What are "embeddings"?
Embeddings are a way to represent real-world objects—such as words, images, or even sounds—as points in a mathematical space. Think of this space as a giant map, where each point represents an object, and the distance between points indicates how closely related they are. This concept is particularly useful in machine learning and artificial intelligence because it helps computers understand and process complex information in a way that’s somewhat similar to how humans think.
Imagine you have pictures and their descriptions. In the real world, we know that the word “cat” relates to a picture of a cat, whereas a dog is quite different. Embeddings allow a machine to understand these relationships by placing these words close to each other on the map. For instance, the picture of a cat and its description "cat sleeping in bed" will be close to each other, whereas a picture of a dog would be located in a different part of the embedding space.
Embeddings are powerful because they allow machine learning models to capture and understand the complex relationships in data without human intervention - they are learned directly from data. This ability to learn from data allows models to handle tasks that would be too complex for humans to define manually. For example, in text search engines or recommendation systems, embeddings can help find similar texts or suggest related products by comparing their positions on this map. A machine learning model can take two pieces of text, convert them into embeddings, and then easily measure how similar they are by seeing how close they are on the map.
How do embeddings work?
At a high level, embeddings convert raw data—like words, images, audio, or even user preferences—into numerical representations called vectors. These vectors enable the machine to recognize patterns, similarities, and relationships between different pieces of data, making it possible for AI systems to perform tasks like text understanding, image recognition, and personalized recommendations.
To grasp how embeddings work, it helps to understand the concept of vectors in mathematics. A vector is simply an array of numbers, and each number represents a position along a specific dimension. For example, a simple vector might look like this: [2.3, 4.5, -1.2]. Each value in the vector corresponds to a feature of the data it represents. The beauty of embeddings lies in their ability to represent complex data in a way that machines can easily interpret.
For instance, imagine you’re dealing with words in a text. Traditional machine learning models might use a method called one-hot encoding, where each word is represented as a unique binary vector with many zeros and just one "1" at a specific position. However, this approach doesn’t capture the relationships between words. Words like "king" and "queen" might end up looking completely unrelated, even though they are semantically similar.
Embeddings solve this problem by placing words, or any other data types, into a continuous, high-dimensional space where similar objects are positioned close to each other. This means that the word "cat" would be placed near the picture of a cat, and the difference between their vectors might be similar to the difference between "dog" and picture of a dog. The resulting vectors capture not just the identity of the objects but also their relationships and characteristics.
These vectors, known as embedding vectors, are generated through a process that involves training on large datasets using algorithms like neural networks. During training, the model learns to adjust the positions of the data points in this space so that objects with similar properties end up close together. For example, in a recommendation system, a model might learn to place a user's preferences and a movie’s features close together if that user is likely to enjoy the movie.
Once the embeddings are created, they are typically stored in a special type of data base commonly known as vector database. Vector databases are optimized for high-dimensional vectors and executing similarity searches. You can learn more about vector databases and what role they play in modern AI systems in this blog.
Why are embeddings important?
Embeddings are used across many domains, including natural language processing (NLP), computer vision, recommendation systems, and recently also structured data - instead of “classic” machine learning. Read on to learn what role embeddings play in their most popular use cases and applications:
Semantic Search and Information Retrieval: Traditional search engines rely on keyword matching, which can often lead to irrelevant results. Embeddings enable semantic search, where the engine understands the meaning behind the words, leading to more accurate and contextually relevant results. For instance, if you search for “best places to eat near me,” a semantic search engine powered by embeddings will understand your intent and suggest relevant options rather than just matching keywords.
Recommendation Systems: Companies like Netflix, Amazon, and Spotify use embeddings to power their recommendation engines. By converting both user preferences and content (like movies, products, or songs) into embeddings, the system can match users with items that closely align with their tastes, leading to personalized and relevant recommendations.
Natural Language Processing (NLP): In NLP, embeddings are used to represent words or phrases in a way that captures their meanings and relationships to other words. This is why AI models like OpenAI's GPT can generate contextually relevant and coherent responses. Embeddings allow these models to understand the context of a conversation, leading to more accurate and human-like interactions.
Computer Vision: Embeddings are also crucial in computer vision tasks such as image recognition, object detection, and facial recognition. By converting images into vector representations, AI models can more easily identify and classify visual patterns, enabling applications like automated image tagging and security systems.
Graph Analysis: Embeddings are used to represent nodes in a graph (like social networks or recommendation systems) in a continuous space, preserving the relationships between them. This allows for efficient analysis and insights, such as identifying community structures or predicting future connections.
Predictive Modeling: Embeddings, however, transform structured (tabular) data into a continuous vector space that preserves the relationships and patterns within the data, making it easier to build accurate predictive models. By converting structured data into embeddings, we can capture more nuanced and meaningful insights, enabling models to predict outcomes more effectively with less manual effort than “classic” machine learning requires. Representing tabular data as embeddings, it becomes much easier to join tables and train predictive models (neural networks):
What are the benefits of embeddings?
Traditional ML approaches often rely on simple representations of data, like one-hot encoding for categorical variables. While these methods can work in certain contexts, they fail to capture the relationships between data points, leading to less accurate and less efficient models. Embeddings overcome these limitations by providing a richer, more nuanced representation of data, enabling AI systems to perform better across a variety of tasks.
Dimensionality Reduction: Embeddings reduce the complexity of high-dimensional data by representing it in a lower-dimensional space. This not only makes data easier to work with but also improves the computational efficiency of ML models, allowing them to process large datasets faster and with less computational power.
Improved Model Generalization: By capturing meaningful patterns and relationships within data, embeddings help ML models generalize better to new, unseen examples. This is particularly important in scenarios where labeled data is limited or where the model needs to perform well across a variety of tasks.
Semantic Representation: Embeddings enable models to understand and interpret data in a way that captures the underlying semantics. This is crucial for tasks like language translation, where understanding the meaning of words and sentences is more important than just recognizing them.
Democratize Predictive Analytics: Using embeddings to build predictive models on structured data offers a powerful solution to many of the challenges inherent in traditional machine learning approaches. Traditional models often require extensive data preprocessing, feature engineering, and optimization to perform well, particularly when dealing with complex structured data. The embedding approach to predictive modeling not only reduces the effort and AI expertise required to develop predictive models, but also opens up advanced analytics, such as understanding the factors driving trends or customer behavior, to a broader audience.
Versatility Across Domains: Whether it's text, images, or structured data, embeddings can be applied across different types of data and tasks. This versatility makes them a critical tool in the AI toolbox, enabling a wide range of applications from chatbots to autonomous vehicles. That’s also because they facilitate transfer learning, allowing AI models to leverage pre-trained embeddings from large-scale datasets and fine-tune them for specific tasks or domains with limited labeled data. This transfer of knowledge accelerates model convergence and improves performance, especially in scenarios where annotated data is scarce or expensive to obtain.
How Featrix can help you apply AI to your data
Featrix is a platform for using embeddings to create predictive models on structured data. Many of the actually interesting questions about structured data are still challenging to answer using traditional analytics and machine learning models. For example, while compiling trends in transactional data every which way is easy, trends don't answer most questions analysts are after, like what causes a particular trend; for example why did revenue flatten out, or what factors makes customers consider canceling the service you're providing. Applying generative models to structured data into embeddings opens up these types of analysis to more automation. You can think of Featrix as LLM for your structured data. 
Featrix works in two steps:
- Data Evaluation: Featrix begins by assessing the inherent structure of the data to determine its suitability for modeling.
- Predictor Construction: Once deemed suitable, Featrix constructs predictors based on embeddings as "neural functions", bypassing the need for extensive optimization.
Unlike traditional machine learning approaches where a data scientist typically spends 80% of their time on cleaning and preparing the data, and significant AI expertise is required to obtain performant models, Featrix works on raw data and delivers performant models often in a single step.