Generative AI refers to machine learning models that can generate new content, such as text, images, music, or code, based on patterns learned from large datasets that they have been trained on. These training datasets can include books, articles, images, websites, and other sources. When given a prompt, an AI tool uses its learned knowledge to generate new content that follows the patterns it has seen during training. This new content is not guaranteed to be factually accurate.
Common types of generative AI models include the following:
- Text generators: Large language models (LLMs) such as GPT-4 can generate coherent and relevant text, often conversational in tone, across many subject areas.
- Image and video generators: Visual generation models like DALL-E and Veo allow users to create rich media content from text prompts.
- Music and audio generators: Generative audio models allow users to compose music, model voices, generate sound effects, or create other audio based on given parameters.
- Code generators: Tools like GitHub Copilot assist developers by suggesting code snippets or completions based on the context.