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Multimodal data

Multimodal data is information represented across two or more distinct modalities, such as text, images, audio, or video.

How 2 Manning books explain it

WordPress Power Toolkit: Harness AI to build next-level websites

Multimodal input is information supplied to an AI system through multiple modalities, such as text, images, audio, or video.

How it is used
Explains how AI systems broaden learning and information gathering beyond text to include varied real-world and online sources.
How deep it goes
Defines multimodal inputs and illustrates their sources, but stops short of explaining implementation details or model architecture.
Examples
Uses traffic cameras, podcasts, YouTube videos, human communications, and current-event web searches as examples.
What you can do after
Evaluate how AI systems can draw information from text, images, audio, video, and current web sources.

Read how WordPress Power Toolkit: Harness AI to build next-level websites explains multimodal data →

Data Analysis with LLMs

Multimodal data combines information from multiple modalities, such as text, images, audio, or video.

How it is used
Combines text and image inputs so a language model can answer questions about pictures.
How deep it goes
Explains how a message represents text and images as typed elements.
Examples
Pairs an image with a banana-presence question and describes generating a poem and picture from instructions.
What you can do after
Build a user message containing a text question and image URL for visual question answering.

Read how Data Analysis with LLMs explains multimodal data →

Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.