In the AI/ML world, zero-shot learning refers to a machine learning technique that allows a model to classify or predict new classes without being explicitly trained on them. This is done by using auxiliary information, such as textual descriptions or embeddings, to help the model make predictions.

Zero-shot learning is a powerful technique that can be used to extend the capabilities of machine learning models. For example, it can be used to create models that can classify or predict new types of images, text, or audio.

Here are some of the benefits of zero-shot learning:

* It can be used to extend the capabilities of machine learning models without requiring additional training data.
* It can be used to create models that are more robust to changes in the data.
* It can be used to create models that are more efficient to train.

Here are some of the challenges of zero-shot learning:

* It can be difficult to find high-quality auxiliary information.
* The accuracy of zero-shot learning models can be lower than models that are explicitly trained on the new classes.
* Zero-shot learning models can be more difficult to interpret than models that are explicitly trained on the new classes.
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