The term embeddings is primarily found in the fields of artificial intelligence, big data and smart data, and digital transformation.<\/p>\n
In artificial intelligence, embeddings describe a method of converting information such as words, images, or products into a special numerical form \u2013 known as vectors. These numbers help computers to better understand and compare meanings and relationships. You can think of embeddings as translators: they convert complex content into a format that a machine can work with.<\/p>\n
A vivid example: if an online shop wants to provide product recommendations, an AI learns which products are similar through embeddings. In this way, the computer recognises that trainers are \u201ered\u201c and \u201esporty\u201c and recommends similar models, even if not all data points match exactly. So, embeddings make relationships visible that are hidden at first glance.<\/p>\n
This makes embeddings an important building block for smart search functions, chatbots, or personalised recommendations, thus contributing significantly to the digitalisation and automation in many companies.<\/p>","protected":false},"excerpt":{"rendered":"