Technical Analysis of Data-Centric and Model-Centric Artificial Intelligence
The artificial intelligence (AI) field is going through a dramatic revolution in terms of new horizons for research and real-world applications, but some research trajectories in AI are becoming detrimental over time. Recently, there has been a growing call in the AI community to combat a dominant r...
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| Published in | IT professional Vol. 25; no. 6; pp. 62 - 70 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Washington
IEEE Computer Society
01.11.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1520-9202 1941-045X |
| DOI | 10.1109/MITP.2023.3322410 |
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| Abstract | The artificial intelligence (AI) field is going through a dramatic revolution in terms of new horizons for research and real-world applications, but some research trajectories in AI are becoming detrimental over time. Recently, there has been a growing call in the AI community to combat a dominant research trend named model-centric AI (MC-AI), which only fiddles with complex AI codes/algorithms. MC-AI may not yield desirable results when applied to real-life problems like predictive maintenance due to limited or poor-quality data. In contrast, a relatively new paradigm named data-centric (DC-AI) is becoming more popular in the AI community. In this article, we discuss and compare MC-AI and DC-AI in terms of basic concepts, working mechanisms, and technical differences. Then, we highlight the potential benefits of the DC-AI approach to foster further research on this recent paradigm. This pioneering work on DC-AI and MC-AI can pave the way to understand the fundamentals and significance of these two paradigms from a broader perspective. |
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| AbstractList | The artificial intelligence (AI) field is going through a dramatic revolution in terms of new horizons for research and real-world applications, but some research trajectories in AI are becoming detrimental over time. Recently, there has been a growing call in the AI community to combat a dominant research trend named model-centric AI (MC-AI), which only fiddles with complex AI codes/algorithms. MC-AI may not yield desirable results when applied to real-life problems like predictive maintenance due to limited or poor-quality data. In contrast, a relatively new paradigm named data-centric (DC-AI) is becoming more popular in the AI community. In this article, we discuss and compare MC-AI and DC-AI in terms of basic concepts, working mechanisms, and technical differences. Then, we highlight the potential benefits of the DC-AI approach to foster further research on this recent paradigm. This pioneering work on DC-AI and MC-AI can pave the way to understand the fundamentals and significance of these two paradigms from a broader perspective. |
| Author | Majeed, Abdul Hwang, Seong Oun |
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| SubjectTerms | Algorithms Artificial intelligence Predictive maintenance |
| Title | Technical Analysis of Data-Centric and Model-Centric Artificial Intelligence |
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