Multi-task learning in Machine Learning

In most machine learning contexts, we are concerned with solving a single task at a time. Regardless of what that task is, the problem is typically framed as using data to solve a single task or optimize a single metric at a time. However, this approach will eventually hit a performance ceiling, oftentimes due to the size of the data-set or the ability of the model to learn meaningful representations from it. Multi-task learning, on the other hand, is a machine learning approach in which we try to learn multiple tasks simultaneously, optimizing multiple loss functions at once. Rather than training independent models for each task, we allow a single model to learn to complete all of the tasks at once. In this process, the model uses all of the available data across the different tasks to learn generalized representations of the data that are useful in multiple contexts.