Multimodal AI

Multimodal AI systems can understand and generate multiple types of content—text, images, audio, video—often simultaneously. "Modality" refers to the type of data.

Single-modal AI: A model that only handles one type:

• Text-only LLM
• Image-only classifier
• Speech-only recognizer

Multimodal AI: A model that handles multiple types:

• Analyze an image and answer questions about it in text
• Generate images from text descriptions
• Transcribe audio and summarize the content
• Create video from a text script

The power of multimodal AI is connecting different types of information, much like humans naturally combine seeing, hearing, and speaking.

GPT-4o (OpenAI) Processes text, images, and audio. Can see images you share, hear your voice, and respond in text or speech.

Claude 3 (Anthropic) Processes text and images. Can analyze charts, read documents, describe photos, and answer questions about visual content.

Gemini (Google) Handles text, images, audio, and video. Can analyze long videos and answer questions about them.

CLIP (OpenAI) Connects images and text in a shared embedding space. Enables searching images by text description.

Whisper (OpenAI) Converts speech to text across many languages. Multimodal in processing audio to produce text.

Stable Diffusion, DALL-E, Midjourney Generate images from text descriptions. Text → Image multimodality.

Shared representation space Different modalities are converted to a common format the model can process. This often means:

1. Tokenization: Breaking each modality into tokens

   • Text → word/subword tokens
   • Images → patch tokens
   • Audio → audio tokens

2. Encoding: Converting tokens to embeddings that capture meaning

3. Processing: A transformer (or similar architecture) processes all embeddings together

4. Generation: Output can be any modality the model supports

Why it works: By training on paired data (images with captions, videos with descriptions), models learn to align concepts across modalities. The concept of "dog" connects to visual patterns of dogs AND the word "dog."

Document understanding Process documents with text, tables, charts, and images together. Extract information that requires understanding all elements.

Visual question answering "What's in this image?" "How many people are in this photo?" "What's wrong with this diagram?"

Content creation Generate blog posts with relevant images. Create videos from scripts. Produce presentations from outlines.

Accessibility Describe images for visually impaired users. Transcribe audio for deaf users. Convert between modalities.

Customer support "Here's a photo of my problem." Agent analyzes image and provides help.

E-commerce Search products by uploading photos. "Find me something similar to this."

Medical imaging Analyze medical images and generate reports. Combine visual analysis with clinical notes.

More modalities Touch, smell, and physical interaction. Robots that see, hear, and feel.

Richer generation Not just images from text, but full videos, 3D worlds, and interactive experiences.

Better integration Seamless switching between modalities. Ask a question in voice, see the answer as a video, follow up in text.

Real-time processing Live video analysis, real-time translation with lip-sync, instant visual content creation.

Embodied AI Multimodal models controlling robots that interact with the physical world using multiple senses.

The trend is clear: AI is becoming more like humans in its ability to see, hear, read, write, speak, and create across all forms of media.