Index
- accidents, 90
- adaptive robots, 104
- addictive technology, 142
- Affectiva, 10, 23, 138, 139–140
- Agrawal, Ajay, 73–78, 145–152
- AI. See artificial intelligence
- AI assistants, 112–113, 117–118, 142
- Aida, 117–118, 121
- Alexa, 113, 142. See also AI assistants
- algorithms
- biases in, xv, 21–22, 58
- deep learning, 13–14
- efficiency of, 57–58
- machine learning, 30–31, 33–35, 57–58, 68–69, 112–113
- performance failures, 89–95
- training, 47–48, 75, 112–113
- translation, 38–39
- Allen Institute for Artificial Intelligence (AI2), 158
- Alphabet, 59, 159
- Amazon, xv, 9, 14, 17, 19, 113, 138, 146–149, 152
- amplification, of human abilities, 116–117
- Apple, 113, 115, 132, 138
- Applied Machine Learning (AML), 29–52
- Aptonomy, 10
- Argo AI, 150
- artificial intelligence (AI)
- adoption of, 53–61
- business use of, xi–xvi, 3–28
- capabilities of, 6–11
- collaboration between humans and, 109–134
- data and, 73–78
- emotional analysis by, 137–144
- employment impact of, xv, 114
- ethical issues of, xv, 70, 115
- evolution of, 154
- expectations for, 5
- at Facebook, 29–52
- future of, 153–162
- governance time for, 57–58
- human interactions with, 56–57
- versus human performance, 9–10
- impact of, 5, 25–26
- integration time for, 55–56
- investment in, 100, 150
- limitations of, 23–24, 49–50, 70–71
- nontechnical employees and, 65–72
- performance failures, 89–95
- pilot projects, 79–88
- product development and, 36–38
- risks of, 21–23
- scaling, 58–60, 121
- strategy for, 79–88, 145–152
- system development time for, 54–55
- understanding, 68–71
- uses of, 65–66, 69–70
- varieties of, xii–xiii
- See also machine learning
- AT&T, 132
- Autodesk, 124
- AutoEmotive, 139–140
- auto manufacturing, 122–124
- automation, 84
- autonomous cars, 7, 114, 115, 154
- Baidu, 86
- Berinato, Scott, 29–52
- Beyond Verbal, 138
- Bezos, Jeff, xv
- biases, xv, 21–22, 58, 70
- big data, 83, 153
- black-box problems, 113–114
- Blue River Technology, 150
- BMW, 104
- Brain Power, 140–141
- Brynjolfsson, Erik, 3–28
- business
- adoption of AI by, 53–61
- data, 73–78
- redesigning, 119–131
- use of AI in, xi–xvi, 3–28
- See also companies
- business models, 19–20, 148
- business processes, 19, 121–131
- business strategy, 145–152
- cancer
- diagnosis, 10, 15, 19
- treatment, xv, 55, 124
- Candela, Joaquin, 29–50
- CAPTCHAs, 156
- car manufacturing, 122–124
- Carnival Corporation, 125, 130–131
- casino management, 125
- causal links, 120
- chatbots, 117–118, 139–140
- cloud computing, 17
- CLUE, 44
- cobots, 118–119, 122
- co-creation, 119–121
- coding, 11
- cognition, 6, 8–10, 23, 158
- collaborative intelligence, 109–134
- common sense, 157–158
- communication, 86
- companies
- adoption of AI by, 53–61
- data of, 73–78
- investment in AI by, 100, 150
- pilot projects for, 79–88
- See also business
- competitive advantage, 59, 100
- conversational user interfaces, 142
- Cortana, 112–113, 117, 118
- Coursera, 17
- creativity, 116–117
- credit card fraud, 123, 124, 126
- credit decisions, 9, 115
- Cruise Automation, 150
- customer service, 117–118, 125
- Danske Bank, 123, 126
- DARPA, 158
- data
- AI and, 153–162
- big, 83, 153
- privacy issues, 154–155
- protection, 114, 115, 155
- training, 75–77
- value of, 73–78
- data compliance officers, 115
- Daugherty, Paul, 109–134, 153–162
- Davenport, Chase, 153–162
- Davenport, Thomas H., xi–xvi, 53–61
- decision making, 125, 128–129
- emotional analysis and, 139–140
- by machines, 21–22
- Deep Instinct, 9
- deep learning, xiii, 13–14, 54, 81
- DeepMind, 8–9, 150
- deep neural nets, 7–8, 14, 21
- discrimination, 115
- disease prediction, 125
- Dreamcatcher, 116–117
- drones, 10
- dystopian view, 101, 107
- early adopters, 53, 58–59, 154
- economic growth
- drivers of, 3–4
- lack of, 102–103
- edge cases, 154
- Ellie, 142. See also AI assistants
- emotions, 137–144
- employees
- nontechnical, 65–72
- as partners, 106–107
- recruitment of, 124, 126–128
- skills needed by, 131–132
- technical, 66–67
- training, 106
- employment
- impact of AI on, xv, 99–110, 114
- redefining, 105
- Enam, Zayd, 18
- Enlitic, 10
- entrepreneurs, 21, 24
- equipment maintenance, 125, 128–129
- errors, 9, 22, 89–95
- ethical issues, xv, 70, 115, 154–155
- Etzioni, Oren, 158
- expertise, 156–157
- explainers, 113–114
- external partners, 82–83
- Facebook, 7, 15, 29–52, 138
- Messenger, 42, 44
- facial recognition, 154
- fast.ai, 17
- fast follower strategy, 53–61
- FBLearner Flow, 40
- Fidler, Devin, 106
- financial services, 125
- flexibility, 122–123, 124
- Ford Motor, 150
- fraud
- credit card, 123, 124, 126
- customer, 58
- detection, 123, 124, 126
- financial, 123, 124, 126
- fusion skills, 131–132
- Gans, Joshua, 73–78, 145–152
- Gaussian processes, 159–160
- General Data Protection Regulation (GDPR), 114, 115, 155
- General Electric, 125, 128–129
- General Motors, 150
- general-purpose technologies, 3–4, 25
- GGH Morowitz, 125
- Gigster, 124
- GNS Healthcare, 120
- Goldfarb, Avi, 73–78, 145–152
- Google, 17, 81, 138, 150
- Google Assistant, 142
- Google Brain, 81, 86
- Google Maps, 81
- governance, 57–58
- guest experience, 125
- health care, 125
- Hill, Colin, 120
- hiring process, 126–128
- HSBC, 123, 124
- human brains, 101–102
- human labor, 20. See also employment
- human-machine collaboration, 109–134
- humans
- as explainers, 113–114
- interactions between AI and, 56–57, 117–118
- machines assisting, 116–119
- roles of, 112–116
- as sustainers, 114–115
- as trainers, 112–113
- Hyundai, 119
- IBM, 9
- Icahn School of Medicine, 125
- ImageNet, 7, 9, 10
- image recognition, 7–9, 45–48
- Infinite Analytics, 9
- insurance claims processing, 9
- integration time, 55–56
- intelligent agents, 9
- intelligent enterprises, 100, 103, 105–107
- interaction learning, 57
- interactions, between humans and machines, 56–57, 117–118
- interactive voice responses (IVRs), 139–140
- internal combustion engine, 3–4
- interpretability, 21, 27
- investment advice, 56–57
- invisible problems, 120
- irrational decisions, 140
- jobs
- impact of AI on, xv, 99–110, 114
- loss of, 65, 101, 109
- redefining, 105
- John Deere, 150
- JPMorgan Chase, 14–15
- Karim, 142. See also AI assistants
- Kindred, 16
- Kleber, Sophie, 137–144
- Knickrehm, Mark, 99–108
- knowledge
- codifying, 11, 55
- engineering, 54
- tacit, 11–12
- Koko, 113
- labor
- augmentation, 104
- division of, 20
- human, 20
- impact of AI on, 99–108
- See also employment; jobs
- Landay, James, 7
- late adopters, 53–54, 58–61
- leadership, 24, 25, 84–85
- LeCun, Yann, 15
- life-or-death decisions, 22
- Lumidatum, 9
- Lumos, 45–48
- Machine Common Sense (MCS) program, 158
- machine learning, xii–xiii
- about, 66–67
- algorithms, 30–31, 33–35, 57–58, 68–69, 112–113
- applied, 29–52
- as change driver, 19–21
- cognition and, 8–10
- data for, 17–18
- at Facebook, 29–52
- importance of, 4
- infrastructure, 17
- limitations of, 10–11, 23–24, 70–71
- natural language, 42, 44
- nontechnical employees and, 66–72
- risks of, 21–23, 27–28
- types of, 12–16
- understanding, 11–12, 68–71
- uses of, 9, 17–21, 69–70
- See also artificial intelligence (AI)
- Mahidhar, Vikram, 53–61
- malware detection, 9
- Martinho-Truswell, Emma, 65–72
- Masthoff, Judith, 142–143
- McAfee, Andrew, 3–28
- McCarthy, John, 6
- medical images, 10
- Mercedes-Benz, 122–123, 124
- Microsoft, 16, 17, 89–90, 112–113, 158
- Minsky, Marvin, 6
- mistakes, 89–95
- money laundering, 9
- Morgan Stanley, 125
- M Suggestions, 44–45
- natural language processing, 42, 44, 48–49, 54
- neural nets, 7–8, 14, 21, 154
- Ng, Andrew, 14, 79–88
- nontechnical employees, 65–72
- occupation redesign, 19
- online educational resources, 17
- operating models, 104
- optimistic realists, 103, 108
- organizational design, 105
- Pandora, 130
- partnerships
- with employees, 106–107
- external, 82–83
- pattern recognition, 15, 57, 67–68, 123, 159–160
- PayPal, 9
- perception, 6–10, 23
- performance failures, 89–95
- personal information, 115
- personalization, 125, 130–131
- Pfizer, 59, 125
- Picasso, Pablo, 23–24
- pilot projects, 79–88
- Polanyi’s Paradox, 12, 21
- Pratt, Gil, 26
- predictions, 145–152
- Predix, 128–129
- privacy issues, 115, 154–155
- probabilities, 159–160
- problem solving, 8–10
- product design, 124
- productivity, 102–103, 110, 121
- productivity skeptics, 102–103, 108
- Project Loon, 159
- public safety, 124
- reasoning, 155–156
- recruitment, 124, 126–128
- reinforcement learning, 15–16
- retail fashion, 125
- robots, 10, 16, 90, 104, 115, 118–119, 122, 155–156
- Roche, 124
- Rumsfeld, Donald, 120
- safety engineers, 115
- Salesforce, 17, 55
- Sanbot, 10
- scale, 124–125, 126–128
- SEB, 117–118, 121
- security guards, 10
- self-driving cars, 7, 114, 115, 154
- Sensay, 138
- Siemens, 157
- Simon, Herbert, 6
- Singapore, 124
- singularity, 101–102
- Siri, 113. See also AI assistants
- smart glasses, 140–141
- smart speakers, 141–142
- social recommendations (social rex), 44–45
- software development, 124
- Soyuz, 33, 36
- speech recognition, 6–7, 9–10, 81
- speed, 123, 124, 126
- Starbucks, 130
- statistical truths, 22
- Stitch Fix, 105, 125
- stock trading, 9
- strategy
- for AI, 79–88
- business, 145–152
- supervised learning systems, 12–15
- sustainers, 114–115
- system development, 54–55
- tacit knowledge, 11–12
- task redesign, 19
- Tay chatbot, 89–90
- teams, 85–86
- technical diligence, 85
- technical employees, 66–67
- technological innovations, 3–4, 25
- technology optimists, 102, 107
- Thrun, Sebastian, 18
- Toyota Research Institute, 26
- training data, 75–77
- translation algorithms, 38–39
- transparency, lack of, xv
- Udacity, 17, 18, 20
- unemployment, 101
- Unilever, 124, 126–128
- unknown unknowns, 120
- unsupervised learning systems, 15
- utopian view, 101–102, 107
- value creation, 83, 85
- verifiability, lack of, 22
- Vicarious, 155–156
- Virgin Trains, 125
- voice recognition, 6–7, 9–10
- Wilke, Jeff, 14
- Wilson, H. James, 109–134, 153–162
- workers. See employees
- workforce
- impact of AI on, 99–108
- See also employees
- WorkFusion, 18–19
- XiaoIce, 142. See also AI assistants
- Yampolskiy, Roman V., 89–95
- Zimmermann, Annette, 137
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