The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
By Morgan C. Benton. 2016
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Katherine C. Kellogg
- Edward W. Felten
- Melissa A. Valentine
- Robert C. Seamans
- Jenna Burrell
- Angèle Christin
- A. Hirschi
- Berkman Sahiner
Citations in the corpus
- Research Priorities for Robust and Beneficial Artificial Intelligence (2015, background): “In this context, the criterion for intelligence is related to statistical and economic notions of rationality – colloquially, the ability to make good decisions, plans, or inferences.”
- Ethics of artificial intelligence and robotics (2020, background): “…from AI have ranged from the alarmed (Frey and Osborne 2013; Westlake 2014) to the neutral (Metcalf, Keller, and Boyd 2016; Calo 2018) and the optimistic (Brynjolfsson and McAfee 2016; Harari 2016; Danaher forthcoming-a) – though there is agreement that very significant disruption is likely.”
- Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses (2021, result): “We believe that this relationship is in line with current scientific perceptions of AI technologies (e.g., Brynjolfsson & McAfee, 2014; Brynjolfsson & Mitchell, 2017) and accordingly provides a measure of support for our methodology.”
- Six Human-Centered Artificial Intelligence Grand Challenges (2023, background): “Expansion of AI-enabled use cases across a broad spectrum of domains has underscored the benefits and potential risks of AI (Brynjolfsson & McAfee, 2014; PricewaterhouseCoopers, 2017; West, 2018).”
- Toward New-Generation Intelligent Manufacturing (2018, background): “Highlighted by the innovation and application of computing, communications, control, and other information technologies, the third industrial revolution has continuously pushed industrial development to a new height [51].”
- Artificial Intelligence and Management: The Automation–Augmentation Paradox (2020, background): “8 Managers need to use their intuition and common-sense judgment — reconciling the machine output with reality — to make a final decision about the most desirable option (Brynjolfsson & McAfee, 2014: 92).”
- GDP-B: Accounting for the Value of New and Free Goods in the Digital Economy (2019, unknown): “Our 4 Among others, see, for example, Gordon (2016) and Cowen (2011) giving a pessimistic view and Sichel (2016), Mokyr, Vickers and Ziebarth (2015) and Brynjolfsson and McAfee (2011, 2014) giving a more optimistic view.”
- Generative artificial intelligence (2023, unknown): “Thus, the prospects of value co-creation come in hand with potential changes in human work roles, requiring workforces in various domains to adapt their tasks as a diverse set of tasks could be impacted by generative AI (Brynjolfsson & McAfee, 2016; Eloundou et al., 2023).”
- Deep learning in medical imaging and radiation therapy. (2018, background): “…our everyday lives, for example, by reducing healthcare costs, as well as to negatively affect society, for example, by causing large-scale unemployment and rising income inequality 8,9 (according to one estimate, half of all working activities can be automated by existing technologies 10 ).”
- Discontinuities, competition, and cooperation: Coopetitive dynamics between incumbents and entrants (2018, background): “6 It is helpful to understand that digitalization has incrementally affected the underlying core-knowledge production to some extent, but the relevant distinction here is that old-line complementary assets have been radically challenged because the new digital distributive technologies can substitut”
- The Employment Consequences of Robots: Firm-Level Evidence (2020, unknown): “…shifted the comparative advantage from humans to machines for a growing list of occupations (Brynjolfsson and Mitchell 2017, Felten et al. 2019, Frey and Osborne 2017), potentially leaving human labor with substantially fewer activities that can add value (Brynjolfsson and McAfee 2014, Ford 2015).”
- Data‐Driven Materials Science: Status, Challenges, and Perspectives (2019, background): “Often connected to the fourth industrial revolution[6] or the second machine age,[7] such data-driven approaches permeate science, business, politics, and even social life.”