Knowledge Combination Analysis Reveals That Artificial Intelligence Research Is More Like "Normal Science" Than "Revolutionary Science"

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0549.pdf (1.2 MB)
Date
2024-01-03
Authors
Wang, Jieshu
Maynard, Andrew
Lobo, José
Michael, Katina
Motsch, Sébastien
Strumsky, Deborah
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5598
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Abstract
Artificial Intelligence (AI) research is intrinsically innovative and serves as a source of innovation for research and development in a variety of domains. There is an assumption that AI can be considered "revolutionary science" rather than "normal science." Using a dataset of nearly 300,000 AI publications, this paper examines the co-citation dynamics of AI research and investigates its trajectory from the perspective of knowledge creation as a combinatorial process. We found that while the number of AI publications grew significantly, they largely follows a normal science trajectory characterized by incremental and cumulative advancements. AI research that combines existing knowledge in highly conventional ways is a substantial driving force in AI and has the highest scientific impact. Radically new ideas are relatively rare. By offering insights into the co-citation dynamics of AI research, this work contributes to understanding its evolution and guiding future research directions.
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Value, Success, and Performance Measurements of Knowledge, Innovation and Entrepreneurial Systems, academic publications, artificial intelligence, knowledge combination, novelty, scientific research
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https://hdl.handle.net/10125/107058
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10 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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Value, Success, and Performance Measurements of Knowledge, Innovation and Entrepreneurial Systems
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