Multitask is a term in artificial intelligence (AI) and machine learning (ML) that refers to the ability of an algorithm, model, or system to perform more than one task at the same time or within the same framework. Instead of training separate models for each individual task, a multitask approach enables a single model to learn and make predictions about several tasks, often by sharing representations or parameters across them.
In practice, multitask learning commonly involves training a neural network to solve a set of related problems simultaneously. For instance, in natural language processing (NLP), a model might be trained to perform sentiment analysis, named-entity recognition, and part-of-speech tagging at the same time. The benefit is that the knowledge gained from one task can help improve the model’s performance on the others—a concept known as inductive transfer. Multitask models typically share some layers (for extracting general features) and have task-specific layers (for task-specific outputs) on top.
Multitask approaches are especially useful when tasks are related or when data for some tasks is limited. By learning from multiple objectives, the model can generalize better and avoid overfitting to a single task. For example, a multitask image recognition system might simultaneously classify objects and detect their locations, which can help the model understand images more holistically.
There are several strategies for designing multitask systems. The most common is hard parameter sharing, where the early layers of the model are shared across all tasks, while the later layers are task-specific. Another method is soft parameter sharing, where separate models are regularized to keep their parameters similar. The choice of strategy depends on how related the tasks are and how much data is available for each.
Multitask learning is not without challenges. If the tasks are too different, sharing parameters can actually hurt performance—a problem known as negative transfer. It’s important to carefully select which tasks to combine and to consider the balance between them during training. Additionally, adjusting loss functions so that no single task dominates the learning process is key to successful multitask models.
Multitask capabilities are increasingly important in modern AI, especially with the rise of large-scale models that are expected to handle a wide range of tasks with minimal retraining. Multitask learning is a foundational concept behind many advances in transfer learning, multitask reinforcement learning, and even the development of general-purpose AI systems.
