Algorithms learn a Sense of Fairness
Only if Artificial Intelligence interprets and applies fairness in the same way as humans do, will society accept it. That is why scientists from the Max Planck Institute for Intelligent Systems in Tübingen look at the causal reasoning behind data – because it matters how data comes about. Only when self-learning machines satisfy our ethical and legal requirements will the public accept them as just and fair.
Germany's most prestigious research funding prize - €2.5 million each for outstanding research work
The latest recipients of Germany's most prestigious research funding prize have been announced. The Joint Committee of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) chose ten researchers, three women and seven men, to receive the 2018 Leibniz Prize. The prizes will be awarded on 19th March 2018 in Berlin.
for his contributions to the theory and practice of machine learning
ACM's most prestigious member grade recognizes the top 1% of ACM members for their outstanding accomplishments in computing and information technology and/or outstanding service to ACM and the larger computing community.
An algorithm jointly developed by researchers of the Max-Planck Institute for Intelligent Systems and for Software Systems in Kaiserslautern promises to optimize which doubtful news stories to send for fact checking and when to do so, helping to prevent fake news from spreading on Social Media.
The Algorithm EnhanceNet-PAT is OK not being perfect – but shows a better result (Talk at ICCV 2017, Venice)
Scientists at the Max Planck Institute for Intelligent Systems in Tübingen utilize the Artificial Intelligence of a software to create a high definition version of a low resolution image. While the pixel-perfectness is being sacrificed, the reward is a better result.
For the fifth time, the MLSS takes place in Tübingen
Bernhard Schölkopf joined the initiative "Latest Thinking"
Exoplanets are planets beyond our own solar system. Since they do not emit much light and moreover are very close to their parent stars they are difficult to detect directly. When searching for exoplanets, astronomers use telescopes to monitor the brightness of the parent star under investigation: Changes in brightness can point to a passing planet that obstructs part of the star’s surface. The recorded signal, however, contains not only the physical signal of the star but also systematic errors caused by the instrument. As Bernhard Schölkopf explains in this video, this noise can be removed by comparing the signal of the star of interest to those of a large number of other stars. Commonalities in their signals might be due to confounding effects of the instrument. Using machine learning, these observations can be used to train a system to predict the errors and correct the light curves.
CYBATHLON Championship for Athletes with Disabilities
Zürich. On October 8, 2016, a collaboration of the research group "Brain-Computer-Interfaces" at the MPI-IS and the "Autonomous Systems Lab" at the TU Darmstadt will send a joint team into the Brain-computer-Interface Race at the Cybathlon 2016 in Zurich. The so called Athena-Minerva team consists mainly of computer science students of bachelor and master-level at the Technical University Darmstadt. They are interested in "Machine Learning", signal processing and especially for Brain-Computer-Interfaces (BCI). The team is headed by Moritz Grosse-Wentrup from MPI-IS and by Jan Peters, TU Darmstadt. The pilot is Sebastian Reul.
Leopoldina - National Academy of Sciences
Founded in 1652, the Leopoldina is one of the oldest academies of science in the world. It is dedicated to the advancement of science for the benefit of humankind and to the goal of shaping a better future. With some 1,500 members, the Leopoldina brings together outstanding scientists from Germany, Austria, Switzerland and many other countries.