标题： 前沿开发实验室和SpaceML的学习——NASA和ESA的AI加速器 显示英文标题

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

摘要： 利用人工智能和机器学习技术的研究存在于各种环境中，这些环境通常具有不同步的目标和时间表：学术实验室和政府组织追求开放性研究，侧重于长期价值的发现，而工业界的研究则由商业目标驱动，因此更关注短期的时间表和投资回报。 从研究到产品的过渡过程往往是隐含的或临时性的，导致技术转化失败，尤其是在跨组织和跨学科的研究与开发中情况更为严重。 更有甚者，许多能够产生成果的能力仍然被锁定在个别研究人员的私人存储库和专业知识中，这减缓了对他人未来研究的影响，并加剧了机器学习社区在可重复性方面面临的挑战。 随着研究机构专注于越来越多的领域，跨学科研究交接和成熟的机会减少。 面对这些紧张局势，我们看到有必要在研究开发过程中衡量其正确性、影响和相关性，以促进更好的协作、改进的可重复性、更快的进步以及更可信的结果。 我们对前沿开发实验室（FDL）进行了案例研究，FDL是美国宇航局和欧洲航天局公共私营合作下的一个人工智能加速器。 FDL的研究遵循基于负责任开发、实施和传播人工智能研究的原则实践，使FDL能够推动成功的跨学科和跨组织研究项目，通过美国宇航局的技术就绪水平来衡量。 我们还审视了SpaceML开源研究项目，该项目帮助加速并促成FDL的研究转化为可在公民科学家中有广泛采用的部署项目。 显示英文摘要

摘要： Research with AI and ML technologies lives in a variety of settings with often asynchronous goals and timelines: academic labs and government organizations pursue open-ended research focusing on discoveries with long-term value, while research in industry is driven by commercial pursuits and hence focuses on short-term timelines and return on investment. The journey from research to product is often tacit or ad hoc, resulting in technology transition failures, further exacerbated when research and development is interorganizational and interdisciplinary. Even more, much of the ability to produce results remains locked in the private repositories and know-how of the individual researcher, slowing the impact on future research by others and contributing to the ML community's challenges in reproducibility. With research organizations focused on an exploding array of fields, opportunities for the handover and maturation of interdisciplinary research reduce. With these tensions, we see an emerging need to measure the correctness, impact, and relevance of research during its development to enable better collaboration, improved reproducibility, faster progress, and more trusted outcomes. We perform a case study of the Frontier Development Lab (FDL), an AI accelerator under a public-private partnership from NASA and ESA. FDL research follows principled practices that are grounded in responsible development, conduct, and dissemination of AI research, enabling FDL to churn successful interdisciplinary and interorganizational research projects, measured through NASA's Technology Readiness Levels. We also take a look at the SpaceML Open Source Research Program, which helps accelerate and transition FDL's research to deployable projects with wide spread adoption amongst citizen scientists.