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Fig. 7. Strategic coordinate diagram of research front topics. V. C ONCLUSION The PLDA model is used to identify the research front topics based on NSF fund projects information. These projects were funded in the field of artificial intelligence from 2013 to 2018 years. The results show that intelligent system and infrastructure construction, wireless power transmission, quantum computing technology, intelligent simulation, imaging technology, new materials and new detection device research and development and so on, are mainly research front contents of artificial intelligence in five years in NSF. Exploring the front topic of the American artificial intelligence from the perspective of NSF fund projects can help us know well the research layout and development direction of the American artificial intelligence early and quickly, which plays an important role in the research of artificial intelligence in China because we can obtain quantitative information for prioritized technologies that could be used for technology management and decision making for research funding and technology investment, and promote the research and development of artificial intelligence in China. [5] [6] [7] [8] [9] [10] [11] [12] [13] R EFERENCES [1] [2] [3] [4] L. Zhang, Y. Huang, X. Liu, X. Wang, “Hot Spots Identification Based on NSF Funded Projects Information: A Case Study of Graphene”, Science and Technology Management Research, no. 7, pp. 43-49, 2017. X. Ma, “Development and application of topic models”, Computer Knowledge and Technology, no. 15, pp. 16-18, 2018. W. Wang, J. Ma, “Topic Detection and Evolution Analysis of Research Project based on LDA--Study of Projects on Ocean Acidification Supported by NSF”, Journal of Intelligence, no. 07, pp. 34-39, 2017. X. Wang, Z. Liu, R. Bai, L. Xu, J. Chen, “The Method of Research [14] [15] 86 Front Topic Detection Based on the Fund Project Data”, Library and Information Service, no. 13, pp. 87-98, 2017. L. Xu, X. Wang, R. Bai, “Research on Scientific Research Fronts Detection Method of Fund Sponsored Projects Based on TDT Model”, Information studies: Theory & Application, no. 07, pp. 72-78, 2018. J. Li, L. Xu, S. Zhao, “Prediction and Visualization of Emerging Topics of Fund Sponsored Projects Based on Time Series Analysis and SVM Model”, Information studies: Theory & Application, 2018. Y. Li, C. Su, J. Jia, Z. Xu, R. Tian, “Analysis of Development Status of World Artificial Intelligence Based on Scientific Measurement”, Computer Science, vol. 44, no. 12, pp. 183-187, 2017. X. Wang, Y. Yao, C. Ran, F. He, “Research on Parallel LDA Topic Modeling Method”, Transactions of Beijing Institute of Technology, vol. 33, no. 6, pp. 590-593, 2013. J. Niu, W. Tang, F. Xu, X. Zhou, Y. Song, “Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis”, vol. 5, no. 5, pp. 66, 2016. Dhar, Vasant. “The Future of Artificial Intelligence”, Big Date, vol. 4, no. 1, pp. 5-9, 2016. Y. Wang, “Development of AI and proposal of AI in China”, Electronic Engineering & Product World for Engineering Managers & Desingers, no. Z1, pp. 23-26, 2017. Nick, “A Brief History of Artificial Intelligence”, Beijing: Post & Telecom Press, 2017. Y. Zhuang, F. Wu, C. Chen, Y. Pan, “Challenges and opportunities:from big data to knowledge in AI2. 0”, Frontiers of information Technology & Electronic Engineering, no. 01, pp. 3-15, 2017. Tencent Research Institute, China ICT Internet Law Research Center, Tencent AI Lab, Tencent Open Platform, Artificial intelligence, Beijing: Renmin University Press, 2017. CIC Consulting Industry and Policy Research Center. 2018-2022, China’s artificial intelligence industry depth research and investment prospect prediction report (up-to-bottom volume), Shenzhen: CIC Consulting Industry and Policy Research Center, 2018.