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Research with AI and ML technologies lives in a variety of settings withoften asynchronous goals and timelines: academic labs and governmentorganizations pursue open-ended research focusing on discoveries with long-termvalue, while research in industry is driven by commercial pursuits and hencefocuses on short-term timelines and return on investment. The journey fromresearch to product is often tacit or ad hoc, resulting in technologytransition failures, further exacerbated when research and development isinterorganizational and interdisciplinary. Even more, much of the ability toproduce results remains locked in the private repositories and know-how of theindividual researcher, slowing the impact on future research by others andcontributing to the ML community's challenges in reproducibility. With researchorganizations focused on an exploding array of fields, opportunities for thehandover and maturation of interdisciplinary research reduce. With thesetensions, we see an emerging need to measure the correctness, impact, andrelevance of research during its development to enable better collaboration,improved reproducibility, faster progress, and more trusted outcomes. Weperform a case study of the Frontier Development Lab (FDL), an AI acceleratorunder a public-private partnership from NASA and ESA. FDL research followsprincipled practices that are grounded in responsible development, conduct, anddissemination of AI research, enabling FDL to churn successfulinterdisciplinary and interorganizational research projects, measured throughNASA's Technology Readiness Levels. We also take a look at the SpaceML OpenSource Research Program, which helps accelerate and transition FDL's researchto deployable projects with wide spread adoption amongst citizen scientists.

Learnings from Frontier Development Lab and SpaceML -- AI Accelerators for NASA and ESA