Together with an expert, we figure out how embeddings work, why they have become the basis of intelligent systems, how they are interconnected with cloud technologies, and what significance they have for the infrastructure of the future
Artificial intelligence can write texts, recognize faces, recommend products, and even predict industrial failures — all thanks to its ability to understand abstract data. At the heart of this ability are embeddings, one of the key tools of machine learning. They allow complex and heterogeneous objects — words, images, products, users — to be translated into digital language that a machine can understand. Without them, AI would be just a set of formulas.
Why AI Needs ‘Digital Translation’
Human language is polysemantic and contextual. When we write “lock,” we can accurately determine whether we are talking about a fortress or a device for locking doors, depending on the context. For a machine, this is a challenge: words, images, events, search queries — all of this must be translated into a numerical format in order to compare, analyze, and train models.
Embeddings are a way to do this translation. A word, image, or other object is represented by a vector — a numerical representation in a multidimensional space, trained on statistical relationships or large language models. These vectors allow the system to determine similarities between objects, build dependencies, and draw conclusions. For example, the embedding of the word “cat” will be closer to “animal” than to “car.”
An example of a 3D projection of embeddings. Although real LLM embeddings cannot be visualized, the principle remains the same – words that are close in meaning are grouped in space (Photo: GitHab)
A 2024 study showed that embeddings obtained using GPT‑3.5 Turbo and BERT AI models significantly improve the quality of text clustering. In the tasks of grouping news or reviews by topics, they allowed increasing cluster purity metrics and improving processing accuracy.
How Embeddings Help AI Understand the World
Embeddings enable neural networks to find connections between objects that are difficult to specify manually. For example, an online store’s recommendation system can determine that users interested in “hiking backpacks” often buy “hiking water filters” — even if these products are not directly related in the catalog. Embeddings capture statistical dependencies, user behavior, contexts, and even stylistic features of the text. This is the key to creating personalized services and scalable intelligent systems.
The main task of embeddings is to transform complex data (text, images, behavior) into a set of numbers, in other words, a vector that is convenient for algorithms to work with. Vectors help AI find similarities, understand meaning, and draw conclusions. Moreover, many things can be represented as embeddings: individual words, entire phrases and sentences, images, sounds, and even user behavior.
Texts and Language (NLP)
It is important for AI not just to “read” the text, but to understand what is behind it. Embeddings allow models to capture hidden connections between words, to determine that, for example, “cat” is closer to “animal” than to “car”. More complex models can create embeddings not only for words, but also for entire sentences – this helps to more accurately analyze the meaning of phrases, which is important, for example, for chatbots or automatic translation systems.
Images and visual content
In computer vision, embeddings allow you to turn an image into a set of features — color, shape, texture, etc. This helps algorithms find similar images, recognize objects, or classify scenes: for example, distinguishing a beach from an office.
Recommender systems and personalization
Modern digital platforms create embeddings not only for content (movies, products), but also for the users themselves. This means that each user’s preferences are also represented as a vector. If your vector is close to another person’s vector, the system can offer you similar content. This approach makes recommendations much more accurate.
How Embeddings Are Created: From Simple to Complex
Embeddings can be thought of as a multidimensional space where each point is an object (word, image, user). The proximity between points in this space reflects the learned similarity. For example, in Word2Vec (an algorithm that turns words into vectors that reflect their meaning and similarity in meaning), the vectors of the words “king” and “queen” will be close, and their difference will be close to the difference between “man” and “woman”. However, in more modern models (e.g., BERT), vectors depend on the context, and such linear dependencies are weaker.
There are different ways in which AI translates text, images, or sound into vectors—aka embeddings.
Classic text models (e.g. Word2Vec or GloVe) create one vector per word. The difficulty is that they do not take into account the context. For example, the word “onion” will mean both a vegetable and a weapon – the model will not understand the difference.
Modern transformer-based models (BERT, GPT, and others) work differently: they analyze the environment in which the word occurs and create a vector that corresponds to this very meaning. This is how AI understands what kind of “bow” we are talking about – green or rifle.
For images, embeddings are built differently. Neural networks trained on huge arrays of images “extract” visual features from them: colors, shapes, textures. Each object in the image is also represented by a vector.
Multimodal embeddings combine data from multiple sources at once — text, images, audio, video — and present them in one common vector space. This allows AI to find connections between different types of data. For example, to recognize that the caption “kitten playing with a ball” refers to a specific moment in a video or a fragment in a photo.
Embeddings are at the heart of recommendation systems, voice assistants, computer vision, search systems, and many other applications. They allow us to find connections between objects, even if these connections are not explicitly stated.
More and more often, attention is paid to adapting embeddings to specific tasks. For example, a model can not just “understand what the text is about,” but form a representation specifically for the desired purpose – be it legal analysis, customer support, or medical expertise. Such approaches are called instruction-tuned and domain-specific.
Where Embeddings Live: Cloud Servers for AI
Training and using embeddings is a resource-intensive process. Especially when billions of parameters and multimodal data are involved. Such tasks require:
a large amount of computing power with GPU resources (specialized graphics processors designed for resource-intensive tasks);
storage of vector databases;
fast indexing and searching for nearby vectors;
low latency in response generation, such as in chatbots and search.
Therefore, the development of embeddings is closely linked to the growing demand for cloud computing and infrastructure optimized for AI workloads. To work with embeddings, businesses need not just virtual machines, but specialized servers with GPU support, high-speed storage, and flexible scalability.
Such cloud solutions allow you to train and retrain your own models, launch services based on LLM, and integrate AI algorithms into websites, applications, and analytical systems. Cloud servers remove the barrier to entry into AI — businesses do not need to invest in their own cluster, they just need to choose the appropriate configuration for their model or service.
Today, embeddings are the basis of search, recommendations, content generation and automation. In the coming years, they will become even more complex, individualized and contextual. AI will increasingly recognize the meaning, goals of the user, the context of interaction – and offer relevant answers. According to analysts , the global artificial intelligence market will grow by about 35% per year and will amount to $1.8 billion by 2030.
But without a reliable infrastructure — fast, scalable, with support for vector databases and GPUs — such systems will be either slow or unavailable. That’s why the development of embeddings and cloud infrastructure go hand in hand: the former provides intelligence, the latter — power and flexibility.
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