What is Generative AI?

Generative AI enables users to quickly generate new content based on a variety of inputs.
Inputs and outputs to these models can include text, images, sounds, animation, 3D models, or other types of data.

How Does Generative AI Work?
Generative AI models use neural networks to identify the patterns and structures within existing data to generate new and original content.

Generative AI models use neural networks to identify the patterns and structures within existing data to generate new and original content.

Examples of foundation models include GPT-3 and Stable Diffusion, which allow users to leverage the power of language.
For example, popular applications like ChatGPT, which draws from GPT-3, allow users to generate an essay based on a short text request.
On the other hand, Stable Diffusion allows users to generate photorealistic images given a text input.

How to Evaluate Generative AI Models?
The three key requirements of a successful generative AI model are:

Quality: Especially for applications that interact directly with users, having high-quality generation outputs is key. For example, in speech generation, poor speech quality is difficult
to understand. Similarly, in image generation, the desired outputs should be visually indistinguishable from natural images.

Diversity: A good generative model captures the minority modes in its data distribution without sacrificing generation quality.
This helps reduce undesired biases in the learned models.

Speed: Many interactive applications require fast generation, such as real-time image editing to allow use in content creation workflows.

Credit : https://www.nvidia.com/en-us/glossary/generative-ai/