Multimodal AI
Vision-language models, audio AI, cross-modal understanding, GPT-4V, Gemini
Multimodal AI systems process and generate content across multiple modalities — text, images, audio, and video — in a unified framework. Rather than siloed models for each modality, multimodal models understand relationships between modalities, enabling capabilities like image captioning, visual question answering, and audio transcription.
Key Points
- Modalities: text, image, audio, video, code, structured data, 3D point clouds
- Vision-Language Models (VLMs): combine a vision encoder (ViT) with an LLM (GPT-4V, LLaVA)
- CLIP (Contrastive Language-Image Pre-training): aligns image and text in a shared embedding space
- Image Understanding: describe images, answer questions about visual content, OCR
- Audio AI: speech recognition (Whisper), text-to-speech (ElevenLabs), music generation (Suno)
- Video Understanding: action recognition, scene description, temporal reasoning
- Cross-modal retrieval: find images matching a text query using shared embedding space
- Challenges: aligning different modalities in a shared space, compute cost, cross-modal reasoning
- GPT-4o: natively processes text, image, and audio in real time
- Gemini 1.5: up to 1M token context including interleaved text, images, audio, video
Real-World Example
Google Lens uses multimodal AI to let you point your camera at a flower, restaurant menu, or math problem — and get an instant intelligent response. It combines computer vision, OCR, and language understanding. OpenAI's "seeing" GPT-4V can describe a complex chart, read handwriting, or explain a meme.