Multimodal AI
Multimodal AI refers to models that can understand or generate more than one type of data, such as text, images, audio and video, within a single system. A multimodal assistant can, for example, read a photo of a chart and explain it in text, or answer a spoken question aloud.
Multimodal models map different kinds of input into a shared representation so they can reason across them. Some are trained on paired data, such as images with captions; CLIP (Radford et al., 2021) is an influential example that learned to match images and text. Others connect specialised encoders for images or audio to a language model, or generate images and audio directly.
Why it matters: multimodality decides what you can hand an AI tool. A text-only model cannot read a scanned invoice, a screenshot or a diagram; a multimodal one can. When comparing assistants, check which inputs and outputs are supported, file size limits and whether features such as voice or image generation are included in your plan.
Example: a field technician photographs an error screen, and a multimodal assistant identifies the error code and suggests troubleshooting steps.
Limits: support varies by modality. A model may read images well but struggle with small text, handwriting, complex charts or long videos, and generated images or audio carry their own licensing and accuracy questions.
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Sources
- Learning Transferable Visual Models From Natural Language Supervision. Radford et al., arXiv, March 2021
- Flamingo: a Visual Language Model for Few-Shot Learning. Alayrac et al., arXiv, April 2022
Topic: AI fundamentals
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