Summary of Data Analysis in the Era Of Generative Ai, by Jeevana Priya Inala et al.
Data Analysis in the Era of Generative AI
by Jeevana Priya Inala, Chenglong Wang, Steven Drucker, Gonzalo Ramos, Victor Dibia, Nathalie Riche, Dave Brown, Dan Marshall, Jianfeng Gao
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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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 A novel paper explores the applications of large language and multimodal models in data analysis, examining how they can be leveraged to enhance various stages of the workflow. The authors investigate design considerations and challenges in developing AI-powered tools that translate high-level user intentions into executable code, charts, and insights. They also discuss human-centered design principles for intuitive interactions, building user trust, and streamlining the AI-assisted analysis workflow across multiple apps. Furthermore, the paper highlights research challenges in developing these systems, including enhancing model capabilities, evaluating and benchmarking, and understanding end-user needs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI-powered tools can revolutionize data analysis by translating user intentions into actionable insights. The authors of this paper investigate how large language and multimodal models can enhance various stages of the data analysis workflow. They discuss design considerations and challenges in developing AI-powered tools that are easy to use and provide accurate results. The paper also highlights research challenges in developing these systems, including enhancing model capabilities and understanding end-user needs. |
