Continuous Learning/Lifelong Learning

Overview

A machine learning paradigm where models are designed to continuously learn from new data and adapt over time, without forgetting previously learned information. This contrasts with traditional models that are typically trained offline and have difficulty incorporating new data without a complete re-training process. Continuous learning models aim to be able to continually update their knowledge base as they are exposed to new data, better approximating real-world learning behavior.

Adaptation and Learning

Continuous learning enables AI models to learn from new data without forgetting what they've already learned.

Real-time Adaptation

This allows models to adapt in real-time, similar to the way real-world learning happens in people.

Adaptable Models

Models can continually improve over time, which makes them much more adaptable in dynamic, real-world environments, unlike traditionally trained models.

Related Concepts

• Machine Learning
• Model Training Dataset
• Data Drift