Imagine you could translate words, images, or sounds into numbers – in such a way that similar content gets similar numerical values. These are embeddings: mathematical translations that enable computers to "understand" meanings.
What is an Embedding?
An embedding is a numerical representation (vector) of data that captures its meaning and relationships. They translate complex content into sequences of numbers, so that similar content is mathematically similar. You can view an embedding as the "compressed essence" of an object.
What form does an embedding take?
An embedding is a mathematical vector – a series of numbers. For example, "cat" might be represented as [0.2, -0.4, 0.7], "dog" as [0.3, -0.5, 0.6], and "car" as [0.8, 0.1, -0.2]. "Cat" and "dog" have similar values and are close together in mathematical space, while "car" is further away. In real applications, embeddings often have hundreds or thousands of dimensions.
What types of embeddings exist?
The most important types are:
- Word embeddings: Represent individual words and their relationships
- Document embeddings: Capture the meaning of entire sentences or documents
- Image embeddings: Encode visual content and features of images
- Audio embeddings: Convert sounds and speech into numerical representations
How is an embedding created?
Embeddings are generated using machine learning models, typically neural networks. The model learns to recognize important features and thus compresses high-dimensional data into fewer dimensions – an intelligent compression. Different techniques are used depending on the data type:
- For images, often Convolutional Neural Networks (CNNs)
- For words, models like Word2Vec, GPT, or BERT
- For audio, techniques like Wav2Vec or VGGish
Can embeddings be visualized?
Since embeddings are high-dimensional, visualizing them is challenging. With mathematical techniques like t-SNE, they can be reduced to 2D or 3D for display. This allows us to see how similar concepts are grouped in space.
What is the connection between LLMs and embeddings?
Embeddings are the cornerstone of Large Language Models (LLMs)! In models like GPT, words are first converted into embeddings that capture both meaning and context. Without embeddings, modern language models wouldn't exist.
What are embeddings used for in practice?
Embeddings are the foundation for:
- Search engines that understand the intention behind search queries
- Recommendation systems for products, movies, or music
- Language models like ChatGPT
- Image recognition that finds similar images
- Translation services between languages
- Chatbots that link questions with appropriate answers
What is an example of embeddings in everyday life?
When your search engine delivers the right results despite typos, embeddings are working in the background. Or when your music service suggests songs that match your taste. Also when your smartphone can find images with content like "beach" without you labeling them as such.
What does our agency do with embeddings?
We use embeddings for:
- Intelligent search functions for websites
- Personalized recommendation systems
- Automatic content categorization
- Similarity analyses of various data types
Our understanding of this technology enables customized AI solutions that meet our clients' needs – without compromising ethical standards.
Conclusion
Embeddings are the bridge between human meaning and mathematical computer language. The concept is simple: similar things get similar numbers – and this opens up a world of possibilities for intelligent applications.
Questions about embeddings or their use in your project? Contact us for a consultation!