What is multimodal AI?
Multimodal AI is AI that takes in or produces more than one kind of data, such as text, images, audio or video, instead of text alone.
Some models are multimodal themselves; other systems join several single-purpose models, one for speech, one for text, one for pictures.
How it works
A multimodal model turns each kind of input into a shared internal form, so it can read a photo and a question together. A system without one can still be multimodal by chaining models: speech is transcribed to text, the text is answered, and the answer is read aloud.
Examples
- Asking what is wrong in a screenshot of an error.
- Reading a chart or a photographed receipt.
- Talking to an assistant and hearing it answer.
Limitations
- Models misread small text, handwriting and crowded images.
- Chained systems lose tone and emphasis when speech becomes text.
- Images and audio cost more to process than text.
How Venta uses it
Images you drop into a conversation are sent to a model that can look at them. Voice works both ways: what you say is transcribed, and replies can be read aloud.
Questions people ask first
- Can I talk to Venta instead of typing?
- Yes. Voice works in the browser, on phones and in the Windows app; the voice page explains how.
- Can Venta read a screenshot?
- Yes. Drop the image into the conversation and ask about it.
- Is multimodal AI the same as generative AI?
- No. Generative AI describes making new content; multimodal describes working with more than one kind of data. A system can be either, both or neither.