What is embeddings?

Embeddings are a way to turn words, images, or other data into a list of numbers (a vector) that a computer can understand. Each item gets its own unique set of numbers, and similar items end up with similar numbers. Think of it as giving every word a location on a map, where distance on the map shows how related they are.

Let's break it down

  • Data → Numbers: Raw data (like the word “cat”) is converted into a fixed‑length list of numbers, e.g., [0.12, -0.34, 0.78, …].
  • Learning the numbers: A model (often a neural network) looks at lots of examples and learns which numbers best capture the meaning or features of each item.
  • Similarity: By measuring the distance (e.g., cosine similarity) between two vectors, we can tell how alike the original items are.
  • Fixed size: No matter how long the original text or how complex the image, the embedding always has the same number of dimensions (like 128, 256, 768, etc.).

Why does it matter?

Embeddings let computers work with complex, unstructured data in a simple, mathematical way. Because the numbers capture meaning, we can:

  • Compare items quickly (search, recommendation).
  • Feed them into other machine‑learning models that expect numeric input.
  • Reduce the amount of data needed to store or process, since a short vector replaces a long piece of text or a high‑resolution image.

Where is it used?

  • Search engines: Matching a query to relevant documents.
  • Chatbots & language models: Understanding user input and generating responses.
  • Recommendation systems: Suggesting movies, products, or friends.
  • Computer vision: Representing images for classification or similarity search.
  • Audio processing: Turning speech or music clips into vectors for tasks like speaker identification.

Good things about it

  • Efficiency: Small vectors are fast to store, transmit, and compute with.
  • Flexibility: The same embedding technique can be applied to text, images, audio, or even graphs.
  • Transferability: Pre‑trained embeddings (like Word2Vec, BERT, CLIP) can be reused for many tasks without training from scratch.
  • Semantic power: Captures nuanced relationships (e.g., “king” - “man” + “woman” ≈ “queen”).

Not-so-good things

  • Loss of detail: Compressing rich data into a fixed‑size vector can discard subtle information.
  • Bias: If the training data contains stereotypes, the embeddings can inherit and amplify them.
  • Interpretability: It’s hard to understand what each dimension of the vector actually represents.
  • Domain mismatch: Embeddings trained on one type of data (e.g., news articles) may perform poorly on a very different domain (e.g., medical notes) without fine‑tuning.