Just like how "Hello world!" becomes discrete tokens for text processing, a photograph gets chopped into image patches, and a song becomes a sequence of audio codes.

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Modern AI systems process images, audio, and video by converting them into discrete tokens, similar to text processing. Images use patch embeddings (dividing into grid squares), vector quantization (learning visual codebooks), or contrastive embeddings. Audio employs neural codecs for quality preservation, ASR transcription for semantic content, or hierarchical approaches for multi-scale representation. Each tokenization method involves trade-offs between computational efficiency, information preservation, and semantic understanding, with the optimal choice depending on specific use cases and requirements.

How LLMs See Images, Audio, and More

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