We are no longer baffled by all the tasks algorithms can perform. And apparently, they are now even able to ‘explain’ their output. But is that something we really want?

Diffusion processes in nature are highly complex, and scientists strive to understand them in detail. With a new physics-aware neural network, we were able to model and predict such processes much more precisely than previously possible.

Different perspectives advance research. Yet Africa is considered all too rarely in this context. A fellowship program for young researchers aims to change that. It brings five talents from African countries to Tübingen to spend half a year working on research projects in machine learning.

Machines may drive you to work one day, but they currently still fail when faced with unusual situations or noisy data. That’s because machines see the world very differently from humans - but this gap is starting to narrow.

Researchers train a neural network to estimate – in just a few seconds – the precise characteristics of merging black holes based on their gravitational-wave emissions. The network determines the masses and spins of the black holes, where in the sky, at what angle, and how far away from Earth the merger took place.

Spatial soil variability makes a farmer's daily business challenging as it leads to varying growth conditions for field crops. Machine learning can help to map soil properties so that farmers can adapt fertilizing and irrigation management in a time- and cost-efficient way.