The Naked Future (35 page)

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Authors: Patrick Tucker

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Friedberg, David,
80
–86

Friendster,
175

Fukushima Daiichi nuclear plant,
9
–12,
239

deception of public about,
9
–10

public data about,
10
–12

Future

futurist conceptions of,
5
–6,
37
–38

naked.
See
Naked future

predictive technologies for.
See
Prediction

as product of human brain,
232
–36

Gakhal, Baldeesh,
125

Gambling casinos, customer loyalty programs,
109
–13

Gates Foundation,
58

Gatto, John Taylor,
143

GenBank,
54

Geographic information systems.
See also
Mapping

dating/matchmaking devices,
162
–67

emergency response systems,
16
–17

geo-social apps,
19
–22,
165
–66

location predictability based on,
25
–30

smartphone tracking capabilities,
17
–20

ZIP code, use for customer info,
118
–19

Geopolitical event prediction.
See
Intelligence activities

Geostationary Operational Environmental Satellite-R Series (GOES-R),
79

Getz, Kenneth A.,
47

Ginger.io,
175

Gingrich, Newt,
77

Global Infectious Diseases and Epidemiology Network (GIDEON),
57

Global Initiative on Sharing All Influenza Data (GISAID),
54

Global warming,
69
–70,
74
–77

Godwin, Larry,
189

Gold, Josh,
177

Google

AdSense,
105

AdWords,
81

Double Click,
156

Flu Trends,
62

geo-social apps,
19
–22

Glass,
165

Hangouts,
216

Now,
xvii

Government surveillance.
See also
Airport security; Insider threats; Intelligence activities

connection tracking,
219
–20

federal surveillance strategy,
212

prediction market,
237
–38

terrorists, database of,
220

Gowanus Canal,
12
–14

Grandinetti, Russ,
100

Granovetter, Mark,
123

Grant, Rachel,
3
–4

Greenfeld, Karl Taro,
109

GRiDPad,
226

Grindr,
19
,
165

Grocery store analytics,
114
–21

club card customer tracking,
117
–18

personalizing items for customers,
116
–21

and product placement,
114
–15

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

smartphone as shopping buddy,
117

and store layout,
114

Grok,
226
–32

Grossman, Terry,
38

Grove, Andrew,
225

Gryc, Wojciech,
113
–16,
127

Guardians of Health,
49
–50

Guardian Watch,
15
–16,
213
–16

Guha, Ramanathan V.,
159

Haque, Usman,
11

Harrah's Total Gold Program,
109
–13

Hasan, Nidal,
208
–9

Hawkins, Jeff,
225
–30,
234

Health and wellness.
See also
Medical/health care

biophysical tracking,
31
–33,
38
–39

and reactions to stress,
41
–42

Heider, Fritz,
158

Heinlein, Robert A.,
227

Hidalgo, César A.,
18

Hierarchical Association Rule Model (HARM),
45
–46

Hill, Reuben,
179
–80

Hilt, James,
100

Hitchcock, Alfred,
100

Hofmann, Paul,
236
–38

Hole-in-the-wall experiment,
144
–48

Honest signals, sociometric data on,
167
–72,
174

Houri, Cyril,
18

House of Cards
,
88
–89,
98

Howe, Scott,
121

Huettel, Scott A.,
235

Hui, Sam K.,
94

Humphreys, Todd,
195

Hunt, Gus,
239
–40

Hussein, Saddam,
219

i2,
219

IAC/InterActiveCorp.,
156

Informational determinism,
241

Inkiru,
126

In-Q-Tel,
218

Insider threats,
208
–12

insiders, defined,
210

screening for,
208
–12

World of Warcraft
study,
211
–12

Insider Threat Task Force,
209

Instagram, geo-social apps,
19
–22

Instant Savings,
118

Intelligence activities,
196
–200

Arab Spring indicators,
200

bin Laden, locating,
199
–200

chatter, analysis of,
198

connection tracking system,
218
–21

disaster forensics,
208
–9

Hussein, Saddam, locating,
219

insider threat, screening for,
208
–12

military invasion, prediction of,
199

political demonstrations, prediction of,
196
–98

Satellite Sentinel Project (SSP),
199

sites monitored,
198

social network data, use of,
198

Intelligence Advanced Research Projects Activity (IARPA),
197
–98

Intergovernmental Panel on Climate Change (IPCC),
70
,
76

Internet

big data, search for term,
xiii

use to predict future,
xiii

Internet of Things,
6
–17

and corporate profits,
8

emergency response systems,
15
–17

meaning of,
6

and proactive citizenry,
10
–11,
14
–15

sensors, use of,
6
–8,
10
–15

and smartphones,
16

Ion Proton,
54

Ishino, Seigo,
9
–11

Iwatani, Yukari,
162

Jacobson, Sheldon H.,
205

Janies, Daniel,
55
–56,
59

Janikowski, Richard,
189
–90,
222

Japan, myth about earthquakes,
3
–4

Japan earthquake (2011)

Earthquake Early Warning (EEW) system,
2
–4,
9

Fukushima Daiichi meltdown,
9
–11

Jay-Z,
107

Jerk-O-Meter,
174
–75

Jones, Gordon,
15
–16,
213
–16

Kahneman, Daniel,
36
–37,
42

Kasyjanski, Carol,
7

Kelling, George L.,
184

Kelly, Kevin,
32

Kemp, Charlotte,
163
,
164
,
166

Khaliq, Siraj,
81
–82

King, Mike,
191
–93,
222
–23

Kokjohn, Tyler J.,
57

Kosmix,
126

Krumm, John,
27
–28

Kurzweil, Ray,
37
–39,
44
,
96

Kutcher, Ashton,
20

Laibowitz, Mat,
163

Larmy, Christopher,
176
–77

Latinos, stereotype threat and learning,
134
–36

Learning.
See also
Education

and memory,
148
,
234

and prediction,
234
–36

resistance to,
235

and stereotype threat,
134
–36

Leetaru, Kalev,
199
–200

Leonard, Andrew,
89

Leskovec, Jure,
159
–60

Lie detectors, for airport screening,
202
–5

Lipton, Eric,
204

Lonsdale, Joe,
218

Lotame,
156

Lovegety,
162
–63,
172

Loveman, Gary William,
109
–13

McCarthy, Gregory,
235

McCue, Colleen,
187

Mack, Peter B.,
235

Madan, Anmol,
171
,
174
–75

Manning, Bradley,
209

Mapping.
See also
Geographic information systems

flu viruses,
55
–56

potential political demonstrations,
196
–98

for predictive policing,
185
–86,
191

MapReduce,
83

Marketing.
See also
Advertising

consumer behavior prediction product,
120
–21

customers, categories of,
119

and data resellers,
156

grocery store analytics for,
114
–21

one-to-one at scale,
116

Martin, Trayvon,
215

Massively open online course (MOOC),
133
,
148
–50

Match.com,
156
,
166
,
172
–74

Matchmaking/dating,
152
–76

balance theory applied to,
158
–61

and Facebook,
159
–60

honest signals method,
171
–72

ineffectiveness of,
157
–58,
166

Match.com,
156
,
166
,
172
–74

matching systems, methods of,
154
–55,
161
,
161
–62,
164

mobile devices for,
162
–67

OKCupid,
154
–57,
165

paid versus free services,
156
,
161

personality factors approach,
172
–74

privacy issues,
155
–56

real life event hosting,
166
–67

Singles in America survey,
173
–74

status theory applied to,
157
–58,
160
–61,
167

in Vedic astrology,
152
–53,
182

Matzen, Laura,
148

Matzke, Brett,
176
–77

Mauboussin, Michael J.,
37

Mechanical Turk,
64

Medical/health care

Bluetooth-enabled pacemakers,
7

flu detection,
50
–67

future illness prediction,
45
–46

health data sharing, pros/cons,
46
–48

Memory

and learning,
148
,
234

machine, evolution of,
231
–32

and prediction,
228
–29,
232
–36

Memphis, Tennessee, predictive policing,
188
–92,
222

Metadata, meaning of,
xv
–xvi

Mikuriya, Kaori,
162

Military security, eavesdropping, sensors for,
8

Mill, John Stuart,
143
–44

MIT Human Dynamics Lab,
167

MIT Media Lab,
141
–44,
150
,
163

Mitra, Sugata,
144
–47

Mobile devices.
See also
Smartphones

first tablet PC,
226

for matchmaking/dating,
162
–67

for predictive policing,
193
–94

sales versus stationary,
226

Monaco, James,
91

Monsanto,
85
–86

Montjoye, Yves-Alexandre de,
18

Moore, Gordon,
225

Moore's law,
225

Morin, Dave,
20

Movies

box-office success prediction,
90
–99

Netflix recommendation engine,
87
–89,
97
–99

shot length based on human attention,
100
–101

Mui, Phil,
120

Munley, Kimberly D.,
208

Naked future

and apps,
xvi
–xvii

and big data,
xiii
–xiv

elements of,
xii
–xiii,
xvii

and ubiquitous computing,
6

Namazu
(Earth Shaker),
3

National Climatic Data Center (NCDC),
81

National Counterterrorism Center (NCTC),
220
–21

National Oceanic and Atmospheric Administration (NOAA),
81
,
86

National Polar-orbiting Operational Environmental Satellite System (NPOESS),
79

National Security Agency (NSA)

media reports on,
xiv

private company compliance with,
210

surveillance program, public opinion of,
205
–6

Navizon,
17
–18

Negroponte, Nicholas,
141
–44,
146
–47,
164

Neighborhood watch network,
213
–17

limitations of,
214
–15

Neocortex,
227
–28

Netflix, recommendation engine,
87
–89,
97
–99

Neural networks, for crime prediction,
185
–86

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