
Contents
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4.1 Introduction 4.1 Introduction
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4.2 The Economics of New Research Tools: The Interplay between New Methods of Invention and the Generality of Innovation 4.2 The Economics of New Research Tools: The Interplay between New Methods of Invention and the Generality of Innovation
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4.3 The Evolution of Artificial Intelligence: Robotics, Symbolic Systems, and Neural Networks 4.3 The Evolution of Artificial Intelligence: Robotics, Symbolic Systems, and Neural Networks
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4.4 How Might Different Fields within Artificial Intelligence Impact Innovation? 4.4 How Might Different Fields within Artificial Intelligence Impact Innovation?
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4.5 Data 4.5 Data
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4.5.1 Publication Sample and Summary Statistics 4.5.1 Publication Sample and Summary Statistics
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4.5.2 Patent Sample and Summary Statistics 4.5.2 Patent Sample and Summary Statistics
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4.6 Deep Learning as a GPT: An Exploratory Empirical Analysis 4.6 Deep Learning as a GPT: An Exploratory Empirical Analysis
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4.7 Deep Learning as a General Purpose Invention in the Method of Invention: Considerations for Organizations, Institutions, and Policy 4.7 Deep Learning as a General Purpose Invention in the Method of Invention: Considerations for Organizations, Institutions, and Policy
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4.7.1 The Management and Organization of Innovation 4.7.1 The Management and Organization of Innovation
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4.7.2 Innovation and Competition Policy and Institutions 4.7.2 Innovation and Competition Policy and Institutions
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4.8 Concluding Thoughts 4.8 Concluding Thoughts
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Appendix Appendix
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References References
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Comment Comment
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Is AI a Technology for Innovation or Imitation? Is AI a Technology for Innovation or Imitation?
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Hammers That Make Nails Hammers That Make Nails
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Competition, Innovation, and Privacy Competition, Innovation, and Privacy
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References References
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4 The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis
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Published:May 2019
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Abstract
Artificial intelligence (AI) may serve as a new general-purpose "method of invention" that can reshape the innovation process and R&D. We review the history of AI, including the distinction between robotics and the potential for "deep learning" to be a general-purpose method of invention. We assess evidence of this differential impact in the changing nature of measurable innovation outputs in AI, including papers and patents. We find evidence of a "shift" in the importance of application-oriented learning research since 2009 (relative to robotics and symbolic systems research), and that some of this shift began outside the United States. We consider implications of our findings, including changes in the innovation process, and policy and institutional responses to deep learning where it represents a general-purpose method of invention. There may be significant substitution away from routinized labor-intensive research towards research that exploits the interplay between passively generated datasets and enhanced prediction algorithms. The potential commercial reward of such research may trigger a period of racing, driven by powerful incentives for companies to acquire and control critical datasets and application-specific algorithms. We suggest that policies encouraging transparency and dataset sharing across public and private actors can stimulate more innovation-oriented competition and research productivity.
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