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Multimodal AI

Models that perceive and reason across more than one kind of data at once — text, images, audio, and video together.

Multimodal AI is one of the core areas in the AI University map of AI. Explore the diagram, then dive into each topic — every subtopic grows into its own deep-dive over time.

flowchart LR
  TXT[/Text/] --> ENC
  IMG[/Image/] --> ENC
  AUD[/Audio/] --> ENC
  ENC[[Shared embedding space]] --> REASON{{Reason / generate}} --> OUT[/Any modality/]

Key topics

  • Vision-language models

    Systems like CLIP, GPT-4V and Gemini that jointly understand pictures and words — captioning, visual Q&A, and grounding.

  • Cross-modal embeddings

    Mapping different modalities into one shared vector space so text can search images and vice versa.

  • Any-to-any generation

    Turning text into images, images into text, or speech into video with unified generative models.

  • Fusion strategies

    Early, late, and attention-based fusion — how signals from each modality get combined.

  • Document & chart understanding

    Reading PDFs, tables, screenshots and diagrams as mixed visual-textual data.

  • Multimodal agents

    Agents that see a screen or camera and act — the basis of computer-use and assistant robots.

NLP & Large Language Models · Computer Vision · Speech & Audio AI · Generative AI

Learn this properly

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