According to studies by innovation researcher and AI expert Dietmar Harhoff, Germany is quite advanced in basic AI research on an international scale. However, when it comes to translating these innovations into patents and practical applications in businesses, there is significant room for improvement. Notably, there are now 28 out of a planned 100 professorships for AI, and there is a range of academic educational offerings on AI, particularly in the field of computer science.
There is still a huge gap between experts, professionals, and the "ordinary" citizens. It's no wonder there's a lot of confusion, with feelings ranging from fear to carelessness, as seen in discussions about data protection aspects of the Corona app on Facebook.
However, many people want to learn about AI without committing to a degree program. These individuals have questions like:
- I keep hearing about AI, Big Data, IoT – what are these, and how do they affect my life?
- Aren't data and information the same thing?
- Data is used everywhere, but what do I gain from it?
- How does data protection actually work?
For this audience, there are few educational offerings. One of these is the relatively new AI Campus, a digital learning platform around AI, supported by the Federal Ministry of Education and Research (BMBF). The Stifterverband, the German Research Center for Artificial Intelligence (DFKI), the Hasso Plattner Institute (HPI), NEOCOSMO, and the mmb Institute have been developing the AI Campus together since October 2019. It features outstanding resources that are accessible, engaging, and very practical.
So, what role does statistics play in this? I'm discussing this with Tim Friede (DAGStat) and Gerd Antes (Cochrane Deutschland Foundation) on the occasion of the third World Statistics Day by the UN:
Mastering AI requires a high level of data competence. Data competence or Data Literacy includes not only the more technical skills such as data management, data analysis, machine learning, or visualization.
Rather, Data Literacy is essential for answering the following questions:
- How do you transition from a real-world problem to a problem that can be solved with data and algorithms?
- What is in the data, and what is not?
- How do you correctly interpret the results and implement them into action?
Therefore, it is extremely positive that statisticians are increasingly playing a role in the discussion around AI. For example, the Data Literacy Framework of the Stifterverband, which is now used internationally in higher education, is based on a study by computer scientists and was developed by statisticians.
HFD_AP_Nr_47_DALI_Competence_Framework_WEB.pdf
Now, the challenge is to translate such a competence framework into learning objectives and curricula, and not just (only) in data science programs, but in all other disciplines: medicine, journalism, urban planning, but also in adult education and in schools. This broad transfer is the crucial step in conveying an understanding of the opportunities presented by AI. In this context, statistics, which is almost always in close contact with the disciplines, can make a decisive contribution as a "translator."