  Traditionally, academic labs conduct open-ended research with the primary
focus on discoveries with long-term value, rather than direct products that can
be deployed in the real world. On the other hand, research in the industry is
driven by its expected commercial return on investment, and hence focuses on a
real world product with short-term timelines. In both cases, opportunity is
selective, often available to researchers with advanced educational
backgrounds. Research often happens behind closed doors and may be kept
confidential until either its publication or product release, exacerbating the
problem of AI reproducibility and slowing down future research by others in the
field. As many research organizations tend to exclusively focus on specific
areas, opportunities for interdisciplinary research reduce. Undertaking
long-term bold research in unexplored fields with non-commercial yet great
public value is hard due to factors including the high upfront risk, budgetary
constraints, 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 such
long-term research. With research organizations focused on an exploding array
of fields and resources spread thin, opportunities for the maturation of
interdisciplinary research reduce. Apart from these exigencies, there is also a
need to engage citizen scientists through open-source contributors to play an
active 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 citizen
scientists to partake in development and deployment of high social value
products at the intersection of space and AI.
