Summary of A Cyber Manufacturing Iot System For Adaptive Machine Learning Model Deployment by Interactive Causality Enabled Self-labeling, By Yutian Ren et al.
A Cyber Manufacturing IoT System for Adaptive Machine Learning Model Deployment by Interactive Causality Enabled Self-Labeling
by Yutian Ren, Yuqi He, Xuyin Zhang, Aaron Yen, G. P. Li
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Methodology (stat.ME)
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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 Machine learning (ML) has been shown to boost productivity in many manufacturing settings. To host these ML applications, various software and Industrial Internet of Things (IIoT) systems have been proposed for manufacturing purposes, enabling real-time intelligence. A recent interactive causality enabled self-labeling method was developed to enhance adaptive ML applications in cyber-physical systems, particularly in manufacturing, by automatically adapting and personalizing ML models after deployment to mitigate data distribution shifts. The unique features of this self-labeling method require a novel software system to support dynamism at multiple levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning can make factories more efficient. To use ML, special computer systems have been created for factories. One new way to improve ML is called “self-labeling.” This means that the ML model can change and adapt after it’s already being used. This helps when data changes over time. The self-labeling method needs a special software system to work. |
Keywords
» Artificial intelligence » Machine learning
