Abstract
Our work analyzes the artificial intelligence and machine learning (AI/ML) research portfolios of six large research funding organizations from the United States [National Institutes of Health (NIH) and National Science Foundation (NSF)]; Europe [European Commission (EC) and European Research Council (ERC)]; China [National Natural Science Foundation of China (NNSFC)]; and Japan [Japan Society for the Promotion of Science (JSPS)]. The data for this analysis is based on 127,000 research clusters (RCs) that are derived from 1.4 billion citation links between 104.8 million documents from four databases (Dimensions, Microsoft Academic Graph, Web of Science, and the Chinese National Knowledge Infrastructure). Of these RCs, 600 large clusters are associated with AI/ML topics, and 161 of these AI/ML RCs are expected to experience extreme growth between May 2020 and May 2023. Funding acknowledgments (in the corpus of the 104.9 million documents) are used to characterize the overall AI/ML research portfolios of each organization. NNSFC is the largest funder of AI/ML research and disproportionately funds computer vision. The EC, RC, and JSPS focus more efforts on natural language processing and robotics. The NSF and ERC are more focused on fundamental advancement of AI/ML rather than on applications. They are more likely to participate in the RCs that are expected to have extreme growth. NIH funds the largest relative share of general AI/ML research papers (meaning in areas other than computer vision, natural language processing, and robotics). We briefly describe how insights such as these could be applied to portfolio management decision-making.
Highlights
The research funding portfolios of large, government-sponsored organizations provide insight into the competitive research landscape and the corresponding research opportunities that are being addressed in preparation for the future
If at least 50% of articles in an research clusters (RCs) are classified as AI/ML, we label the cluster as AI
We measure a funding organization’s level of contribution as follows: (1) Identify the share of articles in AI clusters on which the funding organization is acknowledged; (2) Set the median share of AI/ML-classified articles funded as the threshold for the funding organization to be labeled a contributor to a given research cluster—each funder is a contributor to the half of the clusters in which they have greater than their median share; and (3) Define the level of contribution as the fraction of clusters in which the funding organization is acknowledged
Summary
The research funding portfolios of large, government-sponsored organizations provide insight into the competitive research landscape and the corresponding research opportunities (or threats) that are being addressed in preparation for the future. We offer examples of these portfolios in order to highlight important details about each organization’s funding portfolio. Forecasting is a potentially helpful part of this process even if it does not guarantee success on its own (Gerstner, 1972). Forecasting is a process that leaders, experts, and analysts employ to estimate the probability of future events, relative states, or trends based on past and present data. Forecasts are data-driven and clearly described with a well-defined unit of analysis, time frame, occurrence probability or confidence interval, and, if possible, relevant conditional factors
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