Synthetic Data
Definition
Artificially generated data created by algorithms or AI models rather than collected from real-world events, used to augment training sets or protect privacy.
Synthetic data has become increasingly important in AI development for several reasons: it can address privacy concerns (generating realistic but non-identifying medical or financial data), overcome data scarcity (creating training examples for rare events), reduce bias (generating balanced representations), and lower costs (avoiding expensive data collection and labeling). Techniques for generating synthetic data include GANs, diffusion models, simulation engines, and LLMs (generating synthetic text or instruction-following examples). Companies like Mostly AI and Gretel specialize in synthetic data generation. A notable trend is using LLMs to generate training data for other models — for example, using GPT-4 outputs to train smaller models. Concerns include maintaining statistical fidelity to real data and avoiding "model collapse" when AI is trained recursively on AI-generated content.
Related Terms
Data Augmentation
Techniques for artificially expanding training datasets by creating modified versions of existing da...
Dataset
A structured collection of data organized for training, evaluating, or testing machine learning mode...
GAN (Generative Adversarial Network)
A generative model architecture consisting of two neural networks — a generator and a discriminator ...
Training Data
The dataset used to teach a machine learning model, consisting of examples from which the model lear...