Incremental Learning (Glossary)

Incremental learning is primarily at home in the field of artificial intelligence and in connection with big data and smart data.

Incremental Learning translates to „step-by-step learning“. Unlike traditional learning methods, a system here doesn't learn everything at once from a huge dataset, but rather adds a little bit at a time as new data emerges. This means all data doesn't have to be re-analysed every time – saving time and processing power. Incremental Learning is therefore particularly useful when new information is constantly being created, such as in large companies or online platforms.

A typical example: a spam filter for emails that uses incremental learning recognises a bit more about what fraudulent messages look like with every new spam email. If a new type of spam suddenly appears, the filter can continuously recognise it and improve itself – entirely without the need to retrain the entire system.

For businesses, Incremental Learning offers the advantage of keeping pace with dynamic changes by ensuring their AI solutions remain up-to-date. This allows them to respond more quickly and efficiently to new challenges or data.