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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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