King − Man + Woman = Queen: How AI Learns the Meaning of Words

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Everyone's talking about large language models, but here's the question nobody starts with: how does a machine even *represent* a word in the first place? A computer only understands numbers — so before any of the ChatGPT magic can happen, "king," "queen," and "dog" have to become vectors of numbers. And how you do that turns out to be the whole ballgame. This session goes back to the idea that cracked it open: **Word2Vec**. It's the technique that first showed you could capture a word's *meaning* as a position in space — close words end up close together, and you get the famous result that *king − man + woman ≈ queen*. Meaning becomes geometry. Once you see this, modern LLMs stop feeling like magic and start feeling like a natural next step. We'll walk through, intuitively: * **Why words become vectors** — the core idea of embeddings, and why "similar meaning = nearby in space" is so po

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