Monolithic Deep

Monolithic deep learning models, characterized by a single, large network, are increasingly being challenged by research exploring modular and hierarchical alternatives. Current efforts focus on developing more efficient and adaptable architectures, such as Mixture-of-Experts and modular networks with independently trainable components, often leveraging techniques like reinforcement learning to optimize resource allocation and improve performance in resource-constrained environments. This shift towards modularity aims to enhance model interpretability, reduce computational costs, and facilitate the development of more flexible and robust AI systems for diverse applications, including content moderation and robotics.