As with other fields of astronomy, radio astronomy involves managing and analyzing large volumes of data. New and upcoming facilities, such as ALMA WSU and ngVLA, will increase these data volumes and their complexity by 1-2 orders of magnitude. AI/ML methods can help in several ways throughout the process of turning an observing proposal into science.

Language models can help sort through thousands of proposals for telescope time, review them, and set up observations and processing pipelines. Numerically-based techniques can be used to assist with processing and analyzing the ~1-100TB datasets produced by current and future facilities. Last year (and restarting this year), we have been holding meetings at NRAO to discuss ways in which AI/ML can help with aspects of Observatory functions.

This has been given new impetus by the award of the Cosmic AI Institute to a consortium including NRAO. In this talk, Dr. Lacy summarizes the outcomes of these meetings and presents ideas that arose from our discussions, as well as challenges that arise from using these techniques in an Observatory setting.

Dr. Lacy also shows examples of AI/ML applied to radio astronomy data for both quality assurance and scientific purposes, by ourselves and by other groups working on data from the VLA Sky Survey.