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A GAN is a type of machine learning model that creates realistic data by learning from patterns found in existing datasets. It uses unsupervised learning and two neural networks (machine learning models that mimic the complex structure of a human brain) working together: one tries to create new data, while the other checks if that data looks real or not. See also Machine learning and Unsupervised learning.
Computational techniques that generate artificial data or model complex systems by learning patterns from existing data rather than following fixed rules. Examples include agent-based models, which simulate the behaviour of individuals within a system, and generative AI models, which produce new, statistically plausible data. For limitations of some generative AI approaches, see AI hallucinations.
A technique that uses location-based data, such as GPS data, satellite imagery, and IoT sensor data, to interpret geographic patterns, relationships, and trends.