In the field of AI/ML, zero-shot refers to the ability of a model to perform a task or recognize a concept that it has never been explicitly trained on. This means that the model can generalize to new situations or tasks without any additional training or input.

For example, if a natural language processing (NLP) model is trained to translate English to Spanish, it may be able to perform a zero-shot translation from English to French, even though it has never been trained on that task. This is because the model has learned general language rules and structures that can be applied to other languages.

Similarly, a computer vision model trained to recognize certain objects in images may be able to perform a zero-shot recognition of a new object that it has never seen before, based on its understanding of the general features and characteristics of objects.

Zero-shot learning is an important area of research in AI/ML because it enables models to be more flexible and adaptable to new situations and tasks, without the need for extensive training data or human intervention.
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