A special, peer-reviewed edition of OMICS: A Journal of Integrative Biology, has highlighted the importance of key digital technologies, including Artificial Intelligence (AI), machine learning, and blockchain for innovation in healthcare in response to the challenges posed by COVID-19. Vural Özdemir, MD, PhD, Editor-in-Chief of OMICS, said: "COVID-19 is undoubtedly among the ecological determinants of planetary health. Digital health is a veritable opportunity for integrative biology and systems medicine to broaden its scope from human biology to ecological determinants of health. Articles in the special issue include an interview on'Responsible Innovation and Future Science in Australia' by Justine Lacey, Commonwealth Scientific and Industrial Research Organisation (CSIRO), and Erik Fisher, Arizona State University, Tempe, 'Blockchain for Digital Health: Prospects and Challenges' and'Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine.' In'Blockchain for Digital Health: Prospects and Challenges' the article explores the challenges that can be faced with the use of blockchain technology.
"Having helped incorporate Paradox less than four years ago, we were proud to represent Paradox in this important financing, which signifies its status as one of Arizona's premier emerging growth companies," said David Lewis, the DLA Piper partner who led the firm's deal team. "Our extensive experience advising high-growth technology companies, like Paradox, as they navigate the evolving legal developments in the AI space, as well as our ability to develop and implement creative solutions to overcome potential roadblocks to the benefit of both parties, were important assets in our handling of this transaction."
"I think a lot of lessons have been learned, and I think the transition would be a little smoother, but that doesn't mean that it doesn't come without significant challenges," she said. "But there could be situations in different communities where the risk is very low, and we could have safety precautions in place and different guidelines to make sure we're taking every precaution possible to protect the well-being of everyone."
Every day, millions of Americans could be flushing critical coronavirus data down the toilet. With the nation growing ever more weary of sweeping stay-at-home orders and a worsening economy, some scientists say our poop could be the key to determining when a community might consider easing health restrictions. From Stanford to the University of Arizona, from Australia to Paris, teams of researchers have been ramping up wastewater analyses to track the spread of SARS-CoV-2, the virus that causes COVID-19. Understanding the true scale of COVID-19 has been a major stumbling block across the country, as officials struggle with testing shortages, false negatives, and people who are infected but have no symptoms. Sewage data could potentially help fill these gaps by capturing critical information in the aggregate.
In a paper published on the preprint server Arxiv.org this week, researchers at Uber's Advanced Technologies Group (ATG) propose an AI technique to improve autonomous vehicles' traffic movement predictions. It's directly applicable to the driverless technologies that Uber itself is developing, which must be able to detect, track, and anticipate surrounding cars' trajectories in order to safely navigate public roads. It's well-understood that without the ability to predict the decisions other drivers on the road might make, vehicles can't be fully autonomous. In a tragic case in point, an Uber self-driving prototype hit and killed a pedestrian in Tempe, Arizona two years ago, partly because the vehicle failed to detect and avoid the victim. ATG's research, then -- which is novel in that it employs a generative adversarial network (GAN) to make car trajectory predictions as opposed to less complex architectures -- promises to advance the state of the art by boosting the precision of predictions by an order of magnitude.
San Diego-based TuSimple, which operates a separate unit in China, has 40 18-wheelers operating out of its depot in Tucson, Arizona, and is "essentially running 24/7" carrying loads between Phoenix and El Paso, Texas, chief product officer Chuck Price tells Forbes. It's a tiny freight operation compared to the massive fleets of national haulers like J.B. Hunt, Swift, Werner and Amazon AMZN, each with thousands of trucks and drivers, but no company has more self-driving semis than TuSimple, based on U.S. Transportation Department registry data.
Researchers from Microsoft, along with a team from Arizona State University, have published a work that has outperformed the current state-of-the-art models that detect fake news. Though the prevalence and promotion of misinformation have been since time immemorial, today, thanks to the convenience for access provided by the internet, fake news is rampant and has affected healthy conversations. Given the rapidly evolving nature of news events and the limited amount of annotated data, state-of-the-art systems on fake news detection face challenges due to the lack of large numbers of annotated training instances that are hard to come by for early detection. In this work, the authors exploited multiple weak signals from different user engagements. They call this approach multi-source weak social supervision or MWSS.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Get all the latest news on coronavirus and more delivered daily to your inbox. Robots can't feel love, but they also can't get sick. A pizza place in Arizona is testing a new form of social distancing amid the coronavirus outbreak: Instead of sending people out on deliveries, the restaurant has been using robots.
University of Arizona researchers are collaborating on an autonomous technology project that could prove autonomous vehicles can improve traffic flow and decrease fuel consumption. The project aims to demonstrate for the first time in real traffic that using intelligent control of a small number of connected and automated vehicles can improve the energy efficiency of all the vehicles by reducing traffic congestion, said Electrical and Computer Engineering (ECE) Professor Jonathan Sprinkle. "More and more passenger vehicles come with features that automate some driving tasks," Sprinkle said. "New advancements in machine learning are showing how small changes to those features can work to address societal-scale challenges, such as the amount of fuel spent while sitting in stop-and-go traffic during a daily commute." The project is being funded through a $3.5 million U.S. Department of Energy cooperative research project.
Right now, a minivan with no one behind the steering wheel is driving through a suburb of Phoenix, Arizona. And while that may seem alarming, the company that built the "brain" powering the car's autonomy wants to assure you that it's totally safe. Waymo, the self-driving unit of Alphabet, is the only company in the world to have fully driverless vehicles on public roads today. That was made possible by a sophisticated set of neural networks powered by machine learning about which very is little is known -- until now. For the first time, Waymo is lifting the curtain on what is arguably the most important (and most difficult-to-understand) piece of its technology stack. The company, which is ahead in the self-driving car race by most metrics, confidently asserts that its cars have the most advanced brains on the road today. Anyone can buy a bunch of cameras and LIDAR sensors, slap them on a car, and call it autonomous. But training a self-driving car to behave like a human driver, or, more importantly, to drive better than a human, is on the bleeding edge of artificial intelligence research.