🎨 Multimodal Foundation Models
AI systems that understand images, text, audio, and video
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0 / 5 completedIntroduction to Multimodal AI
🎯 What are Multimodal Models?
Multimodal foundation models process and generate content across multiple modalities - text, images, audio, and video - enabling richer understanding and more natural human-AI interaction.
Going beyond text-only models to understand the world like humans do
🌟 Key Modalities
Text
Natural language understanding and generation
Vision
Images, videos, and visual scene understanding
Audio
Speech, music, and environmental sounds
Video
Temporal dynamics and motion understanding
💡 Why Multimodal?
Richer Understanding
Humans process information from multiple senses - AI should too
Cross-Modal Reasoning
Connect concepts across modalities (e.g., match images to descriptions)
Unified Representations
Single model handles multiple tasks without retraining
Natural Interaction
Communicate with AI using voice, images, or text seamlessly
🏆 Landmark Models
CLIP (OpenAI, 2021)
Vision-TextAligned image and text embeddings for zero-shot classification
Flamingo (DeepMind, 2022)
Vision-LanguageFew-shot learning for visual question answering
GPT-4V (OpenAI, 2023)
Vision-LanguageExtended GPT-4 with vision understanding capabilities
Gemini (Google, 2023)
Fully MultimodalNative multimodal training across text, image, audio, video
⚡ Key Advantages
Zero-Shot Transfer
Generalize to new tasks without task-specific training
Grounded Understanding
Connect abstract concepts to visual/audio reality
Emergent Abilities
Discover cross-modal connections not explicitly trained
Unified Interface
Single model for diverse multimodal applications