More than one million papers are published every year in the field of biomedicine and life sciences – an overwhelming volume. To help navigate through the literature, we created a map of the entire landscape of biomedical research using machine learning tools, in the form of an interactive website that allows you to browse 20 million papers simultaneously and see connections between them.

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.

“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.

We can’t change how much the sun shines or how hard the wind will blow. But if we want to utilise renewable energy better, we need to take into account how weather and climate will change over time. Nicole Ludwig, an expert in machine learning and renewable energy systems, develops models that do just that.

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.

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.

A meeting in Vienna, a lecture in Boston, a conference in London – academic events such as these are a part of researchers’ everyday working lives. They are where researchers meet their scientific communities to discuss their own research, exchange ideas with others and develop new ideas for collaboration. But how do they get to London, Boston, or Vienna?

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.

Deep learning algorithms are very good at recognizing specific objects (e.g. a dog, a car) within an image ​(known as image classifiers)​. But how do they actually do that? Most often the mechanisms underlying an algorithm’s decision remain opaque. What if we could explain any ​such ​black-box algorithm intuitively and, by doing so, even learn from it?​

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.

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.

There is currently much debate about the ethics of Artificial Intelligence (AI), with one widespread view holding that AI should never be used to make consequential decisions affecting people. In this blog post, I suggest that on the contrary, rather than worrying about AI “making decisions” about us, we should should pay more attention to who commissioned the chain of technological action using AI rather than the technology itself.

Skepticism about the use of AI systems is widespread. Many say the systems are too opaque. Professor for Explainable Machine Learning Zeynep Akata wants to change that - and has made the user’s perspective the focal point of her research.

With hundreds of scientists, we have explored the properties of different neuron types in mice, monkeys and humans using novel experimental techniques and machine learning methods for data analysis. The result is a unique overview of the motor cortex in the brain and its evolution.

Single-molecule localization microscopy is a powerful method to image cellular structures with nanometer resolution. We developed DECODE, a deep learning based analysis algorithm that makes this technique faster and more precise.

Computer models are a great tool to analyze neuronal mechanisms in the brain, but tuning these models to match brain activity has long been a daunting task for scientists. We developed a new machine learning tool that automates this process and used it to develop a simulation environment for a retinal implant.

Who makes better medical diagnoses, an algorithm or a human? A philosopher specialized in technology, Thomas Grote, says viewing this as a rivalry isn’t productive. He argues in favor of focusing on the interplay of the two – and emphasizes the significance of philosophy.

Algorithms are becoming better and better at analyzing medical images and recognizing diseases. Researchers Christian Baumgartner and Sergios Gatidis – one an expert on artificial intelligence (AI), the other a radiologist – expect that algorithms will fundamentally change doctors’ work.

Computer Science Professor Ulrike von Luxburg speaks in an interview about the opportunities and challenges of trimming machine learning systems to fairness. Prof. von Luxburg also explains why she is convinced that people, rather than machines should resolve certain questions.

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.

The Bayesian formalism can add uncertainty to deep neural networks. But Bayesian deep learning has a reputation as cumbersome and expensive. No longer. Recent results show how to achieve calibrated uncertainty in deep networks efficiently, without affecting their predictive performance.