Multimodal AI
Early AI models worked with text only. Modern AI handles images, audio, video, and more—often simultaneously. This is multimodal AI.
What Is Multimodal AI?
Multimodal AI can process and generate multiple types of data:
- Text — Language understanding and generation
- Images — Vision, image generation
- Audio — Speech, music, sound
- Video — Moving images with temporal understanding
- Code — Programming languages
The most powerful modern models are multimodal by default.
Vision-Language Models
Understanding Images
Capabilities:
- Describe what's in an image
- Answer questions about images
- Extract text (OCR)
- Analyze charts and diagrams
- Identify objects and people
Models: GPT-4V, Claude 3, Gemini Pro Vision
Use cases:
- Accessibility (describe images)
- Document processing
- Quality control in manufacturing
- Medical image analysis
Example Interactions
[Upload image of whiteboard with notes]
User: "Transcribe this whiteboard and organize as bullet points"
AI: • Meeting agenda for Q3 planning...
[Upload chart]
User: "What trend does this show?"
AI: "Revenue grew 23% year-over-year..."
Image Generation
Models that create images from text:
Diffusion Models:
- Stable Diffusion (open source)
- Midjourney
- DALL-E 3
How they work:
- Start with noise
- Iteratively "denoise" guided by text
- Result: Image matching the description
Capabilities:
- Photorealistic images
- Artistic styles
- Image editing
- Inpainting (fill in parts)
Audio Models
Speech Recognition
Convert spoken words to text.
- Whisper (OpenAI) — Open source, highly accurate
- Google Speech-to-Text
- Assembly AI
Speech Synthesis
Generate realistic speech from text.
- ElevenLabs — Voice cloning
- PlayHT — Diverse voices
- OpenAI TTS — Simple and effective
Music Generation
- Suno — Full songs from text
- Udio — Music with vocals
- Stable Audio — Instrumental tracks
Video Models
Video Understanding
Analyze video content, answer questions about it.
Capabilities:
- Summarize videos
- Answer temporal questions ("What happened after...?")
- Extract key moments
Models: Gemini 1.5 Pro (can process hours of video)
Video Generation
Create video from text or images.
Models:
- Sora (OpenAI) — Most impressive, limited access
- Runway Gen-2 — Commercially available
- Pika Labs — Easy-to-use generation
Current state: Impressive short clips, consistency issues in longer content.
True Multimodal: Mixing Modalities
The frontier: Models that fluidly combine modalities.
GPT-4o:
- Processes text, images, audio natively
- Can hear and speak, not just read and write
- Real-time conversation with voice
Gemini:
- Native multimodal architecture
- All modalities trained together
- Strong cross-modal understanding
Practical Applications
Document Processing
- Upload PDFs with mixed content
- Extract information from forms
- Process receipts and invoices
Accessibility
- Describe images for visually impaired
- Transcribe audio for deaf users
- Generate audio from text for the blind
Creative Work
- Generate images for articles
- Create video from scripts
- Add music to content
Analysis
- Understand complex diagrams
- Extract data from charts
- Analyze surveillance footage
Challenges
Consistency Across Modalities
- Generated images may not match text exactly
- Video consistency is hard to maintain
- Cross-modal hallucinations
Computational Cost
- Video processing is expensive
- Real-time multimodal is resource-intensive
- Trade-offs between quality and speed
Safety Concerns
- Deepfakes and misinformation
- Copyright questions for generated content
- Privacy issues with face/voice
The Bottom Line
Multimodal AI is becoming the default:
- Most frontier models handle multiple modalities
- Single-modality models are increasingly niche
- The future is AI that perceives the world as we do
For practitioners:
- Leverage vision for document understanding
- Consider audio for accessibility
- Video capabilities are improving rapidly
Next up: Running AI Locally — AI without the cloud