AI Glossary
Synthetic Data
Artificially generated data that mimics real-world data without being collected from actual events or individuals.
Synthetic Data
Overview
Machine learning systems depend on data.
However, obtaining large amounts of real-world data is not always possible. Privacy concerns, regulatory restrictions, cost, and limited availability can all make data collection difficult.
This challenge has led to the growing use of synthetic data.
Synthetic data is artificially generated information that resembles real-world data without directly copying it. The goal is to create datasets that preserve useful patterns while reducing privacy risks and expanding training opportunities.
A helpful way to think about synthetic data is a flight simulator.
Pilots can learn and practice in a simulated environment before flying a real aircraft. Similarly, AI systems can learn from synthetic data before being exposed to real-world situations.
Modern generative AI techniques, including Generative Adversarial Networks (GANs) and Diffusion Models, are often used to create synthetic datasets.
As privacy and data availability become increasingly important, synthetic data is expected to play a larger role in AI development.
Why It Matters
Synthetic data helps organizations train AI systems while reducing privacy concerns and data collection challenges.
Real-World Example
A healthcare organization may use synthetic patient records to train AI systems without exposing real patient information.