Which AI learning method is designed to replicate behaviors, inferences, or decisions demonstrated by a collection of samples?

Which AI learning method is designed to replicate behaviors, inferences, or decisions demonstrated by a collection of samples?

• A. Supervised learning
• B. Self-supervised learning
• C. Mask learning
• D. Generative AI

Answer: (A)

Replicating behaviors, inferences, or decisions demonstrated by a collection of samples is the goal of supervised learning. In this approach, each example provides an input and the desired output, so the model learns a mapping from inputs to outputs based on those labeled instances. As training progresses, it captures the decision patterns shown in the data and applies them to new, unseen inputs, producing outcomes that reflect the demonstrated behaviors. Self-supervised learning uses unlabeled data to learn representations by solving built-in tasks; it doesn’t rely on explicit input-output pairs to mimic specific decisions. Mask learning, a common self-supervised technique, focuses on predicting missing parts of data rather than imitating observed decisions. Generative AI covers models that create new content, which can involve broader generation beyond simply reproducing samples. Since the description emphasizes reproducing decisions demonstrated in the data, supervised learning is the best fit.

Replicating behaviors, inferences, or decisions demonstrated by a collection of samples is the goal of supervised learning. In this approach, each example provides an input and the desired output, so the model learns a mapping from inputs to outputs based on those labeled instances. As training progresses, it captures the decision patterns shown in the data and applies them to new, unseen inputs, producing outcomes that reflect the demonstrated behaviors. Self-supervised learning uses unlabeled data to learn representations by solving built-in tasks; it doesn’t rely on explicit input-output pairs to mimic specific decisions. Mask learning, a common self-supervised technique, focuses on predicting missing parts of data rather than imitating observed decisions. Generative AI covers models that create new content, which can involve broader generation beyond simply reproducing samples. Since the description emphasizes reproducing decisions demonstrated in the data, supervised learning is the best fit.