Multimodal AI

Definition

Multimodal AI combines and processes data from multiple modalities—such as text, images, audio, or video—to generate outputs or predictions.

Purpose

The purpose is to build systems that understand information more like humans, who integrate multiple senses. It is used in healthcare, robotics, and conversational systems.

Importance

  • Expands capabilities beyond single-modality AI.
  • Enables richer human–AI interaction.
  • Requires advanced architectures for fusion of diverse data.
  • Raises complexity in training and evaluation.

How It Works

  1. Collect multimodal datasets with aligned inputs (e.g., text + images).
  2. Encode each modality into vector representations.
  3. Use fusion techniques to combine modalities.
  4. Train models to learn cross-modal relationships.
  5. Generate outputs across one or multiple modalities.

Examples (Real World)

  • CLIP (OpenAI): links images and text for search.
  • Google Gemini: multimodal model handling text, images, and audio.
  • Image captioning systems: generate text descriptions from photos.

References / Further Reading

  • Baltrušaitis et al. “Multimodal Machine Learning: A Survey.” IEEE TPAMI.
  • OpenAI CLIP Paper.
  • Stanford HAI: Multimodal AI Research.