{"title":"Technical Analysis of Data-Centric and Model-Centric Artificial Intelligence","authors":"Abdul Majeed, Seong Oun Hwang","doi":"10.1109/mitp.2023.3322410","DOIUrl":null,"url":null,"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.","PeriodicalId":49045,"journal":{"name":"IT Professional","volume":"9 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IT Professional","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/mitp.2023.3322410","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
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.
IT ProfessionalCOMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
5.00
自引率
0.00%
发文量
111
审稿时长
>12 weeks
期刊介绍：
IT Professional is a technical magazine of the IEEE Computer Society. It publishes peer-reviewed articles, columns and departments written for and by IT practitioners and researchers covering:
practical aspects of emerging and leading-edge digital technologies,
original ideas and guidance for IT applications, and
novel IT solutions for the enterprise.
IT Professional’s goal is to inform the broad spectrum of IT executives, IT project managers, IT researchers, and IT application developers from industry, government, and academia.
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