*** ### What is generative AI? - Generative AI or generative artificial intelligence **refers to the use of AI to create new content**, like text, images, music, audio, and videos. - Generative AI is **powered by foundation models (large AI models) that can multitask and perform out-of-the-box tasks**, including summarization, Q&A, classification, and more. Plus, with minimal training required, foundation models can be adapted for targeted use cases with very little example data. ### How does generative AI work? - Generative AI works by **using an [[Machine learning | ML]] model to learn the patterns and relationships in a dataset of human-created content**. It then uses the **learned patterns to generate new content**. - The most common way to **train a generative AI model** is to **use supervised learning - the model is given a set of human-created content and corresponding labels**. It then **learns to generate content that is similar to the human-created content and labeled with the same labels**. ### What are the most common generative AI applications? Generative AI processes vast content, creating insights and answers via text, images, and user-friendly formats. Generative AI can be used to: - Improve customer interactions through enhanced chat and search experiences. - Explore vast amounts of unstructured data through conversational interfaces and summarizations. - Assist with repetitive tasks like replying to requests for proposals (RFPs), localizing marketing content in five languages, and checking customer contracts for compliance, and more. *** **References**: - [Generate text, images, code, and more with Google Cloud AI](https://cloud.google.com/use-cases/generative-ai)