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

Robotics

Scenario:

We're targeting to automate the new product line using robotics. Keeping in mind complexity, we need to devise an efficient training process.

Response:

We should indeed consider few-shot learning for our robotic training. It could reduce the amount of data needed for effective machine learning.

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.