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Department News

Initiative to establish a European Lab for Learning & Intelligent Systems

  • 24 April 2018

Warning about the competition from the USA and China - appeal to politics

Bernhard Schölkopf Linda Behringer

Award Ceremony of 2018 Leibniz Prize

  • 19 March 2018

Bernhard Schölkopf receives one of the Leibniz Prizes in Berlin - short video about his and his team´s research on machine learning (in German)

"I am very pleased about the Leibniz Prize", says Director Bernhard Schölkopf. "I see it as an award for all my employees and for the research field of machine learning, and it should benefit both in the future. We are only at the beginning, and want to investigate further how computers and living beings can learn to better understand the organizational principles of intelligent behavior."

Bernhard Schölkopf

Süddeutsche Zeitung publiziert Meinungsbeitrag von Bernhard Schölkopf

  • 16 March 2018

Der Direktor der Abteilung für Empirische Inferenz veröffentlicht einen Gastbeitrag über Künstliche Intelligenz auf Seite 2 der Süddeutschen Zeitung. Thema ist die Kybernetische Revolution und dass Europa dafür die besten Köpfe braucht.

Bernhard Schölkopf Linda Behringer Claudia Daefler

The Question is Why

  • 19 December 2017

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.

Niki Kilbertus Linda Behringer

Bernhard Schölkopf receives Leibniz Prize 2018

  • 14 December 2017

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.

Bernhard Schölkopf Claudia Daefler

Bernhard Schölkopf elected ACM Fellow (2017)

  • 11 December 2017

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.

Bernhard Schölkopf

Artificial Intelligence to the rescue in finishing off Fake News

  • 27 November 2017

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.

Manuel Gomez Rodriguez Linda Behringer

From small to not so pixel-perfect large

  • 26 October 2017

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.

Linda Behringer Claudia Daefler Mehdi S. M. Sajjadi Michael Hirsch Bernhard Schölkopf

The Machine Learning Summer School 2017 is back in Tübingen!

  • 19 June 2017

For the fifth time, the MLSS takes place in Tübingen

How Can We Use Machine Learning in the Search for Exoplanets?

  • 01 February 2017

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.

Bernhard Schölkopf