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The Impact of Artificial Intelligence on Innovation
Open accessAbstract
Artificial intelligence may greatly increase the efficiency of the existing economy.But it may have an even larger impact by serving as a new general-purpose "method of invention" that can reshape the nature of the innovation process and the organization of R&D.We distinguish between automation-oriented applications such as robotics and the potential for recent developments in "deep learning" to serve as a general-purpose method of invention, finding strong evidence of a "shift" in the importance of application-oriented learning research since 2009.We suggest that this is likely to lead to a significant substitution away from more routinized labor-intensive research towards research that takes advantage of the interplay between passively generated large datasets and enhanced prediction algorithms.At the same time, the potential commercial rewards from mastering this mode of research are likely to usher in a period of racing, driven by powerful incentives for individual companies to acquire and control critical large datasets and application-specific algorithms.We suggest that policies which encourage transparency and sharing of core datasets across both public and private actors may be critical tools for stimulating research productivity and innovation-oriented competition going forward.
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Endogenous Technological Change
Romer, P. M. (1990) · Journal of Political Economy · doi:10.1086/261725 · Cited by 17,711
“A Model of Growth: Through Creative Destruction”
Aghion, P., & Howitt, P. (1992) · Econometrica · doi:10.2307/2951599 · Cited by 7,529
Learning internal representations by error propagation
RUMELHART, D. E., HINTON, G. E., & WILLIAMS, R. J. (1988) · Readings in Cognitive Science · doi:10.1016/b978-1-4832-1446-7.50035-2 · Cited by 3,002
The Rapid Adoption of Data-Driven Decision-Making
Brynjolfsson, E., & McElheran, K. (2016) · American Economic Review · doi:10.1257/aer.p20161016 · Cited by 843
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The role of artificial intelligence in achieving the Sustainable Development Goals
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The Productivity J-Curve: How Intangibles Complement General Purpose Technologies
Brynjolfsson, E., Rock, D., & Syverson, C. (2021) · American Economic Journal: Macroeconomics · doi:10.1257/mac.20180386 · Cited by 1,003
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023) · arXiv · doi:10.48550/arxiv.2303.10130 · Cited by 2,330
Digital transformation challenges: strategies emerging from a multi-stakeholder approach
Brunetti, F., Matt, D. T., Bonfanti, A., De Longhi, A., Pedrini, G., & Orzes, G. (2020) · The TQM Journal · doi:10.1108/tqm-12-2019-0309 · Cited by 544
Artificial intelligence and industrial innovation: Evidence from German firm-level data
Rammer, C., Fernández, G. P., & Czarnitzki, D. (2022) · Research Policy · doi:10.1016/j.respol.2022.104555 · Cited by 489
Metadata
- Authors
- Cockburn, I., Henderson, R., & Stern, S.
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 2018-03
- OpenAlex
- W2767479331
- Cited by
- 478
- References
- 5
- Type
- report
- Language
- en
- Open access
- green
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