Explainer: How AI Embeddings Work in Language Models

A new visual explainer video delves into the concept of AI embeddings, which are numerical representations that allow neural networks to process text. Each word or token is mapped to a vector in a multidimensional space, with similar contexts resulting in similar representations. This mapping is crucial for language models, as it helps them understand and generate text by using context and attention mechanisms. The explainer highlights how embeddings evolve through model layers, adapting to different contexts.