Multiple Task

Multiple task learning (MTL) aims to improve efficiency and generalization by training a single model to perform multiple related tasks simultaneously, rather than training separate models for each. Current research focuses on developing efficient architectures and algorithms, such as those incorporating Mixture-of-Experts, Low-Rank Adaptation, and hierarchical structures, to handle diverse tasks and data types, including text, images, and sensor data. These advancements are significant because they offer improved resource utilization, enhanced generalization capabilities, and potential for more robust and adaptable AI systems across various applications, from natural language processing to robotics.