A delay in the arrival of the rebooted Intellivision video game console. The Intellivision Amico, the modern reboot of the iconic Intellivision video game console, which had been scheduled to hit the market Oct. 10, now is scheduled for release April 15, 2021. Because of production challenges during the pandemic, the company decided to push back the system's launch. Not to do so would have compromised quality assurance, said Intellivision Entertainment CEO Tommy Tallarico during an online event Wednesday. "Ultimately our date is going to be determined by specific quality criteria that the team has defined and not a moment before," he said.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Physically taxing jobs can hinder one's cognitive health, potentially causing a person's brain to age faster and leave them with a poorer memory as they grow older, a new study suggests. In a study published in the Frontiers in Human Neuroscience in July, researchers surveyed nearly 100 cognitively healthy older adults between ages 60 and 80 years old in order to better understand how stress plays a role in how the human brain ages. Their analysis indicated that adults who reported having higher levels of physical stress in their most recent job were also people who had a smaller hippocampal volume and poorer memory performance. The hippocampus is commonly associated with memory.
The COVID 19 situation, has rendered the industry into an unprecedented situation. Businesses across the globe are now resorting to plan out new strategies to keep the operations going, to meet clients' demands. Work-from-Home is the new normal for both the employees and the employers to function in a mitigated manner. Twitter on their tweet had suggested their employees, to function through "Work-from-Home", forever, if they want to. This new trend can be easily surmised as being effective for a while to manage operations, but cannot be ruled out as the necessary solution, for satisfying the customers and clients in the long run.
To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations. In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (PO, PHO) is global, while the second of (LSS, LHOS) is lobe-wise. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States collected between 2002-Present (April 2020).
When people seek emergency care for shortness of breath, a routine electrocardiogram (ECG or EKG) enhanced by artificial intelligence (AI) is better than standard blood tests at determining if the cause is heart failure, according to new research published today in Circulation: Arrhythmia and Electrophysiology, an American Heart Association journal. "Determining why someone has shortness of breath is challenging for emergency department physicians, and this AI-enabled ECG provides a rapid and effective method to screen these patients for left ventricular systolic dysfunction," said Demilade Adedinsewo, M.D., M.P.H., lead author of the study and chief fellow in the division of cardiovascular medicine at Mayo Clinic in Jacksonville, Florida. The left ventricle supplies most of the heart's pumping power, so it is larger than the other chambers and essential for normal function. In left ventricular systolic dysfunction (LVSD), the left ventricle is weakened and must work harder to maintain adequate blood flow to the body. In a typical year, about 1.2 million people go to emergency departments because they are short of breath.
Researchers from the US National Institute of Standards and Technology found that face masks are causing facial recognition algorithms to fail as much as 50% of the time. In a report, the US National Institute of Standards and Technology found that face masks were thwarting even the most advanced facial recognition algorithms. Error rates varied from 5% to 50%, depending on an algorithm's capabilities. The results are troubling for the facial recognition industry which has been scrambling to develop algorithms that can identify people through their eyes and nose alone as people turn to face masks amid the coronavirus pandemic. The masks have caused trouble for facial recognition software prompting tech companies to adapt.
The dream of creating a machine that emulates human behavior has been an obsession throughout human history. Artificial Intelligence (AI) has been in our minds for many years, since Adam's creation: "God creates him from a moldable material, programs him, and gives him the first instructions (Sánchez-Martín et al. 2007)." Even in Greek mythology with Ovid's account of Pygmalion sculpting a figure of a beautiful woman who is given life for Pygmalion to love her. In Hebrew mythology, the Golem was created with clay and animated to save the inhabitants of a Jewish city. In Norse mythology, the giant Mökkurkálfi or Mistcalf was created from clay to support the troll Hrungnir in his fight against Thor.
Brain-computer interfaces are seeing massive AI breakthroughs including neural bridges being built for learning, treatment of specific diseases and overcoming the electrical-to-biochemical language barrier. These trends are what will optimise the information bandwidth that comes with neuroscience technology. "A monkey has been able to control a computer with its brain." That almost unimaginable yet remarkably accurate observation was made by Elon Musk, author and CEO of Tesla. In his presentation, Musk switched between varying forms of "what is" to "what could be", before announcing the details surrounding Tesla Energy.
PHILADELPHIA - To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study published Tuesday in the journal Scientific Reports, led by senior author Spyridon Bakas, PhD, an instructor of Radiology and Pathology & Laboratory Medicine in the Perelman School of Medicine at the University of Pennsylvania. Federated learning -- an approach first implemented by Google for keyboards' autocorrect functionality -- trains an algorithm across multiple decentralized devices or servers holding local data samples, without exchanging them. While the approach could potentially be used to answer many different medical questions, Penn Medicine researchers have shown that federated learning is successful specifically in the context of brain imaging, by being able to analyze magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.
The creation of the Global Partnership on Artificial Intelligence (GPAI) reflects the growing interest of states in AI technologies. The initiative, which brings together 14 countries and the European Union, will help participants establish practical cooperation and formulate common approaches to the development and implementation of AI. At the same time, it is a symptom of the growing technological rivalry in the world, primarily between the United States and China. Russia's ability to interact with the GPAI may be limited for political reasons, but, from a practical point of view, cooperation would help the country implement its national AI strategy. The Global Partnership on Artificial Intelligence (GPAI) was officially launched on June 15, 2020, at the initiative of the G7 countries alongside Australia, India, Mexico, New Zealand, South Korea, Singapore, Slovenia and the European Union. According to the Joint Statement from the Founding Members, the GPAI is an "international and multistakeholder initiative to guide the responsible development and use of AI, grounded in human rights, inclusion, diversity, innovation, and economic growth."