Data Centric

Data-centric AI prioritizes high-quality data as the primary driver of successful machine learning, shifting focus from solely model optimization. Current research emphasizes improving data quality through techniques like data augmentation, feature engineering, and careful dataset curation, often employing transformer-based models and other deep learning architectures for analysis. This approach is crucial for addressing issues like algorithmic bias, improving model robustness and generalization, and ultimately leading to more reliable and trustworthy AI systems across diverse applications, from healthcare and finance to earth observation and natural language processing.