Contextual embeddings

We used to think word meaning was simple. A word like bank must point to money, right? Then we hit “river bank” and realized—nope. Words only make sense in context. That’s where contextual embeddings come in.

From static to living meaning

Old models gave each word a fixed vector. One slot for bank, no matter if it held your cash or your canoe. AI (artificial intelligence) changed this. She now builds word meaning on the fly, adjusting vectors with the surrounding text.

How transformers do it

Transformers are the engines here. They look at every word in a sentence at once. Then they decide, with attention weights, which neighbors matter. She doesn’t just store a dictionary; she calculates “what you mean” every time.

Why context is everything

Take “He sat on the bank.” If nearby words talk about water, she shifts bank toward rivers and mud. If the words lean on finance, she pushes it toward vaults and loans. Context nudges her, and the embedding follows.

What we get out of it

The result is flexible meaning. She isn’t trapped by a single definition. Instead, her vectors morph, and downstream tasks—translation, summarization, search—get sharper. It’s the difference between a parrot and a partner.

One coder’s thought

We like that she’s fussy about context. It feels closer to how we read, or how we catch sarcasm. Makes us wonder: if she’s already this good at meanings, how long before she starts side-eyeing our jokes?