Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
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.
Recommended Citation
Wang, Jieshu; Maynard, Andrew; Lobo, José; Michael, Katina; Motsch, Sébastien; and Strumsky, Deborah, "Knowledge Combination Analysis Reveals That Artificial Intelligence Research Is More Like "Normal Science" Than "Revolutionary Science"" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 2.
https://aisel.aisnet.org/hicss-57/ks/knowledge_value/2
Knowledge Combination Analysis Reveals That Artificial Intelligence Research Is More Like "Normal Science" Than "Revolutionary Science"
Hilton Hawaiian Village, Honolulu, Hawaii
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.
https://aisel.aisnet.org/hicss-57/ks/knowledge_value/2