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Image by Maddison McMurrin

FAIR in ML, AI Readiness, & Reproducibility (FARR) Workshop

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The FAIR in ML, AI Readiness, & Reproducibility Research Coordination Network (FARR RCN) computer scientists, geoscientists, research data practitioners, geosciences data and tool repositories / providers, and computing infrastructure providers and research tool builders to participate in FARR's in-person workshop on October 9-10, 2024 at the AGU Conference Center in Washington DC. Communities outside of geosciences with similar challenges, as well as industry, government, and non-profits with a stake in these topics are also encouraged to attend.   

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Who should attend?

  • Are you interested in learning about or working towards AI readiness?

  • Do you want to share AI models, or use others' models?

  • Are you concerned about reproducibility of work that uses AI models?

  • Do you have experiences in these areas that you think others would benefit from?

If you are interested in those questions, this workshop will be a good opportunity to connect with others.

 

Purpose:

The "FARR Workshop” aims to make advancement in the areas of AI Readiness, AI Reproducibility, and the intersection of the FAIR Principles and ML through

  • Spurring new or deepened collaborations

  • Sharing best practices and lessons learned

  • Exploring research gaps and priorities 

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Participate

 

Call For Posters - Abstracts are due by September 18, 2024

The FARR RCN seeks posters focused on AI Readiness, AI Reproducibility and FAIR Principles & ML, especially in the geosciences, including but not limited to topics such as:

  • Foundation models (FM), especially as they relate to building FMs, methods for benchmarking, maintaining, extending, data formats, and related considerations

  • Data-centric AI, especially as it relates to research priorities and signals for data repositories and resource providers

  • Using ML and Knowledge Engineering to add context or structure to data 

  • Applying the FAIR principles to data, workflows, and models for AI/ML, and techniques for automation and validation

  • What AI Readiness means for geoscience repositories and related providers: challenges, success stories, and lessons learned

  • Community approaches to AI reproducibility

  • AI reproducibility and refactoring for LLMs and Gen-AI

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Logistics

Agenda   (print version)

 

Attendance at the meeting will be in-person only. There is no registration fee for this meeting. Breakfast and lunch will be provided. Travel support will be made available for a limited number of early career researchers. 

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Posters

Posters should be no larger than 45 inches x 45 inches (114 cm x 114 cm).

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Accommodations: reservations due by September 18, 2024, while supplies last

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Registration

There is no registration fee, but participants should register by September 18, 2024 to allow for planning logistics such as space, food and name badges. Register HERE

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Community Participation Guidelines

 

Venue

AGU Conference Center

2000 Florida Ave. NW, Washington, D.C. 20009

Directions to the AGU Conference Center

Closest Metro station: Dupont Circle

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