Generative AI enables users to generate new content based on a variety of inputs. Inputs and outputs to these models can include text, images, sounds, animations. 3D models or other types of data.  Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.

Generative AI is a type of Artificial Intelligence that creates new content based on what it has learnt  from existing content. 

The process of learning from existing content is called training and results in the creation of a statistical model. 

When given a prompt, GenAI uses this statistical model to predict what an expected response might be and this generates new content. 

How does Generative AI work? 


A Generative AI model attempts to learn patterns from content in order to generate new content. Usually in Generative AI the output is natural language like speech or text, an image or audio. 

The Generative AI process can take training code, labelled data and unlabeled data of all data types and can build a foundation model. The foundation model can then generate new content, for example- text, code, audio, video etc. Models like PaLM, LaMDA ingest very large data from multiple sources across the internet and build foundational language models we can use simply by asking a question. 

Generative language models learn about patterns in language through training data. Then given some text, they predict what comes next. Generative image models produce new images using techniques like diffusion. Then given a prompt or a related imagery, they can transform random noise into images or generate images from prompts.

Generative AI Outputs based on the Input data type
Where the input data is image 


 


Where the input data is text 


FOR ADDITIONAL COMPREHENSIVE INFORMATION ON GENERATIVE AI INCLUDING PRODUCTS, APPLICATIONS,READING LISTS, ETC PLEASE REFER TO OUR PAGE ON GENERATIVE AI for MEMBERS.


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