Technical Analysis of Data-Centric and Model-Centric Artificial Intelligence
- Authors
- Majeed, Abdul; Hwang, Seong Oun
- Issue Date
- Nov-2023
- Publisher
- IEEE COMPUTER SOC
- Keywords
- Analytical models; Market research; Trajectory; Artificial intelligence; Predictive maintenance
- Citation
- IT PROFESSIONAL, v.25, no.6, pp 62 - 70
- Pages
- 9
- Journal Title
- IT PROFESSIONAL
- Volume
- 25
- Number
- 6
- Start Page
- 62
- End Page
- 70
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90746
- DOI
- 10.1109/MITP.2023.3322410
- ISSN
- 1520-9202
1941-045X
- 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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