What do governments plan in the field of artificial intelligence?

The primary goal of this paper is to explore how Natural Language Processing techniques (NLP) can assist in reviewing, understanding, and drawing conclusions from text datasets. We explore NLP techniques for the analysis and the extraction of useful information from the text of twelve national strategies on artificial intelligence (AI). For this purpose, we are using a set of machine learning algorithms in order to (a) extract the most significant keywords and summarize each strategy document, (b) discover and assign topics to each document, and (c) cluster the strategies based on their pair-wise similarity. Using the results of the analysis, we discuss the findings and highlight critical issues that emerge from the national strategies for artificial intelligence, such as the importance of the data ecosystem for the development of AI, the increasing considerations about ethical and safety issues, as well as the growing ambition of many countries to lead in the AI race. Utilizing the LDA topic model, we were able to reveal the distributions of thematic sub-topics among the strategic documents. The topic modelling distributions were then used along with other document similarity measures as an input for the clustering of the strategic documents into groups. The results revealed three clusters of countries with a visible differentiation between the strategies of China and Japan on the one hand and the Scandinavian strategies (plus the German and the Luxemburgish) one on the other. The former promote technology and innovation-driven development plans in order to integrate AI with the economy, while the latter share a common view regarding the role of the public sector both as a promoter and investor but also as a user and beneficiary of AI, and give a higher priority to the ethical & safety issues that are connected to the development of AI.