The seminar consists of a “Wissenschaftsmodul” and a “Fachmodul”, you need to participate/enroll in BOTH seminars.
Learning to use the tools of the future.
The transformative power of Artificial Intelligence (AI) will fundamentally change the creative professions, with technology not designed to replace humans but requiring new skills to harness it.
Critical engagement and responsible use of AI tools.
AI systems can be used to generate targeted "fake news" on a large scale, so in order to learn how to use this technology in a responsible way, knowledge that goes beyond the purely technical aspects is needed. A mature and informed approach to sociology and politics is therefore just as important as programming skills. Morals and ethics are basic democratic values of our society and a qualification in these areas could expand the field of work of students in the art and culture sector by essential aspects. Especially at the Bauhaus University, great importance has always been attached to the teaching of human sciences, which therefore forms a perfect basis for an extended knowledge transfer in the field of AI.
new roles for creatives
In the current debates concerning creativity and AI, the views of computer science experts are dominant. However, since they do not have expert knowledge in the field of art and culture, the discourses prevailing there resemble more personal opinions than actual facts. By developing skills in technical and theoretical areas, students will be enabled to contribute to the debate as "experts on creativity" in a direct and informed way.
the problem of the online API
AI software such as ChatGPT is only available through APIs (Application Programming Interfaces). Applications run on the servers of large providers such as Google and Amazon, and users do not have direct access to the software or to the storage of their data. Various data protection authorities within Europe have blocked ChatGPT and the data protection compliance of such APIs is under discussion in the EU. The use of such APIs in a university context is therefore questionable. Installation on local systems also gives students a direct insight into how they work, which is also not possible with an API.
Edge computing (IoT), machine learning, cloud computing, and data visualization form a single entity. AI systems cannot be analyzed meaningfully in isolation, but only in conjunction with other key technologies such as edge computing (IoT), machine learning, cloud computing and data visualization. In order to understand the phenomenon of AI in its complexity, a "sandbox scenario" is useful. This gives students a direct insight into the real-world application of AI and its cyclical processes. Collecting, storing and analyzing data in a lab setting (e.g.
video feeds and speech analysis) is essential to understanding AI. After processing the data, AI models are created that can be used for prediction.
- Introduction to the use of Jupyter Notebooks
- Data structure and preparation using Python and Pandas
- Machine Learning Models: Linear Regression, Support Vector Machines, Decision Trees
- Introduction to NLP
Starting from technical competence, an ethical-moral compass can subsequently be developed, with which technology can be contextualized in a social, political and cultural context in a scientifically sound manner. In this context, AI is seen as an opportunity to broaden the activity profile of those working in the cultural sector to become a mediator between culture, business, technology and politics and, above all, to inform and involve the general public in these processes. This development therefore calls for training in the creative fields away from production and toward coordination and evaluation.
transparency and sustainability
The focus of teaching should be on the greatest possible transparency and make all results open source and thus sustainably available. It should also provide as authentic an introduction to the topic as possible. Another building block in this sense is hybrid teaching and learning, which is also under the sign of sustainability.
teaching and learning
In the course of dialectical knowledge transfer, a direct combination of technical and scientific modules makes sense. In this way, critical positions and questions can be developed directly from a technical understanding. The technical knowledge imparted can thus be placed directly in a critical context. Instead of a discussion about metaphors in pure science or about a supposedly objective and uncritical approach to technology, a dialectical cognitive process will thus be initiated. The didactic process leads away from active action and the direct production of cultural goods towards a reflexive attitude and their expert evaluation.