Germany

You will find information about our upcoming conference on "(Clinical) Neurotechnology meets Artificial Intelligence". The event takes place from May 8-10, 2019 in Munich (Germany). Project partners are located in Hamburg, Granada and Montreal. The project is funded by the Federal Ministry of Education and Research, as part of the ERANET Neuron program. In the upcoming months, we will fill this website with information about the program, travel and lodging and many more information about what we love to explore: the philosophy and ethics of neurotechnology and AI! Stay tuned!

Germany will spend €3 billion to boost its artificial intelligence capabilities over the next six years, as part of a belated effort by Berlin to catch up with leading AI nations such as China and the United States. The spending pledge is part of a national AI strategy approved by Angela Merkel's cabinet on Thursday, following a two-day seminar on digital challenges attended by the chancellor and her ministers. Berlin expects federal funding to be matched by the private sector, taking investment to at least €6 billion. "Today, Germany cannot claim to be among the world leaders in artificial intelligence," Ms Merkel told journalists after the meeting. "Our aspiration is to make'Made in Germany' a trademark also in artificial intelligence, and to ensure that Germany takes its place as one of the leading [AI] countries in the world."

If you're not into fashion, you may not recognize that name, but Karl Lagerfeld is to fashion as Wayne Gretzky is to hockey as Mick Jagger is to rock and roll as Steve Jobs is to consumer tech. He is, according to industry insiders, nothing less than a fashion god. Born in Hamburg, Germany in 1938, he designed his first line of clothing at the tender age of 17. His meteoric rise is legendary among creative directors and today at 83, he still has tremendous influence in the fashion world as creative director at Chanel and Fendi. Lagerfeld proved over decades that he had the creative vision to know what consumers would want next before they even knew themselves. He once said, "I am not a marketing person. I don't ask myself questions.

Technology is at its most impactful when it is applied to addressing big problems. Perhaps there are no bigger problems than the occurrence of calamities, whether in the form of natural disasters, epidemics, or other catastrophic events. It is in response to seismic events, that fast actions can ameliorate acute conditions and mitigate potentially greater disaster. Such was the case in the wake of the recent super Hurricane Florence which had a massive regional impact when it struck the continental U.S. and devastated the Carolinas with severe flooding and widespread damage. It was in the wake Hurricane Florence that Munich Reinsurance Company, commonly known as Munich Re, stepped into the aftermath to help devastated homeowners and business owners get back on their feet, as it has in response to past calamities.

The warning by Lars Klingbeil, general secretary of the Social Democratic Party, comes less than two weeks before Chancellor Angela Merkel's squabbling coalition is due to sign off on the blueprint. We have to stop taking it easy." Policy makers in Europe's largest economy have been late to realize that its industrial export model is vulnerable at a time of rapid digitalization and growing trade frictions. Germany, handicapped by an outdated research infrastructure and restrictive data protection laws, has yet to produce a world-beating startup that pioneers the use of AI, although 100 companies have formed a lobby group to contribute to the policy process. Klingbeil, whose party is Merkel's junior partner in government, faulted Germany for taking its eye off the ball and getting caught up in the "hysteria" around a three-year-old immigration crisis while other countries were investing in AI research.

FILE - In this Thursday, Oct. 23, 2014 file photo, a member of the Organization for Security and Co-operation in Europe (OSCE) mission to Ukraine watches a drone take off during a test flight near the town of Mariupol, eastern Ukraine. Germany and France say it appears that Russian-backed separatists in Ukraine downed a drone being used by neutral European observers and are demanding accountability. In a joint statement, they said Thursday, Nov. 1, 2018 the downing is a "clear violation" of the Organization for Security and Cooperation in Europe's Special Monitoring Mission in Ukraine, which is to have unimpeded access.

In 2002, the UCR time series classification archive was first released with sixteen datasets. It gradually expanded, until 2015 when it increased in size from 45 datasets to 85 datasets. In October 2018 more datasets were added, bringing the total to 128. The new archive contains a wide range of problems, including variable length series, but it still only contains univariate time series classification problems. One of the motivations for introducing the archive was to encourage researchers to perform a more rigorous evaluation of newly proposed time series classification (TSC) algorithms. It has worked: most recent research into TSC uses all 85 datasets to evaluate algorithmic advances. Research into multivariate time series classification, where more than one series are associated with each class label, is in a position where univariate TSC research was a decade ago. Algorithms are evaluated using very few datasets and claims of improvement are not based on statistical comparisons. We aim to address this problem by forming the first iteration of the MTSC archive, to be hosted at the website www.timeseriesclassification.com. Like the univariate archive, this formulation was a collaborative effort between researchers at the University of East Anglia (UEA) and the University of California, Riverside (UCR). The 2018 vintage consists of 30 datasets with a wide range of cases, dimensions and series lengths. For this first iteration of the archive we format all data to be of equal length, include no series with missing data and provide train/test splits.

Our mission is to provide a novel artistic painting tool that allows everyone to create and share artistic pictures with just a few clicks. We are five researchers working at the interface of neuroscience and artificial intelligence, based at the University of Tübingen (Germany), École polytechnique fédérale de Lausanne (Switzerland) and Université catholique de Louvain (Belgium).

Information-rich representations of text often decrease sample complexity when an natural language processing (NLP) system is trained on a task. One effective way of producing such representations is the traditional NLP pipeline: tokenization, tagging, parsing etc. An alternative are so-called embeddings that represent text in a high-dimensional real-valued space that is smooth and thereby supports generalization. Most commonly, words are represented as embeddings, but more recently contextualized embeddings like ELMo have been proposed. I will address two challenges for embeddings in this talk.

Researchers are planning to recreate the conditions of the lunar surface right here at home. A new facility in the works at ESA's Astronaut Centre in Cologne, Germany will soon serve as a three-part moon analogue environment on Earth, the agency announced this month. There, scientists will simulate lunar soil and a moon habitat, powered by systems that could one day be used to support a real base on the moon. Researchers are planning to recreate the conditions of the lunar surface right here at home. A new facility in the works at ESA's Astronaut Centre in Cologne, Germany will soon serve as a three-part moon analogue environment on Earth.