Multimodal AI
multimodal-ai refers to artificial intelligence models capable of ingesting and/or generating data across various Data-Modalities.
Key Concepts
- Modality: A specific data type or format used as input or output.
- Common Modalities: Includes Text, Images, Audio, Lidar, and Thermal-Imaging.
- Processing Capabilities: Models are distinguished by their ability to integrate and reason across these different data streams simultaneously.
New Insights
- Video Reference:
- Title: What is Multimodal AI? How LLMs Process Text, Images, and More
- Author / Channel: Martin Keen of IBM Technology
- URL: https://www.youtube.com/watch?v=J51oZYcNvP
- DeepSeek Innovation:
- [[lab-notes/2026-05-22-DeepSeeks-AI-Thinking-with-Visual-Primitives-for-Precise|DeepSeek’s AI: Thinking with Visual Primitives for Pre
- NVIDIA Innovation:
- NVIDIA Cosmos 3: Omnimodal World Model for Physical AI and Robotics
- Context: Introduces Cosmos 3 as an “world-model]]|Omnimodal World Model]]” advancing Physical AI and Robotics.
- Significance: Represents a shift from standard multimodal processing to comprehensive world modeling for physical interaction, distinct from traditional generative media approaches.
- Source: Sam Witteveen video analysis (“Cosmos 3 - NVIDIA’s World Foundation Model]]”).