LLMs: What's the Fuss About? — How Machines Turn Words Into Meaning
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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 powerful
- Continuous Bag of Words (CBOW) — predicting a word from the words around it
- Skip-gram — flipping it around: predicting the surrounding words from a single word, and why it shines on rarer words
- Why any of this matters for today's LLMs — how these early ideas grew into the token embeddings inside every model you use now
No heavy math required — this is built to be intuitive and beginner-friendly. Come with curiosity and your questions. We'll wrap with an open Q&A (~30 min) so you can ask whatever's been nagging you about how these models actually work.
Great for beginners. Bring your questions.
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