Synthetic Data
intermediateTraining data generated by AI models rather than collected from real-world sources. Used to augment datasets, protect privacy, or create examples for rare scenarios.
Overview
Synthetic data is artificially generated information that mimics real-world data patterns. AI models can create training examples, augment limited datasets, or generate privacy-preserving alternatives to sensitive data. The technique is powerful but risky. High-quality synthetic data can dramatically expand training sets, especially for rare cases. However, if not carefully managed, it can introduce biases, reduce diversity, or cause model collapse. Modern approaches use careful filtering, human verification, and diversity metrics to ensure synthetic data improves rather than degrades model quality.
Key Concepts
Data Augmentation
Using AI to expand training datasets with realistic variations.
Privacy Preservation
Generating synthetic alternatives to sensitive personal data.
Quality Filtering
Ensuring synthetic data maintains high standards and diversity.