Author here! We explore the past, present, and future of deep learning in astronomy. We predict that GPT-like foundation models will make a huge impact on the field, and that astronomy is ideally placed to supercharge open source large language modelling (Section 9).

My favourite excerpt, where we propose foundation model-powered scientists:

Autonomous agents are no longer science fiction; task-driven autonomous agents powered by the simulacra of a foundation model are capable of solving very general tasks when given only a high-level prompt by a human operator [305,306]. One could therefore imagine a semi-automated research pipeline, where an autonomous agent with astronomical knowledge is given access to a set of astronomical data through an API. The agent would be prompted with a high-level research goal (such as ‘find something interesting and surprising within this dataset’), and would then take steps to achieve this task. These steps could include querying research papers for a literature review, searching a large multi-modal astronomical dataset to find data that supports a theory, evoking and discussing its findings with additional simulacra, or spinning up simulations to test a hypothesis [307]. While the agent operates in the background, the human researcher would be able to provide high-level interpretation of the results, and would be a steady hand providing guidance and refinement of a more general research direction. In this way, an astronomical foundation model would provide the tools to make all astronomers the principal investigator of their own powerful ‘AI lab’