GAN (Generative Adversarial Network)
A framework where two neural networks – a generator and a discriminator – compete, leading to the creation of realistic synthetic data.
Common AI terms beginning with G, defined for advertising professionals.
Find the Flux+Form AI glossary & dictionary to help you make sense of common AI terms. Below you can find an AI Glossary & Dictionary for “G”.
A framework where two neural networks – a generator and a discriminator – compete, leading to the creation of realistic synthetic data.
A common continuous probability distribution characterised by its bell shape; many models assume normality in errors.
The ability of a model to perform well on new, unseen data rather than only on its training set.
A class of AI models that can produce new content such as text, images, video or code by learning underlying patterns in data
Training a model on a large corpus of unlabelled data to learn general features before fine‑tuning on specific tasks.
An optimization method inspired by natural selection that evolves solutions by selection, crossover and mutation.
The practice of optimizing content and brand authority so AI-powered search engines like ChatGPT, Perplexity, and Google AI Overviews recommend your brand in their responses — not just rank it in traditional search.
Serving ads or content to users based on their geographic location, often combined with machine‑learning models to refine targeting.
A metric used in decision trees to measure how often a randomly chosen element would be incorrectly classified.
Searching for the best solution across the entire parameter space rather than settling for a local optimum.
Using graphics processing units to speed up parallelizable computations in AI workflows.
An algorithm for minimizing a function by iteratively moving in the direction of the steepest decrease.
A neural architecture designed to work directly with graph‑structured data, such as social networks or knowledge graphs.
The answering stage of a retrieval system that draws on a knowledge graph of connected facts rather than just similar-looking text, letting it reason across relationships.
A strategy that makes the locally optimal choice at each step with the hope of finding a global optimum.
Systematically searching through a parameter space to find the best combination of hyperparameters for a model.
The accurate, verified data used to train and evaluate models, often collected through human annotation.
Experimenting across marketing channels and product development to rapidly grow a customer base, increasingly supported by AI analytics.
Rules and limits placed around an AI system to keep its inputs and outputs safe, on-policy, and on-brand.
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