Correlation Monitoring Method and model of Science-Technology-Industry in the AI Field: A Case of the Neural Network

Xiaoli Wang, Yun Liu, Lingdi Chen and Yifan Zhang

SAGE Open, 2022, vol. 12, issue 4, 21582440221141299

Abstract: This article aims to analyze the correlation status and development trend among science, technology and industry in the Artificial Intelligence (AI) subfield. First, it constructs the classification system and retrieval strategy for the AI subfield, and performs data cleaning and data finishing. Second, it designs the criteria and judgment method of the Industry Development Stages (IDS) in the AI subfield with the Delphi survey method. Third, it designs the monitoring method and model of the Science-Technology-Industry (STI) correlation with the methods of scientometrics and clustering, which involves three dimensions: science and IDS, technology and IDS, science & technology clustering and IDS; especially the monitoring with the global and national perspectives. Finally, it conducts empirical application in the field of neural network. The results illustrate that the correlation monitoring method can be conducive to grasp accurately the development opportunity and trend, the advantage subfield, and the development status of Science and Technology (S&T) for the dominant countries. The United States leads the global development in the empirical field. The proposed correlation monitoring method in this article can also be applied in other fields.

Keywords: AI; science-technology-industry; correlation monitoring method; hot-degree; influence (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:12:y:2022:i:4:p:21582440221141299

DOI: 10.1177/21582440221141299

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