Embedding in AI is the representation of data in a lower-dimensional space to preserve the relationships between the data points. It is a technique commonly used in natural language processing (NLP) and computer vision (CV) to convert high-dimensional data into a compact, dense, and meaningful representation.

In NLP, the most common type of embeddings is word embeddings. They represent words as vectors of numbers, capturing their semantic and syntactic relationships. These vectors can then be used as input for various NLP tasks, such as sentiment analysis, named entity recognition, and machine translation.

In computer vision, image embeddings are used to represent images in a compact yet meaningful way. These embeddings can be used for tasks like image retrieval, object recognition, and image classification.

Embeddings are learned through a training process and are often used in deep learning models, such as neural networks. They allow the model to capture and preserve the relationships between data points, leading to improved performance for various AI tasks.