Among the many advantages of the expanding field of artificial intelligence is its ability to make sense of vast and complex data. A fundamental challenge in processing real-world information is determining similarity. While computers excel at precise numerical calculations, they struggle with computing similarity between complex objects like images, text or speech. Embeddings are the solution.
Embeddings, an essential concept in machine learning (ML) and natural language processing (NLP), are specialised techniques for transforming intricate data into simpler, more understandable forms. They do this by converting high-dimensional information, like text or images, into compact vectors of numbers—a process often called dimensional reduction. This transformation helps models capture the underlying meanings and relationships within data that inherently features a large number of attributes—data that might otherwise be impossible to interpret.
Simply put, embeddings play a crucial role in machine learning by turning complex data into simplified, manageable forms. This, in turn, creates several advantages:
Dimensionality reduction simplifies large datasets by transforming them into lower-dimensional representations. Embeddings reduce the number of dimensions without losing essential information, making the data more manageable and improving the efficiency of machine-learning models.
Embeddings capture the semantic meaning of data, enabling models to understand and interpret complex relationships. This capability enhances natural language processing tasks (such as sentiment analysis and machine translation) by allowing the model to grasp subtle nuances in language.
Large language models (LLMs) benefit significantly from embeddings. Embeddings provide a foundation for these models to understand and generate human-like text. By representing words and phrases as vectors, LLMs (such as GPT models) can produce coherent and contextually relevant responses. This improves the accuracy and relevance of applications such as chatbots and generative AI (GenAI).
Using embeddings, techniques like t-SNE (t-distributed stochastic neighbour embedding) help create meaningful visual representations of data clusters and relationships. This visualisation aids in understanding data patterns, detecting anomalies and making informed business decisions.
Just as there are many different types of complex data that machine learning algorithms must be able to process to function effectively, there are various embedding models that each offer unique ways to represent that data. Among the most common embedding models are:
PCA is a statistical method used for dimensionality reduction. It identifies the directions (also called principal components) in which the data varies the most and projects the data onto these directions. This results in simplified vectors that capture the essential features of the original data, making it more manageable for analysis.
Developed by Google, word2vec is a neural network-based model that generates word embeddings. It captures semantic relationships between words by training on large text datasets. Word2vec has two main variants: continuous bag of words (CBOW) and skip-gram. CBOW predicts a target word from its context, while skip-gram predicts the context given a target word. Both methods create complex vector representations that reflect the meanings and relationships of words.
SVD is a technique used in matrix factorisation, which is a process that breaks down a large matrix (an array of numbers used to represent complex data) into simpler, more manageable pieces. Matrix factorisation is needed to identify underlying patterns and relationships in the data. SVD decomposes a matrix into three other matrices, capturing the essential structures in the original data. In text data, SVD is often used in latent semantic analysis (LSA) to find hidden semantic structures, allowing the model to understand the similarity between words even if they do not frequently appear together.
Vectors are lists of numbers that represent data in a format that computers can easily process. Each number in a vector corresponds to a specific attribute or feature of the data. For example, in a machine learning model, a vector might represent a word by capturing various aspects like frequency of usage, context and semantic meaning. By converting complex data into vectors, embeddings allow these models to analyse and find relationships within the data more effectively—essentially turning non-numerical data into numerical data.
In embeddings, vectors are crucial because they enable similarity searches and pattern recognition. When a model processes vectors, it can identify which vectors are close to each other in multi-dimensional space. This proximity indicates similarity, allowing the model to group similar items together. Given a large enough dataset, this makes it possible for ML algorithms to understand high-dimensional data relationships.
Embeddings are typically created through the process of training machine learning models on specific tasks. This often involves setting up a supervised problem, known as a surrogate problem, where the primary goal is to predict an outcome. For example, a model might predict the next likely word in a sequence of text. During this process, the model learns to encode the input data into embedding vectors that capture the underlying patterns and relationships.
Neural networks are commonly used to generate embeddings. These networks consist of multiple layers, and one of the hidden layers is responsible for transforming the input features into vectors. This transformation occurs as the network learns from manually prepared samples. Engineers guide this process by feeding the network new data, allowing it to learn more patterns and make more accurate predictions. Over time, the embeddings become refined and operate independently, enabling models to make accurate recommendations based solely on the vectorised data. Engineers continue to monitor and fine-tune these embeddings to ensure they remain effective as additional data is introduced.
Embeddings are versatile and can be applied to diverse types of data, transforming them into vectors for machine learning models to process efficiently. Common objects that can be embedded include:
- Words
Word embeddings convert text into numerical vectors, capturing the semantic relationships between words. This is crucial for tasks like language translation and sentiment analysis. - Images
Image embeddings transform visual data into vectors, allowing models to recognise patterns and features within images. This is used in applications like facial recognition and object detection. - Audio
Audio embeddings convert sound waves into vectors, enabling models to understand and process spoken language, music and other audio signals. This is essential for speech recognition and audio classification tasks. - Graphs
Graph embeddings represent nodes and edges in a graph as vectors, preserving the structural information. This helps in tasks like link prediction, node classification and social network analysis
As previously addressed, embedding typically means transforming objects like text, images and graphs into vectors—arrays of numbers. These vectors allow models to recognise similarities and patterns within the data.
In recommendation systems, embeddings help by representing users and items as vectors in a high-dimensional space. Each user and item is assigned an embedding vector, learnt through historical interactions. The recommendation score for a user-item pair is computed by taking the dot product of their vectors—the higher the score, the more likely the user is to be interested in the item. This approach captures users' preferences and item characteristics, enabling personalised recommendations.
Text embeddings work differently. They are learnt as part of the LLM pretraining process. During pretraining, these models are exposed to vast amounts of text, allowing them to identify contextual relationships between words, phrases and sentences. The model assigns a unique vector to each word or phrase based on how often it appears with certain other words and in various contexts. This process enables the model to capture semantic nuances, such as synonyms or relationships, within the text. This helps the model understand, generate and accurately process human language.
Embeddings have a wide range of applications in machine learning, making them indispensable for a range of tasks. Here are some notable examples:
- Computer vision
Embeddings are used to convert images into numerical vectors that capture the essential features and patterns within the images. This transformation enables tasks such as image classification, object detection and facial recognition. - Recommender systems
Embeddings help represent users and items (such as movies or products) as vectors. These vectors capture the latent features that reflect users' preferences and item characteristics. By comparing the similarity between user and item embeddings, recommender systems can predict which items a user might be interested in. - Semantic search
Semantic search uses embeddings to improve search results by understanding the context and meaning of queries rather than relying solely on keyword matching. Embeddings transform both the search queries and documents into vectors so the search system can find documents that are semantically similar to the user request. - Intelligent document processing
In intelligent document processing, embeddings help convert text data into vectors that capture meaning and relationships within the text. This is useful for tasks like document classification, sentiment analysis and information extraction. By using embeddings, models can better understand and process the content of documents.
Embeddings are transformative tools in machine learning, enabling the simplification and understanding of complex data. Organisations can leverage this capability, with advanced AI solutions from ServiceNow.
ServiceNow offers comprehensive capabilities to implement embeddings within its platform. ServiceNow's AI-powered applications can automatically classify and route tickets, predict issues before they occur and provide personalised recommendations—all powered by sophisticated embedding models. This integration makes it possible for companies in all industries to harness the full potential of their data.
Explore how ServiceNow can transform your data processing capabilities with the right approach to embeddings. Schedule a demo today, and see for yourself how AI in action can enhance your business operations.