Summary of Towards General Industrial Intelligence: a Survey Of Continual Large Models in Industrial Iot, by Jiao Chen et al.
Towards General Industrial Intelligence: A Survey of Continual Large Models in Industrial IoT
by Jiao Chen, Jiayi He, Fangfang Chen, Zuohong Lv, Jianhua Tang, Weihua Li, Zuozhu Liu, Howard H. Yang, Guangjie Han
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 Industrial AI is transitioning from traditional deep learning models to large-scale transformer-based architectures, and the Industrial Internet of Things (IIoT) plays a crucial role. The survey explores the integration of IIoT with large models (LMs) and their potential applications in industrial environments. We focus on four primary types of industrial LMs: language-based, vision-based, time-series, and multimodal models. The lifecycle of LMs is segmented into four critical phases: data foundation, model training, model connectivity, and continuous evolution. The survey highlights how IIoT provides abundant and diverse data resources, supporting the training and fine-tuning of LMs. Additionally, IIoT offers an efficient training infrastructure in low-latency and bandwidth-optimized environments. The deployment advantages of LMs within IIoT emphasize IIoT’s role as a connectivity nexus fostering emergent intelligence through modular design, dynamic routing, and model merging to enhance system scalability and adaptability. Finally, the survey demonstrates how IIoT supports continual learning mechanisms, enabling LMs to adapt to dynamic industrial conditions and ensure long-term effectiveness. This paper underscores IIoT’s critical role in the evolution of industrial intelligence with large models, offering a theoretical framework and actionable insights for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Industrial AI is shifting from traditional deep learning models to large-scale transformer-based architectures, and the Industrial Internet of Things (IIoT) plays an important part. This survey looks at how IIoT works with these big models and what they can do in industrial settings. It talks about four main types of big models: language-based, vision-based, time-series, and multimodal models. The survey shows how IIoT provides lots of different data to train and fine-tune the big models. It also talks about how IIoT helps train these models efficiently by giving them what they need in terms of speed and bandwidth. The survey also highlights how deploying these big models within IIoT can help make systems more adaptable and scalable. Finally, the survey shows how IIoT can help big models learn new things over time, so they can adapt to changing industrial conditions and stay effective in the long run. This paper is important because it helps us understand how IIoT can help with the evolution of industrial intelligence using these big models. |
Keywords
» Artificial intelligence » Continual learning » Deep learning » Fine tuning » Time series » Transformer
