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Multimodal AI

AI systems that can understand and generate multiple types of content like text, images, audio, and video.

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Definition

Multimodal AI models can process and generate content across different modalities - text, images, audio, video - in a unified system.

Modalities: - Text (language understanding/generation) - Images (vision, generation) - Audio (speech, music) - Video (understanding, generation) - Code (programming)

Capabilities: - Describe images - Generate images from text - Transcribe and generate speech - Analyze videos - Create videos from prompts

Examples: - GPT-4o (text + images + audio) - Gemini (text + images + video) - Claude 3 (text + images) - Sora (text to video)

Architecture: Models use encoders for each modality that map inputs to a shared representation space, enabling cross-modal understanding.

Examples

GPT-4o analyzing a chart image and answering questions about it, or Sora generating video from a text description.

Related Terms

Large Language Model (LLM)

AI models trained on massive text datasets that can understand and generate human-like text.

Diffusion Model

AI models that generate data by learning to reverse a gradual noising process.

CLIP

OpenAI model connecting images and text in shared embedding space.

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