Summary of Model-based Data-centric Ai: Bridging the Divide Between Academic Ideals and Industrial Pragmatism, by Chanjun Park et al.
Model-Based Data-Centric AI: Bridging the Divide Between Academic Ideals and Industrial Pragmatism
by Chanjun Park, Minsoo Khang, Dahyun Kim
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 dichotomy between Data-Centric AI and Model-Agnostic AI approaches, highlighting the disparity between high-quality data demands in academia versus algorithmic flexibility prioritization in industry. The study argues that while Data-Centric AI focuses on high-quality data for model performance, Model-Agnostic AI prioritizes flexibility over data quality considerations. This distinction reveals that academic standards do not meet industrial demands, leading to potential pitfalls when deploying academic models in real-world settings. The paper presents challenges and strategies for bridging the gap, proposing a novel paradigm: Model-Based Data-Centric AI, which integrates model considerations into data optimization processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how data is used differently in schools versus companies. It shows that when making AI models, some people focus on getting really good data, while others prioritize being able to use the same approach with different types of data. The study finds that these two approaches often don’t work well together, which can cause problems when trying to use academic research in real-world situations. The paper suggests ways to fix this and proposes a new way of thinking about AI models and data quality that combines the best of both worlds. |
Keywords
» Artificial intelligence » Optimization
