Can we build neural networks whose structure and computational abilities match a real brain? We are not quite there yet, but our new work shows a strategy for getting closer to this goal.

Online learning platforms are popular tools for acquiring new knowledge on our own. However, these platforms have significant shortcomings. We present a new algorithm allowing us to trace the knowledge of learners more accurately, creating opportunities for empowerment by adapting the learning process to their personalized needs.

Bedartha Goswami’s goal is to build a bridge between machine learning and climate science. It’s not easy: when new methods in machine learning are developed, the ways that they can be applied in the climate sector is often not considered. Goswami is a team player, so his solution has been to put together a group with the interdisciplinary expertise needed for real breakthroughs in climate science.

Since the Cyber Valley initiative was established, there has been a controversial debate in Tübingen about research into artificial intelligence (AI) and machine learning. Of all the places in Europe, this picturesque town on the Neckar is supposedly one of the most important centres of AI research? For many, it is hard to imagine.

When children develop into adults, how they learn changes a lot. While children show a lot of random behaviour, adults perform more goal-directed actions. An influential theory describes these changes as being similar to the behaviour of an optimisation algorithm commonly used in machine learning. This empirical test shows that there are striking similarities but also important differences between human development and machine learning algorithms.

“AI and Sustainability” – Since April 4th, 2023, the science and debate platform te.ma has offered the public a discussion forum, a place to ask questions, or simply to inform themselves about the topic. With this new approach, te.ma and the Cluster of Excellence "Machine Learning" aim to provide new impulses in the complicated thicket of science communication that are focused on dialogue with the public.

With a PhD in machine learning, the world seems to lie at your feet. But what to do afterwards: academia, the IT-sector, or something else entirely? For Poornima Ramesh, the answer is clear: she wants to use machine learning to improve people’s lives in places where the problems are most urgent. To further this goal, she joined a global advisory, data analytics, and research organization.

Research in machine learning and data science in and from Africa has the potential to play a more significant global role and faces unique challenges. The pan-African network of AIMS (African Institute for Mathematical Sciences) and its postgraduate programmes prepare young Africans to contribute towards this goal.

Understanding the 3D nature of our world is key to many applications in augmented and virtual reality and simulation. But 3D training data is difficult to obtain. Hence, we develop an algorithm to create 3D graphics that can be trained with 2D images alone. By designing our algorithm such that it can represent 3D data efficiently, we keep the computational cost manageable while moving from 2D images to 3D graphics.

Artificial intelligence (AI) and democracy have many touchpoints. What is unclear, however, is whether AI will strengthen or weaken democracy in the long run. It is about time that we, as researchers and citizens, get more involved and develop ideas for a digitally competent democracy together.