few-shot-learning

Vocabulary Word

Definition

'Few-shot learning' refers to an AI's ability to learn from a small amount of data. It's like training a machine to identify animals by showing only 5 pictures instead of hundreds.

Examples in Different Contexts

In natural language processing (NLP), 'few-shot learning' is used for understanding or generating text based on limited input. An NLP researcher might discuss, 'We've applied few-shot learning to our language model, allowing it to grasp new concepts from a few sentences.'

Practice Scenarios

Scenario:

Our conversational AI needs to understand colloquial language better. Improving its comprehension of informal expressions can enhance user experience.

Response:

Few-shot learning might help in this situation. By training the AI with a small dataset of colloquial expressions, we can improve its comprehension significantly.

Tech

Scenario:

Developing an AI model that can recognize handwritten text accurately can be quite data-heavy. There might be a more efficient way.

Response:

I agree. Few-shot learning would be efficient in that scenario. It might provide a similar level of accuracy even with fewer data.