Index

accounts payable, 147–148

action, in decision-making, 86, 124, 126, 127

Ada Support, 3, 102, 198

Adobe, 214

adoption, timing of, 17, 182–184, 206

adversarial machine learning, 211–212

advertising, 199–200

biases in, 221–224

effectiveness of, 224–225

gender discrimination in, 222–224

agriculture

hybrid corn, 182–184

monoculture in, 227

AI. See artificial intelligence (AI)

AI canvas, 18, 158–164

AI-first strategy, 203–206, 218

AI Insight, 14

AI moment, 7–9

AI neuroscience, 223–224

Air Canada, 40–41

Air France Flight 447, 216–217

airline industry, 192–195, 216, 241–242

airline pilots, 208–209, 216–217

airport lounges, 117–119

AI tools. See tools, AI

AI winter, 46

Alexa, 1, 2–3

Alibaba, 184, 244

Alipay, 245

AliveCor, 58

Allied bombing raids, 112–113

Alphabet, 188

AlphaGo, 8, 211

AlphaGo Zero, 248

Amazon, 133, 134

AI asset acquisition by, 243

AI use by, 16–17, 20, 157

Alexa, 1, 2–3

business model, 16–17, 180–181

Echo, 157, 246

fulfillment processes at, 167, 168, 180–181

low-stakes transactions at, 135–136, 138–140

Machine Learning, 229

Picking Challenge, 168

privacy and, 214

privacy policy, 246

reviews, 136

robots used by, 117

scale of, 241

American Express, 96

The Americans, 115

analogies, 73, 110

anticipatory shipping, 16–17, 180–181

Apple, 2

privacy and data, 213–215

Siri, 157

Apple Watch, 58, 59, 62

arithmetic, 12, 14, 165

Arrow, Ken, 36–37

artificial general intelligence (AGI), 157, 248–249

artificial intelligence (AI)

advances in, 1–2, 46

applications of, 2, 8–9

automation versus, 124

biases in, 48, 69–69, 71, 221–224

business transformation and, 191–202

cost reductions in, 7–20

decision-making and, 149

diversity in machines for, 228

early research on, 45–46

economics of, 3, 8–10, 12–13, 189

as existential threat, 247–249

labor impacts of, 195–198

learning from mistakes by, 109

machine learning as, 51–54

as magical, 8–9

as prediction technology, 2–3

research and development of, 243–244

risks from, 19, 221–232

role of data in, 57

societal impacts of, 19, 235–250

strategy and, 16–17, 18–19, 20, 179–190

superintelligence, 247–249

trade-offs, 4, 5

artificial light, 11

artistic achievement, 129

Asimov, Isaac, 127

AT&T, 241

athletic competition, 129

Atomwise, 158–162

atrial fibrillation, 58–60

automatic teller machines (ATMs), 195–196

automation

AI versus, 124

camera, 126–127

in decision-making, 123–131

of flying, 216–217

grasping problem and, 168–169

job loss from, 236–238

job redesign and, 165–175

legal requirements for humans with, 127–129

in mining, 124–126

missing links in, 166–169

stakes of, 133–144

when not to use, 129–130

when to use, 126–127

workflow and, 147–155

automobile industry, 168–169, 188–189, 193–194, 195. See also autonomous vehicles

autonomous delivery systems, 117

autonomous vehicles, 90

human deskilling and, 217

learning by, 213

legal requirements on, 128

for mail delivery, 115, 116

in mining, 123–126

passengers and, 107

prediction and, 14–15, 100–101

preferences and, 101

rail systems, 116

reward function engineering, 104

school bus drivers and, 173

security risks, 228

stakes of, 143

tolerance for error in, 143, 209–211

training, 209–211

trolley problem and, 128

value capture and, 188–189

Autopilot AI, 8, 213

average, conditional, 47

Babbage, Charles, 12, 78

back propagation, 52

Baidu, 184, 188, 245

bail-granting decisions, 70–71

bank tellers, 195–196

baseball players, selection of, 69, 185

Bayesian estimation, 13

Beane, Billy, 69, 185

Beijing Automotive Group, 188–189

Bernoulli numbers, 12

beta testing, 208, 210–211, 216

Bhalla, Ajay, 25

biases, 19

in advertising, 221–222

in prediction, 48, 68–69, 71

big companies, control by, 241–243

binding affinity, 159–162

Bing, 229, 242

biopsies, 120–121, 172

BlackBerry, 153–154

The Black Swan (Taleb), 73–74

Blake, Thomas, 225

blockchain, 246

Blum, Andrew, 38

bookkeeping, 165–166

Bostrom, Nick, 247, 248

boundary shifting, 181, 189–192

data ownership and, 198–200

what to leave in/leave out, 192–194

brainpower, 239

Breakout, 207

breast cancer, 78–79

Bresnahan, Tim, 12

Bricklin, Dan, 165, 187, 188

A Brief History of Time (Hawking), 236–237

Brynjolfsson, Erik, 102–103

business models, 11, 16–17, 96, 180–181

business strategy. See strategy

business transformation, 191–202

Camelyon Grand Challenge, 78

camera automation, 126–127

cancer diagnosis, 78–79

capital, 194–195, 197

Capital in the Twenty-First Century (Piketty), 239

Capital One, 111

carbon tax, 129

Cardiio, 58

Cardiogram, 58–63

causality, 76–77

reverse, 75

CDL. See Creative Destruction Lab (CDL)

certainty, 32

Challenger, 167

Champy, James, 147–148

chatbots, 133

Chavez, R. Martin, 149

chess, 76

chief executive officer (CEO), AI adoption and, 180

China

AI research in, 8, 244–245

autonomous driving in, 188–189

language translation in, 26

Chiou, Lesley, 242

Christensen, Clay, 205

churn, 46–50

classification, 13

climate change, 129

cloud

learning in the, 212–213

untethering from, 228

clustering, 13

codifiable judgment, 102–103

coffeehouses, 31

cognitive costs, of judgment, 99–100

collaboration, 188–189

collaboration, human/machine

bank tellers/ATMs and, 195–196

bookkeeping and, 165–166

job redesign for, 165–175

in medical imaging, 170–172

collateralized debt obligations (CDOs), 51

college recruitment, 151–153, 162–163

competition, 206, 238–239, 241, 247, 248

complements, 15. See also data; judgment

complex environments, 52

complexity, 115–122

if-then logic and, 116–121

computer revolution, 165

computers, 12, 13

impact on jobs, 165–166

programming, 54

reengineering use of, 147–149

as tools, 157

conditional average, 47

consumer data, 109–110, 213–215, 245–247

consumer demand, 180–182

consumer interactions, stakes of, 133–135

consumer preferences, data on, 200–201

consumer satisfaction, 205

Cook, Tim, 213–214

cookies, 199

corn, hybrid, 182–184, 205

correlations, 97, 224, 225

unanticipated, 51

cortex, 53

cost

versus control, 7–17, 193

effects of reduced AI, 9–11, 12–13

of foundational inputs, 11–13

of prediction, 13–15

strategy and, 16–17

counterfactuals, 75–76, 110–111

country differences, 243–247

creative destruction, 241

Creative Destruction Lab (CDL), 2, 8, 158

credit card fraud prevention, 24–25, 27, 96–99, 103

creditworthiness, 27, 79–80, 96

crime rates, 70–71

Croesus, King of Lydia, 23

crystal balls, 24

C-suite, AI use by, 179–190

customer churn, 46–50

customer data, 109–110, 213–215, 245–247

customer service, 3

Daimler, 188

Dartmouth College, 45

data, 13, 18, 57–65

in churn prediction, 50

consumer, 109–110, 213–215, 245–247

cost of acquiring, 58

crashes, 226

decisions about, 61–63

economies of scale and, 63–64

feedback, 57, 60, 86, 187, 230–231

humans and, 109–110

input, 57, 58, 61, 86, 187, 226–228

machine learning and, 49–50, 59–61, 210–211

ownership and control of, 198–201

for prediction, 57–59, 109–110, 198–201, 226

privacy, 213–215, 245–246

real world, 210

roles of, in AI, 57

search engines and, 242

security risks, 225–231

selling, 200–201

as strategic asset, 187

training, 57, 60, 61–62, 86, 187, 210, 228–230

unique, 200

value of, 57

data manipulation, 225–227

D-Day invasion, 35, 36

decision-making, 85–94

AI and, 149

AI canvas for, 18, 158–164

anatomy of, 86–88

bail granting, 70–71

big and small, 85

certainty in, 32

decomposing, 157–164

elements of, 158, 159

experiments and, 111

fully automated, 123–131

judgment in, 86–88, 90–93, 95–106, 108, 160, 197–198

modeling and, 111–113

prediction and, 2, 4, 15, 18, 29

satisficing in, 119–120, 121

stakes and, 138–140

subjective goals and, 197

timing of, 39–40

trade-offs, 4, 5

uncertainty in, 3, 32–35, 86, 90–93

See also uncertainty

decision trees, 13, 92–93

Deep Genomics, 3

deep learning, 7, 13

back propagation in, 52

flexibility in, 50

imitation risks, 229–230

language translation and, 25–27

DeepMind, 7–8, 207, 211, 248

Deep Thinking (Kasparov), 76

delivery robots, 117

delivery times, 181–182

Delphi oracle, 23

demand management, 180–182

dependent variables, 59

DePodesta, Paul, 185

deskilling, of humans, 216–218

deterministic programming, 54

digital cameras, 14

Discover Weekly, 141–142

discrimination, 221–224

disparate impact, 222–223

disruptive innovation, 179, 205–206

diversity, of prediction machines, 228

division of labor, 67–82

human/machine collaboration, 78–80

human weaknesses in prediction and, 68–71

machine weaknesses in prediction and, 71–78

prediction by exception and, 80–81

“dog fooding,” 208

drone weapons, 128

Dropbox, 214

drug companies, 158–162

Dubé, J. P., 105

D-Wave, 212

earthquakes, 73, 110

eBay, 225

economies of scale, 63–64, 241–243

Edelman, Ben, 222

education, 240

emergency braking, 124

employment. See jobs

Enlitic, 170

errors

consequences of, 143

tolerance for, 208–211

ethical dilemmas, 128

Etzioni, Oren, 246

European Union (EU), 242, 245

exceptions, 80–81

executive leadership, AI and, 179–190

Expedia, 133–134

experience, 87, 208

by humans, 216–218

scarcity of, 215–216, 217

experiments, 111

experts, predictions by, 68–69

externalities, 128–129

Facebook, 110, 137–140

advertising, 222–223

dominance of social media, 241

facial recognition, 73, 109, 214–215, 245

false negatives, 136, 138, 139, 143

false positives, 136, 138, 139, 143

Fanning, Shawn, 74

Farmers’ Almanac, 37

Federal Trade Commission, 221

feedback data, 57, 60, 86, 187

risks, 230–231

films, about AI, 8

financial crisis (2008), 50–51

Forbes, Silke, 192

Ford, 147–148, 188

Frankston, Bob, 165, 188

fraud detection, 24–25, 27, 96–99, 103

free-riding effect, 246

free trade, 239

Frey, Carl, 173

fulfillment industry, 167–169

function engineering, 238

Furman, Jason, 239

gambling, 31–32

Gates, Bill, 187, 236, 239, 247

gender discrimination, 222–224

general adversarial networks, 13

Gildert, Suzanne, 169

Go (game), 8

Goizueta, Robert, 57

Goldin, Claudia, 240

Goldman Sachs, 149

goodness of fit, 47

Google, 133, 134

advertising, 200, 221–222, 224–225

AI development by, 184

AI-first strategy, 203–204, 205, 218

AI tool development by, 150

algorithm of, 77

anti-spam team, 229

data of, 57

DeepMind, 7–8

dominance of, 242–243

Gmail, 209, 211

privacy and, 214–215

search engine, 10, 19, 64, 157, 229, 241

Translate, 25–26, 27

Waymo and, 107

Waze and, 100–101

Google Assistant, 118

government expenditure, on AI, 243–244

Grammarly, 108

grasping problem, 168–169

Griliches, Zvi, 183

Grove, Andy, 179

Hacking, Ian, 54

Hammer, Michael, 147–148

Harford, Tim, 217

Hawking, Stephen, 8, 236–237

Hawkins, Jeff, 53

health insurance, 27–28

heart rate data, 58–63

Heifets, Abraham, 159, 160

heuristics, 68, 69

Heyo, Xu, 188–189

Hinton, Geoffrey, 169

hiring decisions, 71

ZipRecruiter and, 104–106, 111–112

Hoffman, Mitchell, 71

homogeneity, 227–228

Houston Astros, 185

human resources (HR) management, 197

human senses, 109

Hume, Kathryn, 14

humor, 129

hybrid corn, 182–184, 205

IBM, 170

identity verification, 227

iFlytek, 26

if-then logic, 14, 103, 116–121

image classification, 28–29

ImageNet, 7

ImageNet Challenge, 28–29

imitation, 229–230

income, 238–240

incumbents, 206

independent variables, 59

inequality, 238–240

information, missing, 169–170

initial public offerings (IPOs), 10, 149

innovation, 193

disruptive, 179, 205–206

investment in, 243–244

leadership, 243–244

innovator’s dilemma, 206

input data, 57, 58, 61, 86, 187

security risks, 226–228

Instagram, 77

insurance, 32–35, 40, 41, 135

Intel, 15, 241

intelligence

prediction as, 29, 45–55

See also artificial intelligence (AI)

internet, 2, 9–10

intuition, 87

inventory management, 28, 181–182

iPhone

introduction of, 179

QWERTY keyboard, 153–154

James, Bill, 69

Jelinek, Frederick, 120

jobs

AI’s impact on, 195–198, 236–238, 239

augmentation of, 166

bias in ads for, 222–224

division of labor in, 67–82

impact of computers on, 165–166

judgment and, 197–198

redesigning, 165–175

See also workflows

Jobs, Steve, 12, 157, 179

jokes, 129

judgment, 18

cognitive costs of, 99–100

in credit card fraud, 96–99, 103

in decision-making, 86–88, 90–93, 95–106, 108, 160, 197–198

hard-coding, 102–103, 104

by humans, 124, 197–198

jobs related to, 238

predicting, 107–114, 124

prediction and, 186, 197–198

preferences and, 100–102, 108–109

in reward function engineering, 103–106

stakes and, 142–143

strategic choice and, 186

uncertainty and, 103

value of, 95–106

Juhong, Chen, 188

Jurvetson, Steve, 8–9

Kahn, Lisa, 71

Kahneman, Daniel, 68, 69, 235–236

Kapor, Mitch, 187

Kasparov, Garry, 76

Katz, Lawrence, 240

Kie, Ke, 8

Kindred, 169

Kiva, 167

“The Knowledge,” 88–90

known knowns, 71, 72, 78

known unknowns, 71, 72–73, 78, 110

Kurzweil, Ray, 247

labor, 195–198, 239–240

division of, 67–82

See also jobs

“Lady Lovelace’s Objection,” 13

Lambrecht, Anja, 222, 224

language translation, 25–27, 119–120

learning

in the cloud versus on the ground, 212–213, 228

experience and, 215–218

pathways to, 206–208

privacy and data for, 213–215

reinforcement learning, 13, 207–208

by simulation, 211–212

strategy for, 203–219

supervised, 207

-by-using, 206–207, 208, 216

when to deploy, 208–211

Lederman, Mara, 192

Lee, Kai-Fu, 245

legal issues, with automation, 127–129

Lewis, Michael, 69

Li, Danielle, 71

liability, 129, 222–224

lighting, cost of, 11

Lloyd, Edward, 31

Lloyd’s List, 31

Lloyd’s of London, 33

loan decisions, 79–80

Lola, 108–109

London cab drivers, 88–90

loss functions, 142–143

Lovelace, Ada, 12, 13

loyalty cards, 110

Lu, Qi, 245

Lyft, 100

Lytvyn, Max, 108

machine learning, 2

advances in, 46, 51

adversarial, 211–212

as artificial intelligence, 51–54

data use by, 49–50, 59–61, 210–211

experiments in, 49

flexibility in, 50

one-shot, 73

pattern recognition and, 169–170

quantum, 212

regression compared with, 46–50

statistics, prediction, and, 51–54

types of, 207–208

undermining, by bad actors, 230–231

Mailmobile, 115, 117

mail robot, 115–116, 117

mail robots, 118, 119

management by exception, 81

Mastercard, 96

Mazda, 148

MBA program recruitment, 151–153, 162–163

McAfee, Andrew, 102–103

medical diagnosis, 120–121, 169–172, 191–192, 201

medical imaging, 169–172

medicine, 3

Mejdal, Sig, 185

Meta, 2, 184, 200, 214

Microsoft, 9, 200, 204, 205, 218, 241, 245

Bing search engine, 229, 242

Tay chatbot, 230–231

mining, automation in, 124–126

Misra, Sanjog, 105

mistakes, learning from, 109

mobile-first strategy, 204

Mobileye, 15

modeling, 111–113

Moneyball (Lewis), 69, 185

monoculture, 227

monopolies, 241

multivariate regression, 47

music, 12

music industry, 74

music streaming, 140–142

Musk, Elon, 235, 236, 247

Mutual Benefit Life, 148–149

Napster, 74

NASA, 2

national income, 239

National Science and Technology Council (NSTC), 248–249

navigation apps, 89–90, 100–101, 215

Neilley, Peter, 39

neocortex, 53

Netflix, 133

Netscape, 9–10

neural networks, 13

New Economy, 10

New York City Fire Department, 223

Ng, Andrew, 240

Nordhaus, William, 11

Norvig, Peter, 204

Nosko, Chris, 225

Novak, Sharon, 193

Nymi, 227

Oakland A’s, 69, 185

Obama administration, 243–244

object identification, 28–29, 52, 169–170

object recognition, 7

omitted variables, 75

one-shot learning, 73

On Intelligence (Hawkins), 53

Open AI, 2, 236

operational performance, 205

option value, 40

order fulfillment, 167–169

organic light-emitting diode (OLED), 212

Osborne, Michael, 173

OTI Lumionics, 212

Otto, 181–182

Oura, 58

outcomes, 86, 87, 88

outsourcing, 193–194, 195

Page, Larry, 203

Paravisini, Daniel, 79

pattern recognition, 169–170

Pavlov, Ivan, 207

payoffs, 86, 87–88, 92–93, 99–100, 160

Pell, Barney, 2

performance evaluation, 197

personalization, 140–142, 245–246

pharmaceutical industry, 158–162

photography, 14

physical stores, 181

Piketty, Thomas, 239

Pilbara, Australia, 124–125

pilots, 208–209, 216–217

power calculations, 62

prediction, 2–3, 23–43, 46

about, 13

about preferences, 109–110

about the present, 23–24

accuracy of, 142–143

advances in, 27–29

AI and, 13

applications of, 27–28

biases in, 48, 68–69, 71

business strategy and, 3–4

churn, 46–50

in the cloud versus on the ground, 228

consequences of cheap, 29, 53–54

credit card fraud prevention and, 24–25

data for, 57–59, 86, 109–110, 198–201, 226

decision-making and, 2, 4, 15, 18

definition of, 24

by exception, 80–81

of human judgment, 107–114

human/machine collaboration for, 78–80

human weaknesses in, 68–71

if-then logic and, 116–121

imperfect, 134

improvements in accuracy of, 27–29

insurance coverage and, 33–35

as intelligence, 29, 45–55

judgment and, 186, 197–198

language translation and, 25–27

with little data, 110–113

machine weaknesses in, 71–78

made cheap, 13–15

reliability of, 36–38

for risk mitigation, 35–36, 40–42

selling, 200–201

stakes of, 133–144

statistics and, 68

techniques, 13

on-time, 38–40

trade-offs, 20

uncertainty and, 3

uses of, 14–15

weather, 35–39

preferences, 109–110

prejudice, 221–222

presidential elections, 72–73, 110

prices, 9–12. See also cost

privacy

data, 4, 63, 110, 213–215, 245–246

search engine, 64

probabilities, 171

probabilistic programming, 54

processes. See workflows

productivity, 148–149

product personalization, 140–142

product variety, 140–141

Project Apollo, 188

protected classes, 223

protection, 135

protection strategy, to mitigate risk, 40–42

Puchai, Sundar, 203

Putin, Vladimir, 243

quality risks, 224–225

quantum computing, 212

QWERTY keyboard, 153–154

racial profiling, 221–222

radar, 142, 143

radiologists, 120, 169–172

rail systems, 116

randomized control trials, 111

rare events, 73, 110, 113

reason, 53

recruitment, to MBA programs, 151–153, 162–163

reengineering, 147–149

regional airlines, 192–193, 194

regression, 13, 46–50

failures of, 50–51

multivariate, 47

reinforcement learning, 13, 169, 207–208

research and development, 243–244

resources, 247–248

returns to scale, 63–64

reverse causality, 75

reverse engineering, 228–229

reward function, 91

reward function engineering, 103–106

rewards, 99, 186

Rio Olympics (2016), 126–127

Rio Tinto, 125

risk aversion, 32

risk mitigation, 32

predictions for, 35–36

risks and risk management, 19, 40–42, 221–232

insurance coverage and, 32–35

liability and, 222–224

prediction and reducing, 120–121

quality and, 224–225

security and, 225–231

Rivers, Lynn, 244

Robotlandia, 237

robots, 115–117

capabilities of, 168–169

as caregivers, 129–130

grasping problem, 168–169

in space, 127

wisdom of, 235–236

Roomba, 116

Rose, Geordie, 169

Rosen, Guy, 137–138

Rosenberg, Nathan, 206–207

Rumsfeld, Donald, 71–72

Russakovsky, Olga, 28–29

Russia, 243

sabermetrics, 185–186

Salesforce, 184, 214

satisficing, 119–120, 121

scale economies, 63–64, 241–243

Schoar, Antoinette, 79

school bus drivers, 173

Schumpeter, Joseph, 241

science-based startups, 2

search engine optimization, 77

search engines, 242

security risks, 225–231

Se-dol, Lee, 8

self-driving cars. See autonomous vehicles

sensors, 15, 58

Shevchenko, Alex, 108

Simon, Herbert, 119

simulated learning, 211–212

Singularity, 53, 247

skills

loss of, 216–218

need for additional, 240

Skynet, 53

smartphones, 58, 59, 179

Smith, Adam, 67, 78

social interactions, 129–130

society, 235–250

control of big companies and, 241–243

country advantages and, 243–247

existential threat to, 247–249

impacts of AI on, 19

inequality and, 238–240

job loss and, 236–238

Solow, Robert, 147

space exploration, 127

spammers, 77

sports

camera automation and, 126–127

sabermetrics and, 185–186

Spotify, 140–142

spreadsheets, 165–166, 187

Sputnik, 8

stakes, 133–144

of consumer interactions, 133–135

decisions and, 138–140

high versus low, 135–138

judgment reveals, 142–143

personalization and, 140–142

statistics

prediction, machine learning, and, 51–54

prediction and, 68

stereotypes, 19

Stern, Scott, 193, 244

Stigler, George, 117

strategic choice, 186

strategy, 2–4, 15

AI-first, 203–206, 218

AI’s impact on, 18–20, 179–190

business transformation and, 191–202

cheap AI and, 16–17

economics of, 189

judgment and, 186

learning, 203–219

organizational structure and, 185–186

value capture and, 187–189

strong AI, 157

Sullenberg, Chesley “Sully,” 208

superintelligence, 247–249

supervised learning, 207

Sweeney, Latanya, 221

systemwide failure, 227–228

Tadelis, Steve, 225

Taleb, Nassim Nicholas, 73–74

Taming of Chance (Hacking), 54

Tanner, Adam, 221

task analysis, job redesign and, 166–169, 173–174

taxi industry, 179

Tay chatbot, 230–231

technical support services, 102

technological change, price reductions and, 11–12

technology adoption, timing of, 17, 182–184, 206

technophiles, 7

technophobes, 7

telecommunications, 241

Tencent Holdings, 188, 244

Teradata Center, 49

Tesla, 8, 101, 123–124, 126, 128, 133–135, 157, 189, 210–211, 213

Thinking, Fast and Slow (Kahneman), 235–236

Tinder, 213

tools, AI, 18, 20

adoption of, 184

capabilities of, 157–158

decisions about using, 157–164

for deconstructing workflows, 147–155

design of, 149

development of, 184

impact on workflows of, 150–153

iPhone keyboard and, 153–154

job redesign and, 165–175

in radiology, 170–172

topological data analysis, 13

trade, 237, 238–239

trade-offs, 4, 5, 20, 193–194, 204

between human and machine experience, 217–218

satisficing and, 119–121

of when to deploy, 209

traffic apps, 118

training

on-the-job, 208, 209

new employees, 208–209

training data, 57, 60, 61–62, 86, 187, 210

risks, 228–230

translation services, 119–120

transportation, 14

trolley problem, 128

truck drivers, 173–174

true negatives, 136

true positives, 136

Tucker, Catherine, 222, 224, 242

Tunstall-Pedoe, William, 2

Turing, Alan, 13

Turing Award, 119

Turing test, 53

Tversky, Amos, 68

Twitter, 230–231

2001: A Space Odyssey, 247

Uber, 89, 100, 189, 214

umbrellas, 90–93

uncertainty, 3, 18

about weather, 192–193

business boundaries and, 192–194

control and, 193

data to reduce, 57

in decision-making, 32, 33–35, 86, 90–93

increases in, 198

judgment and, 103

prediction and, 118, 119

reducing, 180–181, 182, 189

underwriters, 33

unemployment rates, 237

United States, 243–244

University of Toronto, 2, 7

unknown knowns, 72, 74–78, 110–111, 225

unknown unknowns, 71–72, 73–74, 109, 113

Validere, 3

value capture, 187–189

value creation, 13–15

variables

dependent, 59

independent, 59

omitted, 75

Varian, Hal, 57

Vinge, Vernor, 247

Visa, 96

VisiCalc, 165–166

wages, 238–239, 240

Wald, Abraham, 112–113

Wanamaker, John, 199

War Games, 143

Watson, 170

Waymo, 107, 188

Waze, 100–101, 118, 215

weather forecasts, 35–39, 192–193

web advertising, 199–200, 221–225

WeChat, 188

Wells Fargo, 197

Windows 95, 9

wisdom, 235–236

workflows

deconstructing, 147–155

impact of AI tools on, 150–153

redesigning for automation, 147–150

World War II, 35–37, 112–113, 142

WTF problem, 141

Xanadu, 212

Yahoo, 242

Y Combinator, 236

Yeomans, Mike, 129

YouTube, 200

ZipRecruiter, 104–106, 111–112