Zero-Shot Learning
The capability of a trained AI model to perform a task it was not explicitly trained on by applying generalised knowledge learned from its training data, without requiring task-specific examples or fine-tuning.
In plain language
An AI doing something it was never specifically taught to do. An AI fluent in English might translate a language it never studied, or classify images of animals it never encountered during training. It uses what it learned more broadly to figure out the new task.
Why this matters
Zero-shot learning affects how quickly and cost-effectively organisations can deploy AI to new use cases. It enables faster time-to-value and lower implementation costs. However, zero-shot performance can be unpredictable, so organisations must test outputs carefully and have fallback processes before deploying zero-shot AI in high-stakes decisions.
Relevance
ImplementationZero-shot capability determines deployment speed and cost but introduces verification risks that must be managed through testing and governance.
Related terms
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