Multimodal AI
35 min read
Multimodal Foundations
Combining modalities
The most capable AI systems don't just understand text—they see images, hear audio, and process video. Multimodal AI combines these capabilities, enabling applications that weren't possible before.
What is Multimodal AI?
Multimodal AI processes multiple types of input: text + images (GPT-4V, Claude Vision), text + audio (Whisper + LLM), or even text + images + video. This mirrors how humans experience the world—we don't process vision and language separately; they're integrated.
How It Works
Multimodal models typically have separate encoders for each modality (vision encoder, text encoder), plus a mechanism to align them in a shared space. When you show GPT-4V an image, a vision encoder processes it into embeddings that the language model can understand alongside text.
Applications
Document understanding (analyze PDFs with charts and tables), visual question answering (ask questions about images), content creation (generate images from text descriptions), accessibility (describe images for visually impaired users), and robotics (combine vision with language commands).
💡 Key Takeaways
- Multimodal AI combines text, images, audio, and video
- Separate encoders feed into a shared understanding space
- Enables document analysis, visual QA, content creation
- The future of AI is inherently multimodal
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