What is the definition of synthetic data in the context of Generative AI?

Synthetic data refers to data that is generated through algorithms and simulations to replicate real-world scenarios and conditions. In the context of Generative AI, synthetic data is created to enable the training of machine learning models without relying on real-world data, which can be limited, costly, or subject to privacy concerns. This type of data can be tailored to include specific characteristics or patterns that mimic those found in actual datasets, allowing for more robust testing and development.

Generating synthetic data is particularly useful in situations where obtaining real data is challenging or where diversity and variability in the dataset are necessary to improve model generalization and performance. By using synthetic data, researchers and data scientists can leverage comprehensive datasets that encompass a broader range of situations than might be captured in typical real-world data collections.

The other options represent concepts that do not fully align with the definition of synthetic data. While derived data or survey data can provide valuable insights into real-world behavior, synthetic data specifically emphasizes the generation of simulated information rather than relying directly on observed behavior or limited sources.