IMAGE: Like any recipe, an ideal memristive neuromorphic computing system requires a special blend of CMOS circuits and memristive devices, as well as spatial resources and temporal dynamics that must be... view more WASHINGTON, March 24, 2020 -- During the 1990s, Carver Mead and colleagues combined basic research in neuroscience with elegant analog circuit design in electronic engineering. This pioneering work on neuromorphic electronic circuits inspired researchers in Germany and Switzerland to explore the possibility of reproducing the physics of real neural circuits by using the physics of silicon. The field of "brain-mimicking" neuromorphic electronics shows great potential not only for basic research but also for commercial exploitation of always-on edge computing and "internet of things" applications. In Applied Physics Letters, from AIP Publishing, Elisabetta Chicca, from Bielefeld University, and Giacomo Indiveri, from the University of Zurich and ETH Zurich, present their work to understand how neural processing systems in biology carry out computation, as well as a recipe to reproduce these computing principles in mixed signal analog/digital electronics and novel materials. One of the most distinctive computational features of neural networks is learning, so Chicca and Indiveri are particularly interested in reproducing the adaptive and plastic properties of real synapses.
A new study shows that an artificial intelligence (AI) method that fuses medically relevant information enables critical circulatory failure to be predicted in the intensive care unit (ICU) several hours before it occurs. Developed at the Swiss Federal Institute of Technology (ETH; Zurich, Switzerland) and Bern University Hospital (Inselspital; Switzerland), the early-warning platform integrates measurements from multiple systems using a high-resolution database that holds 240 patient-years of data. For the study, the researchers used anonymized data from 36,000 admissions to ICUs, and were able to show that just 20 of these variables, including blood pressure, pulse, various blood values, the patient's age, and medications administered were sufficient to make accurate predictions. In a trial run of the algorithms developed, they were able to predict 90% of circulatory-failure events, with 82% of them identified more than two hours in advance. On average, the system raised 0.05 alarms per patient and hour.
Drones can do many things, but avoiding obstacles is not their strongest suit yet – especially when they move quickly. Although many flying robots are equipped with cameras that can detect obstacles, it typically takes from 20 to 40 milliseconds for the drone to process the image and react. It may seem quick, but it is not enough to avoid a bird or another drone, or even a static obstacle when the drone itself is flying at high speed. This can be a problem when drones are used in unpredictable environments, or when there are many of them flying in the same area. Reaction of a few milliseconds In order to solve this problem, researchers at the University of Zurich have equipped a quadcopter (a drone with four propellers) with special cameras and algorithms that reduced its reaction time down to a few milliseconds – enough to avoid a ball thrown at it from a short distance.
It was only a few months ago that I wrote about Scientists who developed artificial neurons that mimic our brain cells. Scientists at the University of Bath, Universities of Bristol, Zurich & Auckland collaborated on this effort where the behavior of our brain cells was replicated on tiny silicon chips. As we enter the age of supercomputers, they are still not powerful enough to match the brainpower of biological neurons that power the organ. The neurons communicate via tiny gaps known as synapses. These neurons have a dual mechanism of storing and processing information.
The researchers used multiple layers of Artificial Intelligence including a computer vision algorithm to detect changes in cell appearance and organisation. The algorithm was fed information from robotic microscopy, in collaboration with researchers from the University of Zurich, to image millions of colon cancer cells. Following the perturbation (or decrease in the expression) of every gene in each individual colon cancer cell the study found that smell-sensing genes are strongly associated with how cells spread and align with each other. Reducing the expression of smell-sensing genes can inhibit cells from spreading, potentially by restraining the ability of cells to move. The same behaviour is also observed in the perturbation of key cancer genes.
LONDON & ZURICH--(BUSINESS WIRE)--Finantix, the leading global provider of trusted technology to the wealth management, insurance and banking industries, today announced the execution of a binding agreement for the acquisition of InCube Group AG. The Zürich-based firm boasts an interdisciplinary team of Artificial Intelligence (AI) specialists, quant and software engineers and finance experts that provide data-driven, AI-enabled products and solutions to wealth management and insurance companies. Christine Ciriani, Chief Commercial Officer for Finantix, said: "We are excited to welcome InCube to the Finantix family, enhancing our AI-based insights and personalisation capabilities. This acquisition is a great fit as we share many values and have a common vision. First and foremost, a relentless focus on the need of our customers to leverage the growing amount of data available and the drive to transform this into actionable business insights. We believe that, by combining InCube's AI-based products, deep data science experience and domain expertise with Finantix's comprehensive offering, our customer-base will benefit from an enriched platform ready to accelerate their innovation capabilities in a data-driven era."
Current vision algorithms are largely designed for use in clear conditions, deep learning using convoluted neural networks is now being harnessed to improve visual performance in adverse weather. Capturing high-quality photos is easier than ever, as filters and image-adjustment tools can enhance images. Yet cameras still struggle to provide a clear image in bad weather, especially in extreme conditions such as heavy rain, fog, or poor lighting at night. Objects in a scene can become hard to see or even invisible, especially when they are far from the lens, and colors are often dulled. "In rain and snow, you also have motion blur because they are moving," says Dengxin Dai, a computer vision lecturer at ETH Zurich in Switzerland who coordinated a workshop on all-weather vision at the Conference for Computer Vision and Pattern Recognition (CVPR 2019) in Long Beach, CA. "So the geometry of an object might also get distorted."
Many application domains increasingly require AD, when anomalies carry critical and actionable information. We shall address the problem of detecting and predicting general anomalies in high-dimension KPI performance metrics, i.e., high dimension and dynamic range multivariate non-stationary time series collected from large Cloud / IT environments. Using Keras / TF etc., we will build an ML-based AD framework for transfer, attention and meta-learning that must remain robust also with reduced/missing and noisy training data. Besides feature engineering -- e.g., selection, reduction, compression techniques -- explainability will also be necessary for the model prototype. The research is to be performed at IBM Research – Zurich Lab, Switzerland.
Last June at the International Forum on Women's Brain and Mental Health organized by the Women's Brain Project (WBP) in Zurich, Switzerland, there was a very special guest: Sophia the Advanced Humanoid Robot from Hanson Robotics. Sophia the Advanced Humanoid Robot participated in the last panel of the Forum "Leveraging AI to address gender bias", moderated by Nicoletta Iacobacci. Here, we are thrilled to share a selection of the "hot off the press" video footage of this event. Mara Hank Moret is a philanthropist and WBP supporter. In her conversation with Sophia, topics from gender identification to the importance of empathy, women in AI, and how to integrate diversity in data to avoid systemic biases are covered.
Forward-looking insurers are using AI to innovate insurance processes like claims to keep up with customer demand for a 24/7 digital experience, while boosting operational efficiency. And conversational assistants, such as chatbots, are one of the most prevalent applications of AI used to accomplish this. But for customers to embrace the tech, chatbots need to drive a good conversational experience that mimics human agents, and bots must have access to relevant customer information to successfully address their requests. To that end, insurer Zurich UK worked with white-label chatbot provider Spixii to expand its initially limited digital capabilities to provide customers with an immediate way of declaring claims. The insurer knew it had to meet customer demand for an "always-on" digital experience around claims, and while it likely identified chatbots as a good solution from both a time-to-market and budgeting perspective, it needed to ensure the tech would provide a cohesive experience across online and offline channels.