Discover how reinforcement learning trains AI systems using rewards and penalties. Learn the key differences between supervised, unsupervised, and reinforcement learning methods.
Table of Contents
Question
Aditi is training an AI system in which it is penalized for answers that are largely wrong and rewarded for answers that are largely correct.
What type of machine learning is she using?
A. Structured learning
B. Unsupervised learning
C. Supervised learning
D. Reinforcement learning
Answer
D. Reinforcement learning
Explanation
Aditi is using reinforcement learning, in which the machine learns through trial and error. For each answer that’s largely wrong, the machine is penalized. But for each answer that’s largely correct, the algorithms are rewarded.
Reinforcement learning is a type of machine learning where an AI system learns to make decisions by receiving rewards or penalties based on its actions in an environment. In the given scenario, Aditi’s AI system is penalized for largely wrong answers and rewarded for largely correct answers. This aligns with the concept of reinforcement learning, where the AI agent learns to maximize its rewards and minimize penalties through trial and error.
Here’s a brief overview of the other machine learning types mentioned in the options:
A. Structured learning: This is not a commonly used term in machine learning. It might refer to learning from structured data, but it does not accurately describe the scenario presented.
B. Unsupervised learning: In this type of learning, the AI system learns patterns and relationships from unlabeled data without explicit feedback. It is not applicable to the given scenario, as Aditi’s system receives feedback in the form of rewards and penalties.
C. Supervised learning: This type of learning involves training an AI system using labeled data, where the correct answers are provided during training. While supervised learning uses explicit feedback, it does not typically involve a reward-penalty system like the one described in the question.
In summary, reinforcement learning best fits the description of Aditi’s AI system, which learns through a system of rewards and penalties based on the correctness of its answers.
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