Considering Workforce Development for AI in Scientific Research
Advancements in Artificial Intelligence (AI) continue to propel a revolution in academic research computing. As academics turn to AI to make fundamental strides in their research, they refine and expand existing techniques to create new models and algorithms, AI-ready datasets, AI-relevant cyberinfrastructure, and more. However, with AI techniques and computing resources evolving rapidly, both academia and industry must ensure that a trained workforce of capable researchers is poised to make continued advancements in the AI sphere. The NSF Center of Excellence for Science Gateways (SGX3) convened an initiative called the AI Blueprint Factory to ascertain the AI needs of research communities that use national-scale computing infrastructure, and to make 5 to 10 year forecasts of needed features and resources. Its study team interviewed researchers across disciplines and seniority levels to determine perceived needs, opportunities, and gaps in AI research support. With this study, SGX3 seeks to identify the evolving scientific needs for AI capabilities, in order to foster the utilization of AI techniques in domain science research. In this paper, we describe the study’s conclusions, including concerns and recommendations, relating to workforce development for AI in scientific research.