Dr. Sander kindly agreed to give us this interview at the Idorsia headquarters in Basel, Switzerland. Asking the questions from CDD are Neil Chapman and Mariana Vaschetto. By education I am organic chemist. During my seventh year at school we started to have chemistry classes and soon I had made up my mind to study chemistry. Four years later while still at school I had an opportunity to access the local University's Tectronix graphics computers.
When race car drivers take tight turns at high speeds, they rely on their experience and gut feeling to hit the gas pedal without spinning out. But how does an autonomous race car make the same decision? Currently, many autonomous cars rely on expensive external sensors to calculate a vehicle's velocity and chance of sideslipping on the racetrack. In a different approach, one research team in Switzerland has recently developed a novel a machine learning algorithm that harnesses measurements from more simple sensors. They describe their design in a study published August 14 in IEEE Robotics and Automation Letters.
Physicists built the Large Hadron Collider to study the inner workings of the universe. Inside a 27-kilometer underground ring straddling the French-Swiss border, the machine smashes protons together at nearly the speed of light to produce--fleetingly--the smallest constituent building blocks of nature. Sifting through snapshots of these collisions, LHC researchers look for new particles and scrutinize known ones, including their most famous find, in 2012: the Higgs boson, whose behavior explains why other fundamental particles like electrons and quarks have mass. Less well known is the intricate software engine that powers such discoveries. With particle collisions occurring at approximately a billion times per second, the facility generates about 40 terabytes of data per second, according to LHC physicist Maurizio Pierini.
Last month, Elon Musk's Neuralink demonstrated that it is possible to monitor brain activity from our phones. There were speculations around Neuralink of what potential it has for the future generations. Decoding brain signals has great implications in medicine. A disabled person can be assisted, can understand what a speechless person is feeling and more. So, can we know what someone is thinking?
Court documents released in August revealed that Swiss tax officials are investigating art dealer and freeport magnate Yves Bouvier for allegedly concealing CHF 330 million in profits. The Swiss authorities believe that Bouvier used a fictitious residence in Singapore to evade taxes in his home country, and confiscated one of Bouvier's properties, reportedly worth CHF 4.5 million, as a pledge while they continue investigating his finances. The investigation, however, was nearly derailed in its early stages due to a single vulnerable tax official. An escort girl known only as Sarah has testified that in September 2017, Yves Bouvier sent her to a conference to seduce a key official with Switzerland's Federal Tax Administration. Sarah's honeypot adventure took place mere months after Swiss tax officials had begun looking into Bouvier's finances.
Animated films such as Toy Story, The Lion King, and Spirited Away hold cherished childhood memories for people all around the world. While we fondly remember the adorable characters and catchy theme songs, we're less likely to reflect on the complex computer animation processes that enabled these classics. Such processes, especially with 3D animation, are incredibly time-consuming, with intermediate steps involving modelling, defining skeletal joints and deformation parameters, posing, keyframe setting, and much more. Monster Mash, a novel AI-powered 3D modelling and animation tool, aims to make these arduous 3D animation processes a whole lot easier. The proposed framework comes from researchers at Czech Technical University in Prague, Google Research, University of Washington and ETH Zurich, and enables users to create animated 3D models from a single view. Building on the 1999 work Teddy: A Sketching Interface for 3D Freeform Design by Igarashi et al, Monster Mash is described as the first sketch-based tool that enables creating and animating a smooth, consistent 3D model from a single viewpoint within seconds.
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.
Over the past few decades, research teams worldwide have developed machine learning and deep learning techniques that can achieve human-comparable performance on a variety of tasks. Some of these models were also trained to play renowned board or videogames, such as the Ancient Chinese game Go or Atari arcade games, in order to further assess their capabilities and performance. Researchers at University of Zurich and SONY AI Zurich have recently tested the performance of a deep reinforcement learning-based approach that was trained to play Gran Turismo Sport, the renowned car racing video game developed by Polyphony Digital and published by Sony Interactive Entertainment. Their findings, presented in a paper pre-published on arXiv, further highlight the potential of deep learning techniques for controlling cars in simulated environments. "Autonomous driving at high speed is a challenging task that requires generating fast and precise actions even when the vehicle is approaching its physical limits," Yunlong Song, one of the researchers who carried out the study, told TechXplore.
The giant planets in our solar system are made mainly of hydrogen, mostly in a liquid state. Near the planets- surface, hydrogen exists in an insulating, molecular form – H 2 – but closer to the center, it takes on a metallic form where individual atoms can move around freely. Professor Michele Ceriotti, who heads the Laboratory of Computational Science and Modelling (COSMO) within EPFL's School of Engineering, along with colleagues from the University of Cambridge and IBM Zurich, have used computer simulations to understand the nature of this elusive transition. That's one reason why scientists study it so much. What makes this phenomenon on giant planets fairly unique – and interesting – is that the transition is between two forms of a liquid state, and not from a liquid to a gaseous or solid state,- says Ceriotti.
The project launch in the fourth quarter of 2019 and today's productive go-live of the Insider Trading module has seen SER achieve another milestone in its transformation into a more effective, risk-based and more efficient trading surveillance outfit. With PwC Switzerland, SER was able to draw on an experienced team of experts in the development of "Prometheus". "Time to market was important from the outset. Not only did we want to develop an innovative and efficient application, we wanted to do it as soon as possible. A key success factor for the short implementation time was the symbiosis between our internal specialists and the experts from PwC Switzerland," says Christian Müller, explaining the background of their rapid success.