GENERATIVE AI: A TOOL FOR ADDRESSING DATA SCARCITY IN SCIENTIFIC RESEARCH

Abstract

Generative AI, a pivotal advancement in data science, addresses data scarcity by producing high-quality synthetic data that mirrors real-world data. This article explores Generative AI's capabilities, including data augmentation, privacy-preserving anonymization, simulation of rare events, and cost-efficient data collection. Techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are discussed, highlighting their role in creating realistic and diverse synthetic data. The practical applications span healthcare, finance, and climate science, demonstrating Generative AI's transformative potential in enhancing research across various scientific disciplines.