TL;DR

Multi-modal AI processes and generates multiple data types (text + images, text + audio). Examples include GPT-4V, Gemini, and CLIP. Enables richer interactions and new applications.

What is multi-modal AI?

Definition:
AI that understands and/or generates multiple types of data.

Common combinations:

  • Text + Images (vision-language models)
  • Text + Audio (speech + language)
  • Text + Video
  • All of the above

Vision-language models

Capabilities:

  • Describe images
  • Answer questions about photos
  • Extract text from images (OCR)
  • Identify objects, people, scenes

Examples:

  • GPT-4V (GPT-4 with vision)
  • Google Gemini
  • Claude 3
  • CLIP (OpenAI)

Use cases:

  • Accessibility (describe images for blind users)
  • Content moderation (detect inappropriate images)
  • Shopping (visual search)
  • Healthcare (analyze medical images with context)

Audio-language models

Capabilities:

  • Transcribe speech to text
  • Generate speech from text
  • Understand tone and emotion
  • Translate spoken language

Examples:

  • Whisper (OpenAI transcription)
  • ElevenLabs (voice generation)
  • Google Speech-to-Text

How multi-modal models work

Shared representation:

  • Convert different modalities to same vector space
  • Text embeddings + image embeddings in same space
  • Enables cross-modal understanding

Architecture:

  • Vision encoder (for images)
  • Language model (for text)
  • Fusion layer (combines them)

Applications

Visual Q&A:

  • "What's in this image?"
  • "Read the text in this screenshot"

Document understanding:

  • Analyze charts, graphs, diagrams
  • Extract data from forms

Creative generation:

  • Text-to-image (DALL-E, Midjourney)
  • Image editing via text commands

Accessibility:

  • Describe surroundings for visually impaired
  • Transcribe audio for hearing impaired

Challenges

  • More complex than single-modal
  • Harder to train (need aligned data)
  • Computationally expensive
  • Hallucinations across modalities

Future directions

  • True video understanding
  • Real-time multi-modal interaction
  • 3D and spatial understanding
  • All senses combined

What's next

  • Vision AI Applications
  • Speech Processing
  • Advanced Model Architectures