Description: Embedding learning is a technique used to represent data in a lower-dimensional space while preserving semantic relationships. This representation allows machine learning models to understand and process complex data, such as text, images, or audio, more efficiently. Embeddings convert discrete elements, such as words or pixels, into continuous vectors that capture similarity and relationships between them. For example, in natural language processing, words with similar meanings are represented as close vectors in the embedding space. This technique is fundamental in multimodal models, where different types of data are integrated, as it allows the model to learn jointly and leverage interactions between different modalities. Additionally, embeddings are scalable and can be trained on large datasets, making them a powerful tool for improving the accuracy and generalization of artificial intelligence models.
History: The concept of embeddings began to take shape in the 2000s, with the development of techniques like Word2Vec, introduced by Google in 2013. This technique revolutionized natural language processing by allowing words to be represented as vectors in a continuous space, facilitating the capture of semantic relationships. Since then, the use of embeddings has expanded to other areas, including images and audio, driving the development of multimodal models that combine different types of data.
Uses: Embeddings are used in a variety of applications, including natural language processing, computer vision, and recommendation systems. In natural language processing, they are employed to enhance the understanding of context and semantics of words. In computer vision, embeddings help represent visual features of images, enabling tasks such as classification and object detection. Additionally, in recommendation systems, embeddings are used to represent users and products, facilitating personalized recommendations.
Examples: An example of embedding use is the BERT model, which uses contextual embeddings to enhance language understanding in tasks such as machine translation and sentiment analysis. Another example is the use of embeddings in recommendation systems of platforms like Netflix, where both movies and users are represented to provide personalized recommendations based on similar preferences.
