Link to Recording: link to video file on Google DriveSlides: presentations folder on Google DriveChair: Jebb Stewart
Advances in computing power through new generation Graphical Processing Unit’s (GPU), the availability of data, and the generation of new software libraries have made Machine Learning (ML) and Artificial Intelligence (AI) more accessible to diverse fields including medicine, self-driving cars, and social media. These technologies are seeing a significant increase in applicability and research has shown certain ML algorithms have several advantages over traditional methods, including improving execution speed, stability, and accuracy. There are a variety of areas where AI/ML techniques can be applied in the environmental data management ecosystem. These include efficient and intelligent signal and image processing; quality control mechanisms; pattern recognition; anomaly detection; data fusion; mapping; and prediction. Share your experiences or vision of using AI to help environmental data management techniques.
Presentations (abstracts in this Google Doc):
- Tackling challenges of Ocean Exploration with Machine Learning and Artificial Intelligence
Mashkoor Malik, Megan Cromwell, Kasey Cantwell, Matt Dornback, Philip Hoffman - Machine Learning Application to Small Data: A Case Study Using Ocean Carbon Time Series
Roman Battisti, Adrienne Sutton, Sylvia Musielewicz, Stacy Maenner, John Osborne, John Evans, Randy Bott, Sean Dougherty - A satellite-based daily near-surface temperature data records for the Tibetan Plateau
Yuhan (Douglas) Rao, Shunlin Liang, Dongdong Wang, Yunyue Yu - What is "AI-Ready" Open Data?
Tyler Christensen, Cassandra Ladino, Deirdre Clarkin, Bob Williams
