AI-DRIVEN SOLUTIONS FOR ROBUST DATA GOVERNANCE: A FOCUS ON GENERATIVE AI APPLICATIONS
Keywords:
Artificial Intelligence (AI), Decision-making, Generative AIAbstract
Artificial intelligence (AI) systems are taught to use enormous amounts of data in order to solve difficult issues and acquire the ability to carry out particular tasks. These tasks include prediction, classification, recognition, decision-making, and other similar activities. When compared to the data-centric method, the model-centric approach has been the primary focus of artificial intelligence research over the course of the past three decades. To improve performance, the focus in the model-centric method is on enhancing the code or model architecture. On the other hand, with the data-centric approach to artificial intelligence, the focus is on enhancing the dataset to improve performance. Artificial intelligence thrives on data. Because of this, there has been a recent movement within the artificial intelligence community toward data-centric AI as opposed to model-centric AI. In this work, a comprehensive and critical study of the current state of research in data-centric artificial intelligence is presented. Through this analysis, insights into the most recent advancements in this fast-developing subject are presented. The paper addresses the important difficulties and opportunities that need to be addressed to improve the performance of AI systems. This is accomplished by putting an emphasis on the significance of data in artificial intelligence. In conclusion, this article provides some suggestions for research prospects in the field of data-centric artificial intelligence.
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Copyright (c) 2023 Ankush Reddy Sugureddy (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
