Generative AI · Fast-moving · Intermediate

Multimodal AI

Models that accept or produce more than one data type — text, images, audio, video — within a single system.

What Multimodal AI is

A multimodal model can read a screenshot and answer questions about it, describe a chart, transcribe speech, or generate an image from a description, using shared internal representations.

How it works

Encoders map each modality into a common embedding space consumed by a transformer backbone. Training uses paired data such as image-caption or audio-transcript sets, and outputs are produced by modality-specific decoders.

Why it matters

It removes the need to stitch together separate OCR, vision and speech systems, and unlocks interfaces where a user can simply show the model the problem.

Common uses

→Document and screenshot understanding
→Accessibility descriptions
→Voice assistants with vision
→Video summarisation

Strengths

✓One system across data types
✓Context transfers between modalities

Watch for

✓Higher inference cost
✓Visual reasoning still error-prone on fine detail

Continue exploring

Computer Vision Speech Recognition Vision Transformer Embeddings