Active learning is a term from the fields of artificial intelligence, big data, and automation. It describes a method where computer programmes or algorithms specifically learn – by actively asking for the most important information. The special thing about it is that the system decides for itself what data it needs to improve.
Imagine you have a company that should automatically sort emails into „spam“ and „not spam“. Instead of presenting the program with thousands of emails, it selects the most difficult or unclear cases itself and specifically asks for feedback. This way, the system learns much more efficiently and becomes more accurate at detecting spam more quickly.
Active Learning is used wherever large amounts of data are available, but not all information is equally important or easy to evaluate. This saves time and money, and increases the accuracy of automated systems, whether for fraud detection in the financial sector, image analysis in medicine, or customer service. In short: Active Learning makes machines smarter by asking themselves when and where they still need to learn something.
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