Summary of Dsdl: Data Set Description Language For Bridging Modalities and Tasks in Ai Data, by Bin Wang et al.
DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI Data
by Bin Wang, Linke Ouyang, Fan Wu, Wenchang Ning, Xiao Han, Zhiyuan Zhao, Jiahui Peng, Yiying Jiang, Dahua Lin, Conghui He
First submitted to arxiv on: 28 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Programming Languages (cs.PL)
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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 The proposed Dataset Description Language (DSDL) framework aims to streamline dataset processing in artificial intelligence by providing a unified standard for expressing datasets of different modalities and structures. The DSDL framework adheres to three key principles: generic, portable, and extensible, allowing it to facilitate the dissemination of AI data and easily extend to new modalities and tasks. By reducing the workload required for users to process, disseminate, and utilize AI data, the standardized specifications of DSDL aim to improve the efficiency of AI development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a framework called Dataset Description Language (DSDL) to help people use AI data more easily. Right now, it’s hard to share and work with different types of data because they are in different formats. The new language helps solve this problem by giving a standard way to describe all kinds of data. This makes it easier for people to find, use, and share data, which can help make AI development faster and more efficient. |
