core concepts

Generative AI

AI systems capable of creating new content including text, images, audio, video, and code. Generative AI models learn the underlying patterns and structure of training data, then produce novel outputs that follow similar patterns.

In practice

ChatGPT generating an essay, DALL-E creating an image, and Suno composing music are all generative AI.

Large Language Model
A neural network trained on massive text datasets to understand and generate human language. LLMs use billions of parameters to capture statistical patterns in language, enabling them to perform tasks like translation, summarization, and code generation.
Diffusion Model
A generative model that learns to create data by reversing a gradual noising process. During training, noise is progressively added to data; the model learns to denoise step by step. At generation time, it starts from pure noise and iteratively refines it into coherent output.
GAN
Generative Adversarial Network, an architecture consisting of two neural networks: a generator that creates synthetic data and a discriminator that tries to distinguish real from generated data. The two networks train adversarially, pushing each other to improve.

In the index

Tools that mention Generative AI

GrammarlyTrust 93
Grammarly AITrust 91
CrowdStrike Charlotte AITrust 90
Salesforce EinsteinTrust 87
Magnific AITrust 83
AkoolTrust 83
Steve AITrust 83
Insilico MedicineTrust 81

Matched on each tool’s own description and feature list, highest trust score first.

Briefings

Generative AI

Tools that mention Generative AI

Further reading