@inproceedings{fb1638b9e8ab4ed8bfe69f94307161d2,
title = "Knowledge Combination Analysis Reveals That Artificial Intelligence Research Is More Like “Normal Science” Than “Revolutionary Science”",
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.",
keywords = "artificial intelligence, bibliographic data, co-citation networks, knowledge combination, knowledge management, scientific research",
author = "Jieshu Wang and Andrew Maynard and Jose Lobo and Katina Michael and S{\'e}bastien Motsch and Deborah Strumsky",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE Computer Society. All rights reserved.; 57th Annual Hawaii International Conference on System Sciences, HICSS 2024 ; Conference date: 03-01-2024 Through 06-01-2024",
year = "2024",
language = "English (US)",
series = "Proceedings of the Annual Hawaii International Conference on System Sciences",
publisher = "IEEE Computer Society",
pages = "5598--5607",
editor = "Bui, {Tung X.}",
booktitle = "Proceedings of the 57th Annual Hawaii International Conference on System Sciences, HICSS 2024",
}