AI / Machine Learning

AI / Machine Learning Summary

Summary

 SSAI has always been and will always be on the cutting edge of the latest science and technology developments - and computer science is no exception.  Thus, in addition to software development, website design and development, and IT services, SSAI has expanded into the field of artificial intelligence.  Artificial intelligence is a branch of computer science that seeks to enable software to exhibit human-like behavior in complex environments. The most prominent success in this endeavor has come from the subfield of machine learning, which takes a data-centric and statistically-motivated perspective on the concept of learning. The ideas behind machine learning focus on learning from experience, in any number of forms - such as videos, pictures, and text - as well as any other type of data that can be used to educate machine learning models about their environment. Over time, SSAI has developed a state-of-the-art computer science department that supports research in, implementation of, and on-going education efforts related to artificial intelligence and machine learning applications.

 

Major Accomplishments

The SSAI-led Earth Science Data System Working Groups (ESDSWG) Organization Prioritizes Machine Learning to Keep Pace with the Next Generation of Earth Science Applications

Attribute: Chitra Sancheti, CC BY-SA 4.0, via Wikimedia CommonsThe Earth Science Data System Working Groups (ESDSWG) is an organization that brings together subject matter experts from NASA’s Earth science community to collaborate on developing recommendations for improving NASA Earth science data systems. Membership includes representatives from the Earth Science Data and Information System (ESDIS) project, the Distributed Active Archive Centers (DAACs), and NASA funded research programs, while SSAI employs the lead program coordinator. Current focus areas include improving the infusion of new technologies into NASA Earth science data systems and developing expertise in machine learning for Earth science applications.

Working groups are formed, renewed, and retired on an annual cycle based around the ESDSWG annual meeting. In response to SSAI’s insight into future technology needs, the SSAI-led ESDSWG team designed the 2021 ESDWG Annual Meeting to explore community needs in technology infusion and machine learning and to establish four new working groups to address this need. SSAI’s ongoing work includes liaison with ESDIS and ACCESS program management to monitor progress on the working group objectives and working with the working group technical chairs to ensure that objectives are being met and that they have the resources that they need.

ESDIS continues to value the recommendation produced by the working groups and many of the recommendation go on to be adopted as Earth Science Data Systems (ESDS) best practices. The current work on machine learning is particularly important to the emergence of a new generation of Earth science applications and SSAI’s contribution through ESDSWG collaboration efforts will undoubtedly continue to advance the field. 


Asset Publisher

In The News

SSAI’s Craig Pelisiser wins AIST grant for TERRAHydro proposal, applying Machine Learning to Earth Science models

Congratulations to SSAI's Dr. Craig Pelissier and the winning team he assembled as Principal Investigator for his recently-selected TERRAHydro proposal for NASA's Advanced Information Systems Technology (AIST-21) Program!

Digital Twin Machine Learning




Related Capabilities