sensors, data generated from, 10–14
Environmental Systems Resources Institute (Esri), 17, 118–19, 191–92, 222
Epidemiology, flu detection, 50–67
Epinions.com, 159–60
Erfolg, 163
Estrogen-based personality, 173
e22 Alloy, 177
eValues, 117–18
Explosives, sensors to detect, 8
E-ZPass, 7
FAA Modernization and Reform Act (2012), 195
Facebook
Data Science Team, 121–24, 159–60
as dating site, 159–60
geo-social apps, 19–22
Home, 126
individual rank, factors in, 160
Local Search, 126
Offers, 125–26
social influence in social advertising study, 124–25
sponsored stories, impact of liking, 124–25
user sharing study, 121–23
weak versus strong ties, influence of, 123–24
Far Out model, 27–28
FBI, Domestic Communications Assistance Center (DCAC), 212
Finkel, Eli J., 157, 160–61
Fisher, Helen, 172–74
Fisher Temperament Inventory (FTI), 173
Fitbit, 32, 43
Five-factor personality model, 173
Fleming, James, 71
Flip education model, 137–38
Flu detection, 50–67
Ailment Topic Aspect Model (ATAM) study, 61–67
CDC method, 61–62
databases of viruses, 54, 57
flu triangle calculation, 59–61
future scenario, 50–53, 58–59
point-of-care tests (POCTs), 58–59
political roadblocks, 57
Supramap, 55–56
transmission in social groups, studies, 59–66
virus sequencing procedure, 54–55
Flu prevention, ineffectiveness of, 53–54
Food and Agricultural Organization (FAO), 200
Foreign Intelligence Surveillance Act (FISA), 210
Foreman, Carl, 92
Fort Hood, Texas shooting, 208–9
Fourier analysis, 100
Foursquare, 19, 181
Fowler, James, 60
Franklin, Benjamin, 34–35
Freedman, Matthew, 192
Free Future blog, 30
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, 7
6
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
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
The Naked Future Page 31