What does the term "synthetic data" refer to in the context of generative AI?

The term "synthetic data" in the context of generative AI refers to artificially generated information that mimics real data. This type of data is created through algorithms and models that learn patterns from existing data, allowing it to simulate the characteristics and statistical properties of real datasets without relying on actual sensitive or proprietary information.

Synthetic data is particularly valuable in areas where obtaining real data can be difficult, expensive, or poses privacy risks. By using generative models, organizations can produce large amounts of realistic data for training machine learning algorithms, testing hypotheses, or performing simulations. This mitigates issues related to data scarcity or privacy concerns while still enabling robust analysis and model development.

The other options do not accurately capture the essence of synthetic data. Data produced from real experiments represents actual observations rather than generated information. Data collected from user interactions refers to real-world actions and behaviors, which can be sensitive and specific to individuals. Data solely sourced from public datasets lacks the aspect of artificial generation, as it involves existing data that is publicly available.