Technical Analysis of Data-Centric and Model-Centric Artificial Intelligence

• Published in: IT professional Vol. 25; no. 6; pp. 62 - 70
• Main Authors: Majeed, Abdul, Hwang, Seong Oun
• Format: Journal Article
• Language: English
• Published: Washington IEEE Computer Society 01.11.2023
• Subjects: Algorithms; Artificial intelligence; Predictive maintenance
• ISSN: 1520-9202; 1941-045X
• DOI: 10.1109/MITP.2023.3322410

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.