Summary of Collaborative Evolving Strategy For Automatic Data-centric Development, by Xu Yang et al.
Collaborative Evolving Strategy for Automatic Data-Centric Development
by Xu Yang, Haotian Chen, Wenjun Feng, Haoxue Wang, Zeqi Ye, Xinjie Shen, Xiao Yang, Shizhao Sun, Weiqing Liu, Jiang Bian
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning educators writing for a technical audience can rely on this summary: This paper introduces the concept of automatic data-centric development (AD^2), a crucial task in modern AI research. The authors highlight the importance of prioritizing data development over model design and outline the core challenges of AD^2, including task scheduling and implementation capabilities that require domain-expertise. By automating this process, researchers can focus on developing high-quality datasets for machine learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For curious learners or non-technical audiences, here’s a simplified summary: This paper talks about how artificial intelligence (AI) is getting more powerful thanks to lots of good data. The problem is that making this data is hard work and takes up most of the time. Researchers need a way to make this process easier, so they can focus on improving AI models. This paper proposes a new task called AD^2, which tries to solve this problem by automating the data-making process. |
Keywords
» Artificial intelligence » Machine learning
