Summary of Ai Competitions and Benchmarks, Practical Issues: Proposals, Grant Money, Sponsors, Prizes, Dissemination, Publicity, by Magali Richard (timc-mage) et al.
AI Competitions and Benchmarks, Practical issues: Proposals, grant money, sponsors, prizes, dissemination, publicity
by Magali Richard, Yuna Blum, Justin Guinney, Gustavo Stolovitzky, Adrien Pavão
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As machine learning educators, we’ll explore the pragmatic aspects of organizing AI competitions. The chapter starts by discussing strategies to incentivize participation, including effective communication techniques, aligning with trending topics in the field, and structuring awards. It also touches upon potential recruitment opportunities and more. Next, it shifts focus to community engagement, organizational best practices, and disseminating challenge outputs effectively. Finally, the chapter addresses logistics, covering costs, required manpower, and resource allocation for managing and executing a challenge. By examining these practical problems, readers will gain actionable insights into organizing AI competitions from inception to completion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI competitions can be tricky to organize! This paper talks about making them happen by sharing strategies to get people involved, like talking clearly and using popular topics. It also shares ideas for giving out prizes and finding volunteers. The chapter then looks at how to keep the community engaged and share the results of the competition. Finally, it covers the behind-the-scenes work, including costs, staff needed, and planning resources. By reading this, you’ll learn what it takes to run a successful AI competition. |
Keywords
* Artificial intelligence * Machine learning
