What is Synthetic Data?
Synthetic data is information—such as text, images, or tabular records—that is artificially generated by computer algorithms or AI models, rather than collected from real-world events or human interactions.
How does it work?
When real data is scarce, expensive, or restricted by privacy laws, developers use advanced models to create synthetic replacements. For example, generating thousands of realistic but fake patient health records that preserve statistical accuracy without exposing real individuals' private medical history.
What are the benefits and limitations?
- Benefits: Expanding limited datasets, simulating rare edge-case situations for testing, and reducing some privacy exposure during training.
- Limitations: Synthetic data risks reinforcing model-generated errors (model collapse) and often fails to capture the true, messy diversity of the real world.
What is a common misconception?
Do not claim that synthetic data is automatically private, unbiased, or completely safe. If the model generating the data was trained on biased human data, the resulting synthetic data will likely amplify those exact same biases.
Why does it matter?
As AI companies exhaust the supply of high-quality human-written text on the internet, synthetic data is becoming the primary method to continue scaling and improving models for the next generation of AI.