Artificial Intelligence / Unit 9 AI 0507 worked example, 60 to 90 seconds
Words as Rows
The idea
An embedding gives every word a row of numbers positioned so that words used similarly get similar rows — turning "unseen but plausible" into ordinary nearness arithmetic.
In practice
Tally, for three words, how often each appears near weather-talk and near kitchen-talk: rain lands at (9, 0), snow at (8, 1), flour at (0, 7). Plot the rows as Unit 8's graph-paper points. Rain and snow sit close together; flour sits far away — pure co-occurrence, no meanings defined, no dictionary consulted. Now the payoff. A model that has seen "the snow melted" can half-trust "the rain melted," because prediction can lean on a near neighbor's statistics instead of demanding the exact string. The zero that killed the counting model becomes a small honest number borrowed from the neighborhood — which is what a similarity ruler is for.
Where people slip
Two columns made the toy work; real embeddings use hundreds — the geometry survives the width.
Your place on the trail
Every lesson rests on earlier ones and opens later ones. Tap any to jump.
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