IN024-05
NOAA's Center for Artificial Intelligence: Experiences, Plans and Status

Friday, 11 December 2020: 07:16
Virtual
Eric A Kihn, National Centers for Environmental Information, Boulder, CO, United States, Robert J Redmon, Natl Geophysical Data Ctr, Boulder, CO, United States and Sid Ahmed Boukabara, NOAA NESDIS, Camp Springs, MD, United States
Abstract:
Artificial Intelligence (AI), including Machine Learning (ML) technology, has been advancing rapidly into every aspect of society. As with other science based agencies the benefits of AI are emerging within the National Oceanic and Atmospheric Administration (NOAA) in areas as diverse as environmental observation, data ingest and management, weather and space weather forecasting, ocean forecast and prediction, and marine ecosystem and fisheries management.

To fully utilize AI technology in support of the NOAA mission, NOAA has established a strategic plan designed to accelerate and integrate AI into key NOAA mission areas. The NOAA AI goals are to fully take advantage of modern AI techniques and tools in order to 1) enhance forecast performance and skills, 2) increase efficiency and cost effectiveness in carrying out NOAA missions related to data, and 3) allow for new and innovative ways to exploit NOAA data assets for the benefit of the Nation. With appropriate adaptation AI methods provide transformative advancements in the quality, scope and timeliness of NOAA’s environmental science, products, and services.

The NOAA AI strategic plan calls for the establishment of the NOAA Center for Artificial Intelligence (NCAI). NOAA’s charge to the NCAI is to coordinate the adoption of AI research, development, acquisition, application, information exchange and awareness, and to maintain a portal with open source and government AI and related applications, host training events and workshops, and facilitate new partnerships across industry, academia, and government.

This presentation describes the implementation and experience of NOAA’s NCAI, including its functional components, implementation details, schedule, and metrics for success.