of earthquakes,
2
–4

of future illness,
45
–46

human patterns, predictability of,
28
–29

individual location predictability,
25
–30

influenza-related.
See
Flu detection

intelligence activities for,
196
–200

matchmaking/dating,
152
–76

of movie box-office success,
90
–99

and neural processes,
227
–36

of political demonstrations,
196
–98

recommendation engines,
87
–89,
97
–99

of workplace accidents,
176
–77

Predictive policing,
183
–201

abuses related to,
187
,
195
–96

broken-windows theory,
184

in China,
187

closed-circuit TV (CCTV),
194
–95

connection tracking system,
218
–21

drug dealing, vulnerable neighborhoods,
183
–86

evidence, crowd-sourcing,
213
–17

intelligence activities,
196
–200

mapping/geolocation,
185
–86,
191

meaning of,
186

Memphis example,
188
–92,
222

mobile applications,
193
–94

neighborhood economic data for,
189
–92

neighborhood watch network,
213
–17

with neural networks,
185
–86

New York City example,
187
,
194

Project Exile,
187
–88

rule-induction algorithms in,
190
–91

ShotSpotter,
194

and victimology data,
223

versus zero-tolerance policies,
187
,
195

PRISM system,
210

Privacy issues

data leakage,
20
–21

employee monitoring,
177

and geo-social apps,
19
–22

government collected data,
220
–21

health data sharing, pros/cons,
46
–48

informational determinism concept,
241

matchmaking sites,
155
–56

neighborhood watch network,
215
–17

public knowledge, importance of,
212
–13,
221
–22,
238
–42

smartphone data, limiting,
29

Probability.
See also
Prediction

Bayes theorem,
23
–25

Procter & Gamble, radio frequency identification (RDIF) tag use,
115
–16

Product sales, drivers of,
106

Project Exile,
187
–88

ProMED-mail,
57

PubMatic,
156

Pythagoras,
49
–50

Q Sensor,
44

Quantified Self (QS),
32
–45.
See also
Self-tracking

elements of,
32
–33

Quantified Self Toronto,
33
–34

Quarantine, origin of term,
49
–50

Radio frequency identification (RDIF)

components of,
7

customer behavior tracking with,
115
–16

data trail, creating with,
xv

scope of use of,
6
–7

Ramakrishnan, Naren,
196
–98

Rebello, Sanjay,
138

Recommendations, Netflix,
87
–89,
97
–99

Reinstein, John,
204

Relationships

health, measurement device,
174
–75

longevity, factors in,
161
,
179
–81

marital communication,
178
–79

matchmaking.
See
Matchmaking/dating

stress test of,
179
–81

Rewards programs

airlines,
110

gambling casinos,
109
–13

grocery stores,
117
–18

Romney, Mitt,
170
–71

Rosin, Hanna,
189

Rudder, Christian,
156
,
158

Rudin, Cynthia,
14

Rule-induction algorithms,
190
–91

RunKeeper,
32

Saatchi & Saatchi,
103
–9,
128

Sadilek, Adam,
25
–29,
65
–66

Saffron Technology,
236
–38

Salathe, Marcel,
59
–60

Sam's Club,
118

Sandia National Laboratories,
148

Satellite Sentinel Project (SSP),
199

Scientific method,
78

Scott, A. O.,
96

Search engines, Wolfram Alpha,
39
–40

Seismography, earthquake prediction,
2
–4

Self-tracking,
32
–45

biophysical tracking,
31
–33,
38
–39,
44
–45

devices to compile data,
42
–45

historical view,
34
–37

of inside-view,
37
,
40
–42

for lifestyle management,
43

personal conversations,
174
–75

for self-improvement,
33
–37,
40
–41

Semi-Automated Business Research Environment (SABRE),
110

Senior, Carl,
125

Sensors

to detect ammonia/explosives,
8

for eavesdropping,
8

environmental disaster information,
10
–14

radio frequency identification (RDIF),
6
–7

Sensory data, elements of,
xv
–xvi

Sentient City Survival Kit,
217

Serendipity,
163
–65

Serotonin-based personality,
172
–73

Shaker, Steven,
231

Shepard, Mark,
217

Shook, Robert,
111

ShotSpotter,
194

Silver, Nate,
4

Silverman, Lauren,
166

Singer, Natasha,
121

Singles in America survey,
173
–74

Slashdot,
160

Smartphones

advertisers, connecting to users,
119
–20

geo-social apps,
19
–22,
165
–66

as Internet of Things driver,
16

location data, limiting on,
29

location predictability based on,
25
–30

location-tracking of,
17
–20

neighborhood watch network,
213
–17

as shopping buddy,
117

Smith, Tracy,
31

Snowden, Edward,
209
,
210

Social networks.
See also
individual social media by name

connection tracking system,
219
–20

geo-social apps,
19
–22,
165
–66

online, and intelligence information,
198

Walmart, use of data,
126
–27

weak versus strong ties,
123

Sociometer, honest signals,
167
–72,
174

Socrates,
133

Sonar app,
19

Spacey, Kevin,
89

Speech pattern, mood prediction on,
44

Spencer, Roy,
68
–70

Stagg, James,
71

Status theory, and matchmaking services,
157
–58,
160
–61,
167

Stella, Frank,
103

Stereotype threat,
134
–36

Strauss, Lewis,
72

Stress

reactions, and health,
41
–42

test, of relationships,
179
–81

Supramap,
55
–57

Takafuji, Takeya,
162
–63

Target, big data used by,
xiv

Telemetry,
xiv
–xvi

Amazon reader-behavior analysis,
99
–100

meaning of,
xiv
–xv

Netflix recommendation engine,
87
–89

power and scope of,
xv
–xvi

Television, optimized TV,
89

Terrorism prevention.
See
Airport security; Government surveillance; Intelligence activities

Terrorist Identities Datamart Environment (TIDE),
220

Testosterone-based personality,
173

Tether, Anthony,
238

Texas Virtual Border Watch,
214

TexTrace,
7

Thampi, Arun,
21

Thiel, Peter,
218

Thrun, Sebastian,
138
,
148
–49

Tictrac,
42
–43,
127

Tinder,
166

Topol, Eric,
58

Total Information Awareness (TIA),
237
–38

Total Weather Insurance (TWI),
84
–85

Transportation Security Administration (TSA),
202
–7,
210

Traumatic events, as relationship stress test,
179
–81

Tupes, Ernest,
173

Turow, Joseph,
105
–6

Twitter

Ailment Topic Aspect Model (ATAM) study,
61
–67

cigarette smoking study,
108
–9

geo-social apps,
19
–22

Ubiquitous computing

as Internet of Things,
6
–17

meaning of,
6
,
238

Udacity,
138
,
149

Unconscious mind, honest signals,
167
–72

U.S. Census,
119

Vedic astrology, matchmaking in,
152
–53,
182

Verizon, advertisers, connecting to users,
119
–20

Verleysen, Michel,
18

Victimology,
223

Virus sequencing.
See
Flu detection

Viser, Lisa M.,
44

Visualization,
233
–34

Von Neumann, John,
72
–74

Wagner, Wolfgang,
68
–70

Walmart

average customer, profile of,
118