Skip to main content

Machine Learning and Artificial Intelligence to Advance Earth System Science Opportunities and Challenges: Proceedings of a Workshop (2022) / Chapter Skim
Currently Skimming:

Closing Thoughts
Pages 39-42

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 39...
... Using ML/AI tools offer opportunities to improve predictive models, utilize computing resources in a way that is faster and less costly, improve understanding of the Earth system, and bring together disparate fields of study. For Earth system science applications, participants discussed the importance of benchmark datasets, which allow a quantitative evaluation of ML approaches and a separation of concerns among domain scientists, ML experts, and high-performance computing experts.
Read the entire page →
From page 40...
... Participants explained that these capabilities are not yet being utilized effectively, and open science provides an opportunity to support global creativity and a more broadly inclusive research community. However, making data open is not the same as fully democratizing science -- participants identified other steps that could be useful, including preparing data for analysis, offsetting the cost of experimentation, and bringing communities together to address blind spots and biases to cultivate an inclusive community of practice.
Read the entire page →
From page 41...
... Participants discussed potential roles for federal agencies and professional organizations to address workforce development challenges, strengthen partnerships between academia and the private sector, and expand the accessibility of existing programs like NSF's REUs. USING ML/AI FOR DATA-DRIVEN DECISION MAKING One application of Earth system science is real-time decision making, and participants discussed the role of ML/AI in bringing together physical models, prior knowledge, and datadriven mechanisms to serve the needs of end users.
Read the entire page →
From page 42...
... Participants anticipated that ML/AI methods will ultimately be tools that will be used in combination with, for example, inverse methods and data assimilations, rather than a magic bullet to solve every Earth system science problem. Participants offered actionable steps to make progress, including creating educational pathways so that students and professionals who understand Earth system science can innovate in ML/AI, incorporating ethical and responsible AI into Earth system science applications, institutionalizing the use of open science and open data, pursuing research in how people make decisions, and developing infrastructure to support these innovations.
Read the entire page →

Key Terms

  • advance earth
  • science applications
  • domain scientists
  • data science
  • participants identified

  • earth science
  • prior knowledge
  • existing earth
  • offer opportunities
  • climate science

  • science discipline
  • participants emphasized
  • participants offered
  • science tools
  • participants recognized

  • participants explained
  • science data
  • domain science
  • science pose
  • blind spots

  • socioeconomic asymmetries
  • improve strategies
  • support global
  • broadly inclusive
  • inclusive community

  • utilized effectively
  • learning communities
  • creative teaching
  • creating educational
  • intensive data


    • 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.