Definition
Embedding is a way of representing data such as text, images and audio as points in a continuous vector space where the locations of those points in space are semantically meaningful to machine learning (ML) algorithms [1]. They pickup semantic or contextual similarities between pieces of data, making machines more effective in performing tasks that involve comparison, clustering, or classification.
For instance, computing algorithms understand that the difference between ₦2,000 and ₦3,000 is ₦1,000, indicating a close relationship between these amounts as compared to ₦2,000 and ₦100,000. However, real-world data includes more complex relationships. For example, jollof rice-fried plantain and pounded yam-egusi soup are analogous pairs (main dish with side/accompaniment), while suya-breakfast are opposite terms (suya is evening/night food, not morning food). Embeddings convert real-world data into complex mathematical representations that capture inherent properties and relationships—understanding that jollof rice is closer to fried rice (both rice-based mains) than to chin chin (a snack), and that akara-pap are a traditional pairing like bread-tea, even though they're completely different ingredient types.
Embedding: Semantic relationships in vector space [2].
Origin
Its origins can
be traced to 1950s. John Rupert Firth, a British linguist, put forward an
interesting idea: "You shall know a word by the company it keeps". Simply
put, the meaning of a word depends on the words around it. This laid the
groundwork for everything that followed.
In the early
2000s, Yoshua Bengio and his team first used
the term word embeddings, with their breakthrough of creating a neural language
model that could represent words as vectors — long lists of numbers. It was as
if each word got its unique digital code.
A major
breakthrough came in 2013, when Tomas Mikolov and his team from Google released
Word2Vec which revolutionized the field of embeddings. Word2Vec could quickly
and efficiently create vector representations of words by analyzing huge
volumes of text. It was like the appearance of a supercomputer that could
"understand" language better than ever before.
After 2014, the
development of embeddings accelerated even more. Models appeared that could
work not only with individual words but also with entire sentences and even
documents. The most famous of these is BERT (Bidirectional Encoder
Representations from Transformers), released by Google in 2018 [3].
Context and
Usage
Some of the use
cases and applications of embedding models are as follows:
- Images-captions matching: Models convert images and text into numerical representations (embeddings), matching visuals such as traditional wedding photos to captions with an embedding closest to the image’s embedding, ensuring accurate match. This technique powers tools like image search and photo tagging.
- Movie Recommendations: System uses an embedding model to represent movies capturing genre, cast, mood as numbers, and then recommends similar ones.
- Product Grouping: E-commerce websites use embeddings to group related products together. For example, “red sneakers” might be close to “blue sneakers” in the embedding space, so they’re shown as related
- Text search: Search engines convert queries like “best Nigerian food” into numerical embeddings, then retrieve documents with similar embeddings to return relevant results [4].
Why it Matters
In artificial
intelligence, Embeddings are the ground work that make it possible for
computers to understand the relationships between words and other objects. Simply
put, embeddings enable machine learning models to find similar objects. For example,
given a photo or a document, a machine learning model that uses embeddings
could find a similar photo or document [5].
Related AI
Models and Architectures
- Foundation Model: Large-scale pre-trained model that serves as a base for various downstream tasks.
- Generative Pre-trained Transformer (GPT): Family of language models using transformer architecture trained on vast text data.
- Hidden Layer: Intermediate layer in a neural network between input and output that processes data
- Large Language Model: AI model trained on massive text datasets to understand and generate human language
- Latent Space: Abstract mathematical space where AI models represent data in compressed, meaningful dimensions.
In Practice
A real-life case
study of embeddings in practice can be seen in the case of Gemini API which offers
embedding models to generate embeddings for text, images, video, and other
content. These resulting embeddings can then be used for tasks such as semantic
search, classification, and clustering, providing more accurate, context-aware
results than keyword-based approaches.
Reference
- Barnard, J. (n.d). What is embedding?
- Harsoor, S. (2024). Embeddings: A Deep Dive from Basics to Advanced Concepts.
- Embeddings. (2025). History of Embeddings.
- Mitchell, T. (2024). What are Embedding Models? An Overview.
- Cloud Fare. (2026). What are embeddings?