Overcoming challenges in artificial intelligence training: data limitations, computational costs and model robustness

Abstract

This paper explores challenges in AI training, focusing on data limitations, computational costs, and the need for robust models. It discusses innovative solutions like synthetic data generation, efficient neural architectures, and robustness techniques, highlighting the importance of AI model interpretability.