Summary of Data Issues in Industrial Ai System: a Meta-review and Research Strategy, by Xuejiao Li et al.
Data Issues in Industrial AI System: A Meta-Review and Research Strategy
by Xuejiao Li, Cheng Yang, Charles Møller, Jay Lee
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper explores the current state of artificial intelligence (AI) adoption in industries and identifies significant data issues that hinder its implementation. By mapping out these issues, researchers can develop strategies to address them. The study conducts a meta-review of existing literature on data issues and methods for implementing industrial AI, categorizing 72 identified issues into various stages of the data lifecycle. It also analyzes the data requirements of different AI algorithms and proposes a data management framework to resolve data issues at every stage. This paper enriches our understanding of data usability and usefulness in industrial AI and provides guidelines for professionals navigating this complex landscape. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Industrial artificial intelligence (AI) is becoming increasingly important, but many industries are struggling to adopt it effectively. One major problem is getting the right data. To solve this, researchers looked at existing studies on data issues and methods for implementing industrial AI. They found 72 problems and grouped them into different stages of how data moves through a system. They also studied what kind of data each type of AI needs. Then, they came up with a plan to manage data better throughout the process. This research can help us understand how to make AI work better in industries. |
