Maximoto | Sovereign Engineer

How Do Machines Understand Meaning?

Computers are fundamentally good at one thing: math. They don't understand words, images, or concepts in the way humans do. So how is it possible for a model like ChatGPT to grasp the nuance of a poem, or for a search engine to know that "king" and "queen" are related?

The answer lies in one of the most powerful and foundational concepts in modern artificial intelligence: embeddings.

Embeddings are the unsung heroes of the AI world. They are the crucial bridge that translates our complex, messy, and abstract world into the structured, numerical language that machines can actually work with.

What Exactly is an Embedding?

At its core, an embedding is a vector—a list of numbers—that represents a piece of data. This "piece of data" could be anything: a word, a sentence, an entire document, an image, or even a product in an e-commerce catalog.

But it's not just any random list of numbers. A well-trained embedding model learns to create vectors such that the distance between them in a high-dimensional space corresponds to their semantic similarity.

Let's break that down with an analogy.

Imagine a giant, multi-dimensional library. Instead of organizing books alphabetically, we organize them by their meaning.

Books about "kings," "queens," and "princes" would be clustered together in one section.
Books about "apples," "oranges," and "bananas" would be in another section, far away from the royalty section.
Interestingly, the book "Snow White" might be situated somewhere between the "royalty" section and the "fruit" section, because it's related to both.

In this analogy, the "location" of each book is its embedding. It's a set of coordinates ([x, y, z, ...] ) that places it in a meaningful position relative to everything else.

How are Embeddings Created?

Embeddings are learned, not programmed. They are the output of a neural network that has been trained on a massive dataset. For example, a language model might be trained to predict the next word in a sentence. In the process of learning to do this, the model's internal layers develop a rich numerical representation—an embedding—for each word that captures its context and meaning.

The AI Skill Matcher on my own portfolio uses this exact technique. I use a pre-trained Sentence Transformer model (all-MiniLM-L6-v2) to convert a user's query and my project descriptions into 384-dimensional vectors. By calculating the cosine similarity (a measure of the angle between these vectors), the system can find the projects that are most semantically similar to the query, far beyond simple keyword matching.

Why Embeddings are a Game-Changer

Once you have a way to turn anything into a meaningful vector, you unlock a vast range of AI capabilities:

Semantic Search: This is what my Skill Matcher does. Instead of searching for "python," you can search for "backend web development language," and the model knows that a project with "Django" is a highly relevant result.
Recommendation Engines: How does Netflix know you might like a certain movie? It converts your viewing history and the movies in its catalog into embeddings. It then finds the movies whose embeddings are "closest" to yours in that high-dimensional space.
Classification and Clustering: You can feed these vectors into classic machine learning models to classify text (e.g., spam vs. not spam) or cluster similar documents together without even reading them.
Multimodality: As AI models become multimodal (understanding both text and images), they learn to place the embedding for the word "cat" very close to the embedding for a picture of a cat. This is the foundation of how models like GPT-4o work.

Conclusion

Embeddings are the quiet workhorses of the AI revolution. They are the translator that allows us to apply powerful mathematical operations to abstract concepts like language and art. The next time you use a search engine, get a product recommendation, or chat with an LLM, remember that the magic begins with these powerful, meaning-packed lists of numbers.