Skip to main content

Currently Skimming:

Identifying Future Opportunities to Accelerate Progress
Pages 31-38

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 31...
... In addition, funding and structures that can facilitate the exchange of ideas underlie any vision for progress. Participants discussed opportunities to consider new models that could enable convergent science, knowledge transfer, and partnerships to support science at the intersection of ML/AI and Earth system science.
From page 32...
... . Graph neural networks can incorporate information across a seismic network rather than just from a single station, which could allow for estimating source information and predicting shaking amplitudes (van den Ende and Ampuero, 2020)
From page 33...
... TropicalTidbits. future to predict near-term storm patterns; identification of specific storm cells or hazards that are apparent in radar imagery and project where they are likely to move over the short term; and full complexity simulations of the atmosphere, including numerical weather prediction, to create short-term forecasts.
From page 34...
... Lermusiaux considered examples of learning bottom gravity current models relevant for coastal and climate predictions and of learning biogeochemical dynamics -- ultimately important not only for modeling the Earth system but also for understanding the world's food system. In this latter Bayesian learning example, scientists can discover ecosystem reaction functions with Bayesian ML that assimilates observations in all of the possible candidate models, revealing the best model as well as the equations of a priori unknown model formulations, all with estimates of their uncertainties (Gupta et al., 2019; Haley et al., 2020; Lermusiaux et al., 2020; Lu and Lermusiaux, 2021)
From page 35...
... NOVEL FUNDING MECHANISMS, PARTNERSHIPS, AND KNOWLEDGE TRANSFER AMONG ACADEMIA, INDUSTRY, NONPROFITS, AND GOVERNMENT Conversationalists shared their knowledge, ideas, and creative approaches to create mechanisms for funding opportunities, partnerships, and knowledge transfer among academia, industry, nonprofits, and government. In the context of opportunities and challenges for using ML/AI to advance Earth system science raised throughout the workshop -- promising avenues for research, responsible and ethical use of AI, the urgency to diversify these fields, and strategies for training the workforce -- several participants emphasized the need to develop sustainable mechanisms and funding sources to make progress in these areas.
From page 36...
... Chayes suggested building a dynamic infrastructure that scales to enable Earth system science -- for example, built on an open science platform, such as a Jupyter, enabling transparency and collaboration. Spergel noted that the academic community often does not support careers that develop and maintain tools valuable to the community.
From page 37...
... In order to provide support and retain students in the program, Chayes gave the example of a data science scholars program that focuses on Pell Grant and first-generation students, offering a community of students extra help with course material. Spergel suggested providing a wider variety of onramps that would allow students to get expertise in data science in different ways -- for example, fully funding master's programs in data science or ML at minority-serving institutions, which would remove the cost barrier.


This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More information on Chapter Skim is available.