GAN
Also written: GANs
AI systems that create fake data by having two neural networks compete to make them look real.
Generative Adversarial Networks, or GANs, are a type of AI that creates new, realistic content. Think of it like an art forger trying to create a fake painting and an art detective trying to spot the fake. The forger gets better at making fakes, and the detective gets better at spotting them. This competition helps the forger create incredibly convincing fake images, text, or other data.
For example, a GAN could be trained to create realistic photos of people who don't exist. One part of the AI generates faces, and another part tries to tell if the faces are real or fake. As they compete, the generator learns to make faces that are almost impossible to distinguish from real photographs.
Generative Adversarial Networks (GANs) are a class of machine learning frameworks comprising two neural networks, a generator and a discriminator, that compete against each other. The generator's objective is to produce synthetic data that is indistinguishable from real data, while the discriminator's objective is to correctly identify whether a given data sample is real or generated. This adversarial process drives both networks to improve, with the generator becoming adept at creating highly realistic outputs. Tradeoffs include training instability and mode collapse, where the generator produces limited variety of outputs.
GANs are relevant because frontier AI labs like OpenAI are using AI to detect and disrupt influence operations that leverage AI-generated content. This includes identifying fake news and comments created by AI systems, highlighting the ongoing challenge of distinguishing synthetic media from real information.
- Operation “Stop News”: Russia-origin influence activityOpenAI · OpenAI News ·
- Corrupt Comment: Anti-corruption foundation criticismOpenAI · OpenAI News ·
- OpenAI ChatGPT
- OpenAI has used AI systems, potentially including GANs or similar generative models, to create fake news and comments as part of influence operations, and also to detect and disrupt such activities.
- Diffusion Models
- Diffusion models generate data by progressively adding noise a
- Variational Autoencoders (VAEs)
- VAEs learn a probabilistic mapping to generate
The concept of Generative Adversarial Networks was introduced by Ian Goodfellow and his colleagues in 2014. Source ↗
While direct GAN experimentation might be complex, you can observe generative AI's capabilities by asking ChatGPT to describe an image it has generated or to write a story based on a prompt. This showcases the output of generative models, which share principles with GANs in creating novel content.
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
- PromptRole-play a student struggling with math for AI tutor simulationSimulate a student's math struggles and uncertainty for AI tutor evaluation. · 5 min
- TemplateReusable workflow templates for healthcare teamsTeams can create and share templates for common workflows like documentation or prior authorizations. · 15 min
- TemplateTemplate for building an AI tool tailored to your workA structured approach to defining and building a custom AI tool for specific business needs. · 15 min
- TemplateEvals CSV prompt list for agent skillsA CSV file template for listing prompts used in evaluating agent skills. · 20 min
- HeuristicDouble coding agent task completion with docs linksProviding coding agents with a documentation link significantly increases their task completion rate. · 2 min
Questions people ask
- What is the main goal of a GAN?
- The main goal of a GAN is to generate new, synthetic data that is indistinguishable from real data.
- What are the two main components of a GAN?
- The two main components are the generator, which creates data, and the discriminator, which tries to distinguish real data from generated data.
First seen on Subvolts Oct 1, 2024. Updated Oct 19, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.