Embeddings

We keep hearing about embeddings in artificial intelligence (AI). They sound mysterious, but they’re just a trick for turning messy stuff—like words or images—into neat rows of numbers. Once data is in that form, she can compare, sort, and reason about it.

Word embeddings

The first big splash came with word embeddings. Each word gets mapped to a vector—a long list of numbers—so “cat” and “dog” end up closer to each other than to “spoon.” That’s because the model has seen them in similar contexts. It’s like having a map where distance equals meaning.

We don’t have to worry about equations. Think of it as plotting words on a huge invisible graph. She knows “Paris” and “France” go together because their vectors line up, not because anyone told her.

Image embeddings

Images work the same way. Instead of “cat” being a word, it’s pixels. A photo of a tabby turns into a vector, too. Then, when we search “cat,” she can find all the pictures whose numbers sit nearby.

It’s not magic—just math. But it feels like magic when an app groups our vacation photos without us tagging them.

Embedding models

The models that make these vectors are embedding models. They don’t just memorize—they generalize. Train one on text, and it learns the geometry of language. Train another on images, and it learns shapes and colors. Once trained, she gives us a way to compare new things against everything she’s already mapped.

We can stack these models into bigger systems. Search engines, chatbots, recommendation feeds—most rely on embeddings quietly doing the heavy lifting underneath.

Our musing

As coders, we don’t have to love linear algebra to use this. Just remember: embeddings turn messy human stuff into coordinates she can handle. It’s her way of making sense of our world.