APPLIED ARTIFICIAL INTELLIGENCE
FOR SCIENCE & EXPLORATION ENABLED BY PUBLIC-PRIVATE PARTNERSHIPS
By Madhulika Guhathakurta
The recent advances in Artificial Intelligence (AI) capabilities are particularly relevant to NASA science and exploration goals because there is growing evidence that AI techniques can improve our ability to model, understand and predict our environment using the petabytes of data already within NASA archives. In particular this represents a strategic opportunity in Heliophysics, since the need to improve our understanding of space weather is not only mandated by directives such as the National Space Weather Action Plan and the Presidential Executive Order for Coordinating Efforts to Prepare the Nation for Space Weather Events, but also because space weather is a critical consideration for astronaut safety as NASA moves forward leave LEO and return to the Moon. I will briefly discuss NASA Science Mission Directorate’s (SMD) Strategy for Data Management and Computing for Groundbreaking Science 2019-2024, prepared by the NASA Strategic Data Management Working Group (SDMWG) which recommends that SMD encourage its science divisions to explore novel computational techniques, including those encompassed by artificial intelligence and machine learning (AI/ML) and steps being taken.
I will also talk about the Frontier Development Lab (FDL) which is an AI research accelerator that was established in 2016 to apply emerging AI technologies to space science challenges which are central to NASA's mission priorities and provide some examples. FDL is a partnership between NASA Ames Research Center and the SETI Institute, with corporate sponsors that include IBM, Intel, NVidia, Google, Lockheed, Autodesk, Xprize, Space Resources Luxembourg, as well as USC and other organizations. The goal of FDL is to apply leading edge Artificial Intelligence and Machine Learning (AI/ML) tools to space challenges that impact space exploration and development, and even humanity. Five prior FDL sessions have demonstrated that meaningful progress could be industrialized by bringing together individuals at the PhD and Post Doc level as well as members from industry together to work on connected, but adjacent problems in a shared space mentored by senior scientists with a deep knowledge of the problems. FDL uses sprint methodologies for faster results, uses interdisciplinary teams for better results, and public-private partnerships to lower costs. FDL results will be shared that demonstrate the power of bridging research disciplines and the potential that AI/ML has for supporting research goals, improving on current methodologies, enabling new discovery and doing so in accelerated timeframes.