Multi-task learning is a machine learning technique where a model is trained to perform multiple tasks simultaneously, leveraging the shared underlying structures and patterns across tasks to improve overall performance. This approach is particularly useful when there is limited data available for each individual task or when tasks are related in some way. Multi-task learning has been applied in various fields such as natural language processing, computer vision, and genomics, among others. By training on multiple tasks at once, the model can learn to generalize better and make predictions that are more accurate and robust. Overall, multi-task learning is a powerful technique that can improve the efficiency and effectiveness of machine learning models by allowing them to leverage shared information across tasks.