Data annotation is an important term in the fields of artificial intelligence, big data and smart data, as well as automation. In German, \u201eData Annotation\u201c means \u201edata enrichment\u201c. It describes the process of adding information or \"labels\" to existing data such as texts, images, or videos. This is particularly necessary when machines or computers are to learn to take on tasks independently using artificial intelligence.<\/p>\n
For example: If a company wants to develop an AI that can recognise cats in photos, many images must first be labelled with \u201ecat\u201c. People look at these images and mark where the cat is. Only through this data annotation do computers learn to recognise cats independently.<\/p>\n
Data annotation is therefore a fundamental building block for systems like voice assistants, translation programs, or self-driving cars to function reliably. It ensures that AI systems are trained with the correct information and can later deliver accurate results. In times of large amounts of data (Big Data), the importance of data annotation is continuously increasing and is an important step on the way to automating many processes.<\/p>","protected":false},"excerpt":{"rendered":"