Machine, Platform, Crowd: Harnessing Our Digital Future
By Erik Brynjolfsson, Andrew P. McAfee. 2017
711 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/155f7fb13e0149abbbda738d28aa70eb9df9b392
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Mike Wright
- Frederik Kratzert
- Grey Nearing
- Maryann P. Feldman
- Daniel Klotz
- Katherine C. Kellogg
- Satish Nambisan
- Melissa A. Valentine
Citations in the corpus
- Algorithms at Work: The New Contested Terrain of Control (2020, background): “…how this use may enable geographically dispersed people to come together (Brabham, 2013); give workers high levels of flexibility and autonomy (McAfee & Brynjolfsson, 2017); create better matching between the supply and demand of worker skills (Kittur et al., 2013); and heighten inclusivity…”
- The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes (2019, background): “…new types of customer experiences) (e.g., Boulton, 2018; Boutetiere and Reich, 2018), and more broadly, to indicate how existing companies may need to radically transform themselves to succeed in the emerging digital world (e.g., McAfee and Brynjolfsson, 2017; Rogers, 2016; Venkatraman, 2017).”
- Digital transformation: Five recommendations for the digitally conscious firm (2020, unknown): “…is prone to being associated with paradigm-shifting technologies such as artifi-cial intelligence, machine learning, or big data, all of which require significant up-front investments in time, money, and staff before any significant benefits can be realized (Lee, 2017; McAfee & Brynjolfsson, 2017).”
- Hybrid Intelligence (2019, background): “This triggers new heated debates of when machines will ultimately replace humans (McAfee and Brynjolfsson 2017).”
- Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning (2019, background): “Science and society are firmly in the age of machine learning (ML; McAfee & Brynjolfsson, 2017).”