Which AI category learns from labeled data to estimate probabilities and make predictions?

• A. Rule-Based
• B. Analytical AI
• C. Generative AI
• D. Hybrid AI

Answer: (B)

Supervised learning is being tested here. In this approach, a model is trained on data where each example has a label, teaching the model to estimate how likely different outcomes are given the input and to predict the most probable outcome for new data. This learning-from-labeled-data process underpins the ability to assign probabilities and make predictions on unseen inputs. Among the options, Analytical AI fits this description as a data-driven, prediction-focused category. Rule-based systems don’t learn from data and rely on fixed rules. Generative AI aims to create new data and often centers on modeling the data distribution rather than directly predicting labels from labeled training data. Hybrid AI combines methods, but the essential match to learning from labeled data to estimate probabilities and make predictions is Analytical AI.