The term information bottleneck principle originates from the fields of artificial intelligence, big data, smart data and digital transformation. This principle helps to filter out the really important information from a large amount of information.
Imagine you have a huge pile of files with lots of documents, but you only need the pages that are relevant to your current task. The information bottleneck principle works in a similar way: in the world of data, it searches for the most important information and ignores everything that is not needed. The aim is to ensure that only the relevant data is used so that systems such as artificial intelligence can learn faster and more effectively.
An illustrative example: an AI is supposed to differentiate between photos of dogs and cats. Instead of analysing every detail of the images, the system focuses on essential elements such as ear shape or whiskers. This allows it to work much faster and with less computational effort by simply fading out unimportant parts of the image.
The information bottleneck principle ensures that digital systems can extract the most meaningful features from a large amount of data and utilise them more efficiently. This saves resources and improves performance.
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