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

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dc.contributor.author Wang, Jieshu
dc.contributor.author Maynard, Andrew
dc.contributor.author Lobo, José
dc.contributor.author Michael, Katina
dc.contributor.author Motsch, Sébastien
dc.contributor.author Strumsky, Deborah
dc.date.accessioned 2023-12-26T18:47:20Z
dc.date.available 2023-12-26T18:47:20Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other 4d8db160-55d2-4367-9856-97d65770dc4f
dc.identifier.uri https://hdl.handle.net/10125/107058
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Value, Success, and Performance Measurements of Knowledge, Innovation and Entrepreneurial Systems
dc.subject academic publications
dc.subject artificial intelligence
dc.subject knowledge combination
dc.subject novelty
dc.subject scientific research
dc.title Knowledge Combination Analysis Reveals That Artificial Intelligence Research Is More Like "Normal Science" Than "Revolutionary Science"
dc.type Conference Paper
dc.type.dcmi Text
dcterms.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.
dcterms.extent 10 pages
prism.startingpage 5598
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Value, Success, and Performance Measurements of Knowledge, Innovation and Entrepreneurial Systems