Scientists have developed a method using machine learning to better analyze data from a powerful scientific tool: nuclear magnetic resonance (NMR). One way NMR data can be used is to understand proteins and chemical reactions in the human body. NMR is closely related to magnetic resonance imaging (MRI) for medical diagnosis. NMR spectrometers allow scientists to characterize the structure of molecules, such as proteins, but it can take highly skilled human experts a significant amount of time to analyze that data. This new machine learning method can analyze the data much more quickly and just as accurately.

Artificial intelligence (AI) can be trained to recognise whether a tissue image contains a tumour. However, exactly how it makes its decision has remained a mystery until now. A team from the Research Center for Protein Diagnostics (PRODI) at Ruhr-Universität Bochum is developing a new approach that will render an AI's decision transparent and thus trustworthy. The researchers led by Professor Axel Mosig describe the approach in the journal "Medical Image Analysis", published online on 24 August 2022. For the study, bioinformatics scientist Axel Mosig cooperated with Professor Andrea Tannapfel, head of the Institute of Pathology, oncologist Professor Anke Reinacher-Schick from the Ruhr-Universität's St. Josef Hospital, and biophysicist and PRODI founding director Professor Klaus Gerwert.

Nexar uses data gathered from its dash cameras to develop its Driver Behavioral Map. Nexar, an Israeli AI computer vision company, announced the release of its Driver Behavioral Maps, which aim to make autonomous vehicles (AVs) drive more naturally. The maps make use of crowd-sourced driving data from Nexar's dash cameras, which provide information about human driving behavior. This data is aggregated and overlaid on a high-definition base map to assist AVs in learning the local driving culture and important driving habits. "A self-driving car that drives only according to a raw map would be an immediate danger due to its robotic style of driving," Eran Shir, co-founder and CEO of Nexar, said.

Team's algorithm could lead to pivotal discovery of new materials for solar cells. It will be doing so for billions more years. Yet, we have only just begun tapping into that abundant, renewable source of energy at affordable cost. Solar absorbers are a material used to convert this energy into heat or electricity. Maria Chan, a scientist in the U.S. Department of Energy's (DOE) Argonne National Laboratory, has developed a machine learning method for screening many thousands of compounds as solar absorbers.

Indica Labs, an industry leader in quantitative digital pathology and image management solutions, and The Industrial Centre for Artificial Intelligence Research in Digital Diagnostics (iCAIRD), announced today an agreement to collaborate on the development of an AI-based digital pathology solution for the detection of cancer within lymph nodes from colorectal surgery cases. The primary aim of the innovative research project is to develop a tool which in the future may improve the efficiency of pathology teams within the National Health Service Greater Glasgow and Clyde (NHSGGC) reporting colorectal cancer cases and the detection of metastatic cancer in lymph nodes. Funded by a combination of Innovate UK and industrial partners, and based in Scotland, and supported by the West of Scotland Innovation Hub, iCAIRD is one of the largest healthcare AI research portfolios in the UK. A collaboration of 30 partners from across the NHS, industry, academia and technology, the program is currently delivering 35 ground-breaking AI projects across radiology and pathology, having grown from just 10 projects at the outset in 2019. The mission of iCAIRD is to establish a world-class center of excellence for implementation of artificial intelligence in digital diagnostics.

Barcelona, Spain – 28 Aug 2022: A novel artificial intelligence (AI) algorithm uses routine echocardiograms to identify aortic stenosis patients at high risk of death who could benefit from treatment. The late breaking research is presented in a Hot Line session today at ESC Congress 2022.1 Aortic stenosis is the most common primary valve lesion requiring surgery or transcatheter intervention in Europe and North America.2 Prevalence is rapidly increasing due to ageing populations. Guidelines strongly advise early intervention in all symptomatic patients with severe aortic stenosis due to the dismal prognosis. Approximately 50% of untreated patients with aortic stenosis die in the first two years after symptoms appear.3

Dominant Algorithms to Evaluate Artificial Intelligence: From the view of Throughput Model is an informative reference for all professionals and scholars who are working on AI projects to solve a range of business and technical problems. The six AI algorithmic pathways represent. As AI is increasingly employed for applications where decisions require explanations, the Throughput Model offers business professionals the means to look under the hood of AI and comprehend how those decisions are attained by organizations. Finally, The Throughput Model provides the first steps towards building architectures that combine the strengths of the symbolic approaches that can be adapted for machine learning/ deep learning, and to develop better techniques for extracting and generalizing abstract knowledge from large, often noisy data sets. As AI is employed more and more for applications where decisions require explanations, the Throughput Model offers the means to look under the hood of AI and comprehend how those decisions are attained by organizations.

AF Group companies will use CLARA's industry-leading technology platform to identify the health care providers best suited to help injured employees recover successfully and return to work quickly. AF Group comprises seven affiliated insurance brands that provide innovative, specialty insurance solutions. AF Group companies utilize industry-leading best practices, analytics and resources to help manage risk and minimize losses for policyholders -- and always strive to provide injured workers with security, compassionate care, and the opportunity to return to work as soon as possible. "Innovation in data analytics is a key pillar in our strategy to be the best at what we do, maintaining a culture of claims excellence and compassionate care for our customers," said Paul Kearney, Chief Claims Officer at AF Group. "Returning to work after an injury helps employees rebuild their livelihoods and restore their quality of life. AF Group companies help those workers using a multifaceted approach that incorporates data analytics, evidence-based medicine, and smart technology. CLARA Analytics aligns nicely with that strategy -- helping injured employees recover quickly while also minimizing losses for policyholders and improving our claims management processes."

Retrace, a leader in dental artificial intelligence and provider of digital infrastructure for U.S. healthcare, announces the publication, "A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level" in the August 2022 Edition of the Journal of Dentistry. This groundbreaking study for the first time demonstrates how the use of a novel Generative Adversarial Network (GAN), (U.S. Patent Numbers: US 11,217,350 B2; US 11,276,151 B2; US 11,398,013 B2), often referred to as a "Deep Fake", improves the diagnostic accuracy of AI algorithms in identifying periodontal disease. Medical and dental AI imaging algorithms are often trained on limited data sets from a limited number of providers, patients and imaging sources. As a result, when these algorithms are used in a general production environment, the algorithms struggle to achieve the same level of accuracy as the environment they were trained in. "Over the past few years, we have seen a sharp rise in dental and medical imaging AI companies; some who have even received FDA Clearance," said Dr. Ali Sadat, Founder and CEO of Retrace.

"We offer our deepest apologies to the Black community for our insensitivity in signing this project without asking enough questions about equity and the creative process behind it." On August 14, Capitol Records announced that it had signed FN Meka, a digital rapper and TikTok influencer described by the label as "the world's first A.R. artist to sign with a major label." A press release from FN Meka's 2021 publicist described Meka as an "A.I. powered robot rapper." Meka's first single on the label was "Florida Water," which featured Gunna and gaming streamer Clix. As of today, FN Meka is no longer on a major label; Capitol has announced that it has "severed ties" with the rapper, The New York Times' Joe Coscarelli reports.