The new Face Embedding model by Clarifai enables users to organize, filter, and rank facial images based on visual similarity by transforming them into numerical vectors in a 1024-dimensional space. This model, derived from the Face Detection model, measures visual similarity by calculating the distance between these vectors, with closer vectors indicating greater similarity. The provided tutorial demonstrates how to build a simple application that compares facial similarity between parents and their child using Python and the Numpy library. The process involves initializing the Clarifai app, obtaining embeddings for each face in the images, and calculating the distances between them to determine which parent the child resembles more. The tutorial concludes by encouraging users to experiment with their images and explore other potential applications of face embeddings, such as facial recognition-based searches and deduplication of similar images.