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Image 15 July 2019


AI To Help Diabetes Patients in India

Google and several Indian doctors are working together to use AI to detect diabetic retinopathy, a kind of nerve damage in eyes that, if not detected early, can lead to blindness. The AI model can detect diabetic retinopathy by analyzing the scans of the retinas - the region at the back of the eye - using special cameras. This image is then analysed by Google's machine learning algorithm, which then grades those scans on a five-point scale ranging between no diabetic retinopathy or DR, which can be managed using a combination of diet and medication, to the most severe case, which may need a surgery. Google has already piloted this program in two South-East Asian countries -- Thailand and India. In India, Google partnered with Aravind Eye Hospital in Madurai and Sankara Eye Hospital in Chennai. The nurses capture the image of the patients' retinas. These images are then uploaded on to the cloud where Google's ML algorithm works in combination with the specialists to detect and diagnose the disease, which can then be treated based on its severity. Read More



AI-Powered Mind-Controlled Robotic Arm that Works Without A Brain Implant

Scientists from Carnegie Mellon and the University of Minnesota have accomplished a groundbreaking new technological feat by developing the first-ever noninvasive mind-controlled robotic arm. While similar technology has been available for some time, it involved inserting a brain implant in patients, making it a risky, invasive and quite expensive procedure. To build the novel technology, the researchers have used specialised sensing and machine learning techniques to build up a reliable connection between the brain and a robotic arm. The team’s noninvasive brain-computer interface successfully decoded neural signals, allowing a person, for the first time, to control a robotic arm in real time, instructing it to continuously and smoothly follow the movements of a cursor on a screen. The success of these preliminary trials has given the scientists hope that they will eventually be able to bring this technology to the individuals who need it. Read More



AI Design Can Reduce Heat Pump Energy Consumption

Researchers at the Laboratory for Applied Mechanical Design at Lausanne’s EPFL (École Polytechnique Fédérale de Lausanne) have developed a method that is said to make it easier and faster to add turbo-compressors to heat pumps. Using a machine-learning process called symbolic regression, the researchers came up with simple equations for quickly calculating the optimal dimensions of a turbo-compressor for a given heat pump. Until now, engineers have been using design charts to size their turbo-compressors – but these charts become increasingly inaccurate the smaller the equipment. Also, the researchers argue that the charts have not kept up to date with the latest technology. The EPFL team fed the results of 500,000 simulations into machine-learning algorithms and generated equations that replicate the charts but with several claimed advantages: they are reliable even at small turbo-compressor sizes; they are just as detailed as more complicated simulations; and they are 1,500 times faster. It paves the way to easier implementation and more widespread use of micro turbochargers in heat pumps. Read More



AI Can Spot Galaxy Clusters Millions of Light-Years Away

Researchers at Lancaster University have created the “Deep-CEE” (Deep Learning for Galaxy Cluster Extraction and Evaluation) technique that used an AI model trained to look at colour images and identify galaxy clusters. The AI looks at colour images and picks out potential galaxy clusters using neural networks, which mimic the way that a human brain would learn to recognize objects. It was trained using images of known galaxy clusters, until it was able to identify new clusters in images even when other astronomical objects were present as well. Deep-CEE has already been applied to the multi-spectral imaging and spectroscopic Sloan Digital Sky Survey at Apache Point Observatory in New Mexico. By automating the discovery process, scientists can quickly scan sets of images and return precise predictions with “minimal” human interaction. Moving forward, the researchers hope to run the model on additional equipment, including the Large Synoptic Survey Telescope—a wide-field survey reflecting telescope, currently under construction in Chile and due to come online in 2021. Read More



AI Created from a Sheet of Glass

Scientists from the University of Wisconsin–Madison have found a way to create an AI glass that can recognize images without any need for sensors, circuits, or even a power source. The researchers made a sheet of “smart” glass that could identify handwritten digits. To accomplish that feat, they started by placing different sizes and shapes of air bubbles at specific spots within the glass. Then they added bits of strategically placed light-absorbing materials, including graphene. When the team then wrote down a number, the light reflecting off the digit would enter one side of the glass. The bubbles and impurities would scatter the light waves in certain ways depending on the number until they reached one of the 10 designated spots — each corresponding to a different digit — on the opposite side of the glass. The glass could essentially tell the researcher what number it saw — at the speed of light and without the need for any traditional computing power source. Read More


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