Summary of Artificial Intelligence in Industry 4.0: a Review Of Integration Challenges For Industrial Systems, by Alexander Windmann and Philipp Wittenberg and Marvin Schieseck and Oliver Niggemann
Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems
by Alexander Windmann, Philipp Wittenberg, Marvin Schieseck, Oliver Niggemann
First submitted to arxiv on: 28 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 comprehensive review aims to identify key challenges hindering the widespread adoption of Artificial Intelligence (AI) in manufacturing, particularly in Cyber-Physical Systems (CPS). The authors analyze recent literature, including standards and reports, highlighting system integration, data-related issues, workforce concerns, and trustworthy AI as major obstacles. A quantitative analysis reveals specific challenges that require further investigation from academics. Existing solutions are briefly discussed, along with proposed avenues for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI is helping Industry 4.0 by analyzing big data from Cyber-Physical Systems (CPS). This can improve things like maintenance and planning in factories. But many companies aren’t using AI yet because of some big problems. The authors looked at lots of recent reports and papers to figure out what’s going wrong. They found that it’s hard to make all the different systems work together, there are issues with the data itself, and people worry about their jobs changing. They also want to make sure that AI is trustworthy. The paper talks about some ways to fix these problems and suggests new areas for researchers to explore. |
