Inside the machine (3), how the model represents meaning

▤ Summary

This article explains how language models represent meaning with embeddings, positional encoding, and attention. It shows why token IDs are only labels and why vector geometry drives semantic behavior. The piece also walks through self-attention and multi-head attention using clear examples like ambiguous words such as “pesca.” It closes by connecting these mechanics to practical AI deployment choices, including domain fine-tuning and context window sizing.

▦ Linked Companies

Anthropic

$10M to $25M