【英文】乔治梅森大学报告：全球人工智能基础设施报告（49页）

In 2016, the United States published its National Artificial Intelligence Research and Development Strategic Plan, usually understood in policy communities as the first statement of its AI infrastructure strategy (Select Committee on Artificial Intelligence, 2016). Since then over 60 countries have announced their national or sectoral AI policies. This report employs computer science techniques to analyze the published national AI plans of 54 countries. In other words, we employ AI to analyze AI strategies. The report includes an analysis of 213 documents on AI strategies. Apart from national plans, the set includes reports and publications from various government departments, ministries, nation commissions, bodies appointed to forward recommendations for specific issues and sectors. 

Our computer science methodology, specifically Latent Dirichlet Analysis (LDA) (Blei, Ng and Jordan, 2003), is calibrated to recognize embedded or latent topics that each document contains. It does so through providing probabilities of words that are most likely to occur together in each document. All documents are analyzed together for a pre-specified number of topics, ascertained through rigorous methodological criteria. The choice of the number of topics reflects fulfillment of various methodological LDA criteria for model stability (consistency) and topic stability (coherence). A document may feature a dominant topic, or a document may contain two or more topics. Further, we employ a technique known ensemble-LDA (e-LDA) to provide stable results assessed over multiple model specifications.