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eth zurich

Everything AI?


Artificial intelligence is having a growing impact on our daily lives and is also revolutionizing research. ETH Zurich recognizes its responsibility in this area and is striving to promote innovation and trust in this fast-evolving technology. Sometimes a machine takes everyone by surprise. A recent example occurred at the opening event of Scientifica 2019, where ETH robotics specialists had trained a drone to welcome visitors by writing the word "enjoy." At first everything seemed normal as the drone, known as Voliro, began to write.

This robot can tell when sewers need repairing by scratching the walls

New Scientist

A four-legged robot that inspects concrete can walk through underground sewage tunnels and detect when they need repairing. Hendrik Kolvenbach at the Swiss Federal Institute of Technology (ETH Zurich) in Switzerland and his colleagues have developed a robot that scratches one of its legs against concrete to determine the condition it is in. The robot is waterproof and it can wade through water and climb over obstacles. Because many modern sewage systems were built decades ago, constant underground monitoring is needed to prevent major leaks.

Artificial Intelligence and Security Politics


Artificial Intelligence (AI) is a key driver of changes in the economy, society, and the state. As a result, it also has become a crucial issue in national and international political debates. In this theme, we focus on the effects of AI on defense and foreign policy as well as on internal security and democracy. In particular, we look at the anticipated changes in war and conflict caused by AI-based applications, the competition over AI-technology and resulting shifts in power, the regulation and governance of AI, and changes in the activities of intelligence services. In this CSS Policy Perspective, Sophie- Charlotte Fischer and Prof. Andreas Wenger warn that policymakers and experts increasingly view Artificial Intelligence (AI) within the narrow context of great power competition.

Max Planck ETH Center for Learning Systems


The Max Planck ETH Center for Learning Systems (CLS) offers a unique fellowship program, where PhD students are co-supervised by one advisor from ETH Zurich and one from the MPI for Intelligent Systems in Tübingen and Stuttgart. PhD students are expected to take advantage of the opportunities offered by both organizations and to actively seek cross-group collaborations. The Center also offers a wide range of activities like retreats, workshops, and summer schools, as well as the possibility to engage in organizing such events. This is an exciting new program and admission is highly competitive. Each PhD fellow will have a primary location (chosen based on interests and match) and spends one year at the other location as well.

ETH Meets Digital Festival Zurich


This kind of future may seem like science-fiction. But at Health's Digital Future, a special event titled "ETH Meets Digital Festival Zurich," researchers and partners from ETH Zurich gathered to discuss the realities of this kind of future. Moderated by Diplomatic Courier's own Contributing Editor Shalini Trefzer, the event featured leading voices in technology, entrepreneurship, research and healthcare, all discussing the future of digital health. A key takeaway from the event focused on the complexity of digital health, especially within large organizations. As brought up in the discussion between Novartis CDO Bertrand Bodson and ICRC s Director of Digital Transformation and Data Charlotte Lindsey-Curtet, one of the necessary components with digitizing large organizations is culture.

Microsoft Opens Research Laboratory In Zurich -


Global IT giant Microsoft has opened a new laboratory in Zurich, where it will collaborate with the Swiss Federal Institute of Technology Zurich in the areas of mixed reality and artificial intelligence. Microsoft has opened the Mixed Reality and AI Zurich Lab, where it will collaborate closely with the Swiss Federal Institute of Technology (ETH) in Zurich. According to ETH Zurich, the lab is already home to twelve Microsoft employees, four ETH Zurich doctoral students and one doctoral student from the Swiss Federal Institute of Technology Lausanne (EPFL). ETH professor Marc Pollefeys is the director. The new lab in Zurich is dedicated to researching mixed reality technologies and artificial intelligence.

Artificial Intelligence Proves 30% More Accurate Than Humans at Analyzing Dark Matter


This is a typical computer-generated dark matter map used by the researchers to train the neural network. A team of physicists and computer scientists at ETH Zurich has developed a new approach to the problem of dark matter and dark energy in the universe. Using machine learning tools, they programmed computers to teach themselves how to extract the relevant information from maps of the universe. Understanding how our universe came to be what it is today and what will be its final destiny is one of the biggest challenges in science. The awe-inspiring display of countless stars on a clear night gives us some idea of the magnitude of the problem, and yet that is only part of the story.

Machine Learning in Computational Biology (MLCB)


A strong submission to the workshop typically presents a new learning method that yields new biological insights, or applies an existing learning method to a new biological problem. However, submissions that improve upon existing methods for solving previously studied problems will also be considered. Examples of research presented in previous years can be found online at We specially encourage submissions describing work in progress and early results, for generating discussions helpful in shaping the presented work.

Driver Identification via the Steering Wheel Machine Learning

Driver identification has emerged as a vital research field, where both practitioners and researchers investigate the potential of driver identification to enable a personalized driving experience. Within recent years, a selection of studies have reported that individuals could be perfectly identified based on their driving behavior under controlled conditions. However, research investigating the potential of driver identification under naturalistic conditions claim accuracies only marginally higher than random guess. The paper at hand provides a comprehensive summary of the recent work, highlighting the main discrepancies in the design of the machine learning approaches, primarily the window length parameter that was considered. Key findings further indicate that the longitudinal vehicle control information is particularly useful for driver identification, leaving the research gap on the extent to which the lateral vehicle control can be used for reliable identification. Building upon existing work, we provide a novel approach for the design of the window length parameter that provides evidence that reliable driver identification can be achieved with data limited to the steering wheel only. The results and insights in this paper are based on data collected from the largest naturalistic driving study conducted in this field. Overall, a neural network based on GRUs was found to provide better identification performance than traditional methods, increasing the prediction accuracy from under 15\% to over 65\% for 15 drivers. When leveraging the full field study dataset, comprising 72 drivers, the accuracy of identification prediction of the approach improved a random guess approach by a factor of 25.

'Spider-like senses' could help autonomous machines see better: Researchers are building animal-inspired sensors into the shells of aircraft, cars


They might actually detect and avoid objects better, says Andres Arrieta, an assistant professor of mechanical engineering at Purdue University, because they would process sensory information faster. Better sensing capabilities would make it possible for drones to navigate in dangerous environments and for cars to prevent accidents caused by human error. Current state-of-the-art sensor technology doesn't process data fast enough -- but nature does. And researchers wouldn't have to create a radioactive spider to give autonomous machines superhero sensing abilities. Instead, Purdue researchers have built sensors inspired by spiders, bats, birds and other animals, whose actual spidey senses are nerve endings linked to special neurons called mechanoreceptors.