Multitask ML System

Multitask machine learning (ML) systems aim to improve model performance and efficiency by training a single model to perform multiple related tasks simultaneously. Current research focuses on optimizing task selection and aggregation within these systems, exploring architectures like transformers and convolutional neural networks, and developing algorithms like adaptive online learning methods to handle diverse and dynamically changing task sets. This approach offers significant potential for accelerating scientific workflows, such as in chemical structure elucidation and financial natural language processing, by leveraging shared knowledge across tasks and reducing computational costs compared to training separate models for each task.