# Rethinking industrial artificial intelligence: a unified foundation framework

arXiv: https://arxiv.org/abs/2504.01797 · HTML: https://mlsanity.com/paper/2504.01797/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 72 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 30 of 31)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://mlsanity.com/paper/2405.04434/) (2024)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond](https://mlsanity.com/paper/2103.10689/) (2021)
- [Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models](https://mlsanity.com/paper/2208.07852/) (2022)
- [Can Large Language Models Understand Real-World Complex Instructions?](https://mlsanity.com/paper/2309.09150/) (2023)
- [A Comprehensive Survey on Automatic Knowledge Graph Construction](https://mlsanity.com/paper/2302.05019/) (2023)
- [Multimodal learning with graphs](https://mlsanity.com/paper/2209.03299/) (2022)
- [TransOMCS: From Linguistic Graphs to Commonsense Knowledge](https://mlsanity.com/paper/2005.00206/) (2020)
- [Deep Learning in Industrial Internet of Things: Potentials, Challenges, and Emerging Applications](https://mlsanity.com/paper/2008.06701/) (2020)
- [A Survey of Deep Causal Models and Their Industrial Applications](https://mlsanity.com/paper/2209.08860/) (2022)
- [A Unified Industrial Large Knowledge Model Framework in Industry 4.0 and Smart Manufacturing](https://mlsanity.com/paper/2312.14428/) (2023)
- [Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review](https://mlsanity.com/paper/2012.09049/) (2020)
- [Multi-modal Machine Learning for Vehicle Rating Predictions Using Image, Text, and Parametric Data](https://mlsanity.com/paper/2305.15218/) (2023)
- [Multi-condition multi-objective optimization using deep reinforcement learning](https://mlsanity.com/paper/2110.05945/) (2021)
- [Large Scale Foundation Models for Intelligent Manufacturing Applications: A Survey](https://mlsanity.com/paper/2312.06718/) (2023)
- [Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management](https://mlsanity.com/paper/2110.04256/) (2021)
- [gBuilder: A Scalable Knowledge Graph Construction System for Unstructured Corpus](https://mlsanity.com/paper/2208.09705/) (2022)

## Built on by (most cited first, 1 of 1)

- [Engineering Artificial Intelligence: Framework, Challenges, and Future Direction](https://mlsanity.com/paper/2504.02269/) (2025)

## Abstract (arXiv)

Recent advancements in industrial artificial intelligence (AI) are reshaping the industry by driving smarter manufacturing, predictive maintenance, and intelligent decision-making. However, existing approaches often focus primarily on algorithms and models while overlooking the importance of systematically integrating domain knowledge, data, and models to develop more comprehensive and effective AI solutions. Therefore, the effective development and deployment of industrial AI require a more comprehensive and systematic approach. To address this gap, this paper reviews previous research, rethinks the role of industrial AI, and proposes a unified industrial AI foundation framework comprising three core modules: the knowledge module, data module, and model module. These modules help to extend and enhance the industrial AI methodology platform, supporting various industrial applications. In addition, a case study on rotating machinery diagnosis is presented to demonstrate the effectiveness of the proposed framework, and several future directions are highlighted for the development of the industrial AI foundation framework.