Multimodal AI
AI systems that can process and generate multiple types of data — such as text, images, audio, and video — in a unified model.
Multimodal AI refers to models that work across multiple data types — text, images, audio, video, and more — within a single unified architecture. Rather than having separate specialist models for each modality, multimodal systems can understand and generate across all of them, enabling richer and more capable AI interactions.
GPT-4V, Claude 3 Opus, and Gemini Ultra are examples of multimodal LLMs that can analyze images, read charts, interpret screenshots, and understand video alongside text. On the generation side, models like GPT-4o and Gemini can produce text, images, and audio from text prompts in an integrated way.
Multimodal Capabilities
- Vision-Language — understand images + text together (GPT-4V, Claude 3)
- Audio-Language — transcribe, analyze, and respond to speech
- Video understanding — analyze temporal content in video
- Any-to-any generation — input text, get image/audio/video output
The architecture usually involves separate encoders for each modality that project inputs into a shared representation space, then a unified transformer processes all tokens together. CLIP was an early milestone in aligning vision and language representations. Modern multimodal models are trained end-to-end on paired data — image-caption pairs, video transcripts, etc. — to learn cross-modal grounding.