With a travelling exhibition, this project made it possible to experience how everyday life and the world of work are changing as a result of artificial intelligence (AI). The target group of the eight learning stations are pupils and trainees as well as teachers and trainers. Visitors to the exhibition should also gain an insight into the basic characteristics of AI and try out practical examples of how it works in the background of everyday activities on the Internet. The Federal Ministry of Education and Research funded the exhibition as a project of the Science Year 2019.

The design of the learning stations is based on the common understanding of the project consortium: AI means the realisation of human-like, intelligent behaviour in machines or computers.

AI offers a variety of methods and applications to realise rational behaviour. Expert systems are AI systems that use symbolic procedures to represent the knowledge of human experts in a specific area and can thus solve problems in this area. The knowledge bases are usually created manually and are therefore costly to produce; however, they can be created without existing example data. Learning systems, on the other hand, use machine learning methods to generate models from existing data that can be applied to new data. Decision-making mechanisms are not coded manually, but learned by the system itself. This requires the selection and configuration of a suitable learning procedure and a large amount of training data. The successes of AI in recent years, including in application domains such as text translation and image recognition, are based on learning systems that use the subsymbolic method of Deep Learning.

The WAVE learning stations were not job-specific, but showed the young people in broad terms where AI systems are already influencing our working and living environments today, mostly working in the background without them perhaps being aware of who or what is interacting with them, their data or words.

This project was supported by the BMBF under the funding code 01WJ1912A.

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