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A neuromorphic computing architecture that can run some deep neural networks more efficiently

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As artificial intelligence and deep learning techniques become increasingly advanced, engineers will need to create hardware that can run their computations both reliably and efficiently. Neuromorphic computing hardware, which is inspired by the structure and biology of the human brain, could be particularly promising for supporting the operation of sophisticated deep neural networks (DNNs). Researchers at Graz University of Technology and Intel have recently demonstrated the huge potential of neuromorphic computing hardware for running DNNs in an experimental setting. Their paper, published in Nature Machine Intelligence and funded by the Human Brain Project (HBP), shows that neuromorphic computing hardware could run large DNNs 4 to 16 times more efficiently than conventional (i.e., non-brain inspired) computing hardware. "We have shown that a large class of DNNs, those that process temporally extended inputs such as for example sentences, can be implemented substantially more energy-efficiently if one solves the same problems on neuromorphic hardware with brain-inspired neurons and neural network architectures," Wolfgang Maass, one of the researchers who carried out the study, told TechXplore.


Vienna Gödel Lecture 2022: Toby Walsh

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TU Wien Informatics will be welcoming Toby Walsh, AI expert and "rock star" of Australia's digital revolution, for the Gödel Lecture 2022. Artificial intelligence is an essential part of our lives – for better or worse. It can be used to influence what we buy, who gets shortlisted for a job, and even how we vote. Without AI, medical technology wouldn't have come so far, we'd still be getting lost on backroads in our GPS-free cars, and smartphones wouldn't be so, well, smart. But as we continue to build more intelligent and autonomous machines, what impact will this have on humanity and the planet?


5 Min AI Newsletter #3

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Oral-B's toothbrush uses AI to grade your teeth brushing habits Your teeth might not be the first thing you think about when it comes to a beauty routine. But you, like most of us, are probably in constant pursuit of a healthy and pearly white smile. Enter: the Oral-B iO Series 9 toothbrush, complete with AI technology. According To The Latest AI Research From Graz University, Intel's Neuromorphic Chips Are UpTo 16 Times More Energy Efficient For Deep Learning Due to their high power consumption, new AI methods that utilize DNNs pose a significant barrier to broader deployment, particularly in edge devices. The use of spike-based neuromorphic electronics is one potential solution to this issue.


GitHub - ml-jku/hopular: Hopular: Modern Hopfield Networks for Tabular Data

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While Deep Learning excels in structured data as encountered in vision and natural language processing, it failed to meet its expectations on tabular data. For tabular data, Support Vector Machines (SVMs), Random Forests, and Gradient Boosting are the best performing techniques with Gradient Boosting in the lead. Recently, we saw a surge of Deep Learning methods that were tailored to tabular data but still underperformed compared to Gradient Boosting on small-sized datasets. We suggest "Hopular", a novel Deep Learning architecture for medium- and small-sized datasets, where each layer is equipped with continuous modern Hopfield networks. The modern Hopfield networks use stored data to identify feature-feature, feature-target, and sample-sample dependencies.


About Us

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Our aim is to publish prioritized research from all over the world in terms of global health in the form of oral and summary papers without wasting time. All oral presentation sessions and conferences of the relevant month will be broadcast live on the 27th of each month on MedicReS scientific TV channel broadcasting 24 hours a day. In parallel with all the developments in technology, we delivered MedicReS 2022 Congress to all our members via MedicReS TV on our www.medicres.club Papers coming to our congress pass through the referee system in MedicReS advisory boards, and oral abstracts are published in English in MedicReS GMR World Congress Abstracts and Congress Proceedings Book. Your oral presentations are also given to you as MP4.


A pediatric wrist trauma X-ray dataset (GRAZPEDWRI-DX) for machine learning - Scientific Data

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Digital radiography is widely available and the standard modality in trauma imaging, often enabling to diagnose pediatric wrist fractures. However, image interpretation requires time-consuming specialized training. Due to astonishing progress in computer vision algorithms, automated fracture detection has become a topic of research interest. This paper presents the GRAZPEDWRI-DX dataset containing annotated pediatric trauma wrist radiographs of 6,091 patients, treated at the Department for Pediatric Surgery of the University Hospital Graz between 2008 and 2018. A total number of 10,643 studies (20,327 images) are made available, typically covering posteroanterior and lateral projections. The dataset is annotated with 74,459 image tags and features 67,771 labeled objects. We de-identified all radiographs and converted the DICOM pixel data to 16-Bit grayscale PNG images. The filenames and the accompanying text files provide basic patient information (age, sex). Several pediatric radiologists annotated dataset images by placing lines, bounding boxes, or polygons to mark pathologies like fractures or periosteal reactions. They also tagged general image characteristics. This dataset is publicly available to encourage computer vision research.


Neuromorphic chips more energy efficient for deep learning

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Neuromorphic chips have been endorsed in research showing that they are much more energy efficient at operating large deep learning networks than non-neuromorphic hardware. This may become important as AI adoption increases. The study was carried out by the Institute of Theoretical Computer Science at the Graz University of Technology (TU Graz) in Austria using Intel's Loihi 2 silicon, a second-generation experimental neuromorphic chip announced by Intel Labs last year that has about a million artificial neurons. Their research paper, "A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware," published in Nature Machine Intelligence, claims that the Intel chips are up to 16 times more energy efficient in deep learning tasks than performing the same task on non-neuromorphic hardware. The hardware tested consisted of 32 Loihi chips.


AAIC22 - Applied AI Conference 2022

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Join the Applied AI Conference (online in 2022) where we focus on the real-world impact of AI in this year's topics Sales & Marketing. All of our participants and speakers may arrange virtual 1:1 meetings with each other. We connect AI solution developers with potential users, and we make sure you walk away with a bag of leads. Ever wondered how you could make AI work for your business? AAIC is the right place for you.


Deploying This 'Smart Skin' Will Definitely Make Robots More Human-Like!

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The'smart skin' is like a fabric that contains sensors that recognize crucial indications of a human as well as airborne chemicals. Envisioned for a range of applications from baby monitoring to warfare, smart skin applications are expected to grow exponentially in the future. After nearly six years of research at the Technical University of Graz, Italian-born Anna Maria Coclite has developed the'smart skin' for the next generation of artificial intelligence materials. It senses pressure, humidity, and temperature all the while and produces electronic signs. More delicate robots or more AI prostheses will be consequently possible.


This AI tool predicts whether COVID patients will live or die

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A tool has been developed to help healthcare professionals identify hospitalised patients most at risk of dying from COVID-19 using artificial intelligence (AI). The algorithm could help doctors to direct critical care resources to those in most immediate need, which the developers of the AI tool say could be especially valuable to resource-limited countries. And with no end in sight for the coronavirus pandemic, with new variants leading to fresh waves of sickness and hospitalisation, the scientists behind the tool say there is a need for generalised tools like this which can be easily rolled out. To develop the tool, scientists used biochemical data from routine blood samples taken from nearly 30,000 patients hospitalised in over 150 hospitals in Spain, the US, Honduras, Bolivia and Argentina between March 2020 and February 2022. Taking blood from so many patients meant the team were able to capture data from people with different immune statuses – vaccinated, unvaccinated and those with natural immunity – and from people infected with every variant of COVID-19.