Editing Multimodal Ai (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
The key challenge in multimodal AI is '''grounding''': connecting abstract representations across very different data formats. A photo of a dog and the text "dog" contain the same semantic concept expressed in completely different computational forms — one is pixels arranged in space, the other is a token sequence. Teaching models to bridge these representations is the core problem.

'''Two architectural approaches''':

'''Early fusion''': Combine modalities at the input level — interleave image patches and text tokens into a single sequence and pass it through a transformer. This allows fine-grained cross-modal interaction from the first layer. Gemini and GPT-4o use variants of this approach.

'''Late fusion''': Process each modality with a specialized encoder, then combine the representations at a higher level. A vision encoder (ViT) processes the image into a sequence of visual tokens; a language model processes the resulting tokens alongside text. LLaVA and many production VLMs use this approach because it can leverage separately pre-trained encoders.

'''CLIP's contrastive training''': CLIP is trained on 400 million (image, text) pairs from the internet using contrastive loss — the image and its matching caption are pulled together in embedding space, while mismatched pairs are pushed apart. The result is an embedding space where text and images of semantically similar concepts are close — enabling zero-shot image classification, image retrieval, and cross-modal search.

'''Why is multimodal hard?''' Each modality has different token rates (a 256×256 image = 256 visual tokens; one second of audio = ~50 audio tokens), different noise characteristics, and different temporal structures. Balancing learning across modalities during training — preventing one modality from dominating — is a significant engineering challenge.
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