How do supervised and unsupervised learning differ in AI?

Supervised learning and unsupervised learning are fundamental techniques in AI and machine learning, and the distinction between them lies primarily in the use of labeled data. In supervised learning, algorithms are trained on a labeled dataset, which means that each training example comes with an associated output label. This allows the model to learn the relationship between the input features and the correct output, enabling it to make predictions on new, unseen data based on what it has learned.

In contrast, unsupervised learning operates without labeled data. Instead, the goal is to identify underlying patterns or groupings within the data. It explores the input data to discover structures, such as clusters or associations, without any pre-defined outcomes guiding the training process. This method is particularly useful for tasks like clustering, dimensionality reduction, and anomaly detection, where the true outcomes are not known in advance.

The correct choice highlights the essence of supervised learning's reliance on labeled data to guide its learning process compared to unsupervised learning's exploration of unannotated datasets.