Stuart, Michael T. and Winters, Sabine (2025) Learning Curves in Orbit: Progress with AI in Space Science. [Preprint]
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Abstract
AI methods are being touted as a powerful new source of scientific progress. Are they? If so, what kind of progress do they facilitate? To find out, we employed qualitative research methods to explore how space scientists conceive of AI. We show that space scientists are mainly concerned with whether AI can help them solve specific problems, and more generally, to extend their abilities in useful ways. This coheres best with a “functional” account of scientific progress (Kuhn 1962; Laudan 1978; Shan 2019, 2022). Despite recent work applying functional accounts to seismology (Miyake 2022) and economics (Boumans and Herfeld 2022), the functional account is still “insufficiently assessed” (Shan 2022, 2). Inspired by our qualitative data, we propose a new type of functional account according to which scientific progress is simply improving scientific abilities.
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