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Traditionally, academic labs conduct open-ended research with the primaryfocus on discoveries with long-term value, rather than direct products that canbe deployed in the real world. On the other hand, research in the industry isdriven by its expected commercial return on investment, and hence focuses on areal world product with short-term timelines. In both cases, opportunity isselective, often available to researchers with advanced educationalbackgrounds. Research often happens behind closed doors and may be keptconfidential until either its publication or product release, exacerbating theproblem of AI reproducibility and slowing down future research by others in thefield. As many research organizations tend to exclusively focus on specificareas, opportunities for interdisciplinary research reduce. Undertakinglong-term bold research in unexplored fields with non-commercial yet greatpublic value is hard due to factors including the high upfront risk, budgetaryconstraints, and a lack of availability of data and experts in niche fields.Only a few companies or well-funded research labs can afford to do suchlong-term research. With research organizations focused on an exploding arrayof fields and resources spread thin, opportunities for the maturation ofinterdisciplinary research reduce. Apart from these exigencies, there is also aneed to engage citizen scientists through open-source contributors to play anactive part in the research dialogue. We present a short case study of SpaceML,an extension of the Frontier Development Lab, an AI accelerator for NASA.SpaceML distributes open-source research and invites volunteer citizenscientists to partake in development and deployment of high social valueproducts at the intersection of space and AI.

SpaceML: Distributed Open-source Research with Citizen Scientists for the Advancement of Space Technology for NASA