👉 Supervision computing is an approach to artificial intelligence where a model is trained using labeled data, meaning each input is paired with the correct output or label. This process allows the model to learn the mapping between inputs and outputs, enabling it to make predictions or classifications on new, unseen data. Unlike unsupervised learning, which deals with unlabeled data and seeks to find hidden patterns or structures, supervision computing relies on explicit guidance through labeled examples. This method is widely used in various applications such as image recognition, natural language processing, and speech recognition, where the goal is to build models that can accurately predict or classify data based on learned patterns from labeled datasets.
