Which one of these ways is NOT how AI learns?1 pointSupervised LearningReinforcement LearningProactive LearningUnsupervised Learning

To determine which one of these ways is NOT how AI learns, we need to analyze each option:

1. Supervised Learning: In supervised learning, AI learns from labeled data, where it is provided with input-output pairs and tries to learn the mapping between them. This is a common method used in various AI applications.

2. Reinforcement Learning: In reinforcement learning, AI learns through trial and error. It interacts with an environment and receives feedback in the form of rewards or penalties based on its actions. Over time, it learns to maximize its rewards by making better decisions.

3. Proactive Learning: Proactive learning is a term that is not commonly used in the context of AI learning. It is not a well-defined method, and therefore, it is unlikely to be a way in which AI learns.

4. Unsupervised Learning: In unsupervised learning, AI learns from unlabeled data. It tries to find patterns or structures in the data without any specific guidance or labels. This method is often used for tasks such as clustering or dimensionality reduction.

Based on the analysis, the option that is NOT how AI learns is "Proactive Learning."