Teacher forcing is a type of training technique used in machine learning and artificial intelligence (AI) development. It is used to give the AI agent the opportunity to learn from its mistakes, which re-enforces the learning process.

The term ‘teacher forcing’ originated from the concept of an instructor or teacher that guides the student through the learning material. This concept can be applied to AI in machine learning and other AI methods in order to build confidence in the process of learning.

When the AI agent is using teacher forcing, it is provided with a known answer or outcome in order to guide the decision making process. The AI agent can then compare the known answer with the one that it itself has generated – thus the AI is able to learn from the difference between the two.

An example of teacher forcing could be a language AI application that is asked to converse with a user. Whenever the AI makes a mistake, it is provided with the correct output which it can use to correct its own mistakes and gain a better understanding of the language.

Ultimately, teacher forcing is a useful training technique that encourages active, learning-focused feedback within the AI system. By providing AI agents with known answers, the AI can improve its decision making process and make more accurate predictions.

However, as with any machine learning technique, an over-reliance on teacher forcing should be avoided. Constant use of the technique can cause the AI agent to develop an overconfidence which can lead to problems if the AI agent is not able to comprehend the scenario at hand. Thus, the technique should be used sparingly and typically in combination with other learning techniques.

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