AI systems that create new content, such as text, images, audio, video, or code, based on patterns learned from training data.
Traditional AI often classifies or predicts, for example deciding whether an email is spam. Generative AI produces something new: an essay, a photorealistic image from a text description, a melody, or a working function in a programming language.
The main techniques include large language models built on the transformer architecture for text and code, and diffusion models for images and video. Generative AI is used for drafting, summarizing, brainstorming, customer support, design, and software development. It also raises important issues: outputs can contain factual errors known as hallucinations, training data may include copyrighted material, and the technology can be misused for deepfakes or misinformation. Human review remains important for anything published or high-stakes.
An AI assistant built into an application that helps a person with tasks as they work, while the person stays in control of decisions.
DeepfakeSynthetic audio, video, or images generated or altered with AI to convincingly show someone saying or doing something they did not.
Diffusion ModelA type of generative AI model that creates images, audio, or video by starting from random noise and refining it step by step.
Foundation ModelA large AI model trained on broad data that can be adapted to many tasks, such as writing, coding, or image analysis.
Generative Engine OptimizationAdapting content so it is accurately found, cited, and summarized by AI-powered search and chat tools that generate answers.
HallucinationWhen an AI model produces information that sounds plausible but is false, fabricated, or not supported by its source material.
Large Language ModelAn AI model trained on vast amounts of text to understand and generate human-like language, code, and other content.
Multimodal AIAI systems that can process and combine multiple types of input or output, such as text, images, audio, and video.
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