Knowledge Distillation is a term from the fields of Artificial Intelligence, Big Data and Smart Data, and Digital Transformation. It describes a method used to \u201esimplify\u201c large, powerful AI models for faster and more efficient deployment \u2013 for example, on smartphones or small devices.<\/p>\n
Imagine you have an experienced teacher (the large AI model) and a student (the smaller model). The teacher knows a great deal and can solve complex tasks. With Knowledge Distillation, the teacher \u201eteaches\u201c the student how to solve these tasks as well as possible with fewer resources. The student learns to recognise and implement the most important things in a short time.<\/p>\n
A clear example: Let's take facial recognition on a mobile phone. The original AI model is huge and requires a lot of computing power, for instance on a server. Thanks to knowledge distillation, the same can be run on your smartphone \u2013 the model is smaller, but still reliably recognises people. This makes AI faster, more energy-efficient, and cheaper in practice. This is particularly important in industry, for apps, and for smart devices.<\/p>","protected":false},"excerpt":{"rendered":"