Our smartwatches have gotten pretty good at being able to detect heart problems, or at least notify the wearer that something could be wrong. This is great because these are problems that might not have been picked up until it is too late, and we have heard multiple stories of how people have had their lives saved due to early detection. Now it looks like thanks to work done by University of Utah Health and VA Salt Lake City Health Care System scientists, they have developed an AI-powered wearable sensor that will also be able to detect heart failure in advance, which could also help prevent patients from having to visit a hospital. According to one of the study's authors, Josef Stehlik, "Being able to readily detect changes in the heart sufficiently early will allow physicians to initiate prompt interventions that could prevent rehospitalization and stave off worsening heart failure." How this works is that the sensor will transmit the data from it to a smartphone via Bluetooth.
The science of applied artificial intelligence doesn't get the same kinds of headlines as the pure research efforts of Google or Facebook or others. Mostly that's because what gets built by companies is obfuscated by those same companies, either for proprietary reasons or because the companies actually have nothing much to speak of. Last week, Babak Hodjat, who runs the machine learning operations of software giant Cognizant Technology Solutions, had something to show, so ZDNet traveled to the loft office near San Francisco's Embarcadero where Hodjat and a team of 18 staffers develop algorithms. The ostensible event was the publication, on the arXiv pre-print server, of a paper showing how Hodjat's style of machine learning could compete with the kind made famous by DeepMind's AlphaZero. Before digging into the paper, ZDNet accepted a challenge against the machine, a game of Flappy Bird.
In the time it takes you to read this article, one person in the US will die from an infection that antibiotics can no longer treat effectively. And over the course of this year, 700,000 people around the world will die from drug-resistant infections. That annual death toll could rise to 10 million by 2050, a major UN report recently warned, if we don't make a radical change. For the first time, AI researchers have figured out how to identify brand-new types of antibiotics by training a neural network to predict which molecules will have bacteria-killing properties. They've just published their findings in the journal Cell.
Imagine being thrown into a morning panic by the sound of a blaring alarm, screaming at you to take immediate shelter. Your Smart TV displays the words "AERIAL DRONE RAID" in all red, and as you attempt to rationalize what is going on, you inch towards the window in sheer disbelief as you discover a decimated cityscape. Rogue armies of drone wasps run amok in search of deviants to poison and kill, unmanned tanks obliterate anything moving on the streets and sophisticated digital twin satellites successfully cripple our electric power grid system with advanced EMT attacks. Cyber criminals have already taken advantage of the situation, broadcasting "deep fake" news of a deadly virus to cause panic and hysteria among the masses. Biohackers take it a step further, threatening to unleash an AI-manufactured strain of the flu unless the government provides them with a sizable paycheck.
Scientists convened on an unfinished underground power plant in Elma, Washington to test a group of autonomous military robots in a simulated disaster scenario. The scientists weren't taking part in an experiment but a competition sponsored by the Defense Advanced Research Projects Agency (DARPA), as part of its efforts to develop a range of autonomous robots to fill a variety of military roles. The winning team came from NASA's Jet Propulsion Laboratory, a 60 person crew that oversaw a group of 12 robots they'd programmed through an initiative called Collaborative SubTerranean Autonomous Robots (CoSTAR). 'The goal is to develop software for our robots that lets them decide how to proceed as they face new surprises,' JPL's Ali Agha said. 'These robots are highly autonomous and for the most part make decisions without human intervention.'
Trump calls out politicization of outbreak; reaction and analysis on'The Five.' A second coronavirus case of unknown origin was confirmed in the state of California on Friday, after a Santa Clara County resident reportedly tested positive for the disease. Meanwhile, state officials in Oregon confirmed the first "community spread" case of the virus. The Centers for Disease Control and Prevention (CDC) said that officials are "aware of a second possible instance of community spread of COVID-19 in California," and that the patient has tested positive for the virus and is considered a presumptive positive case, The Associated Press reported. Oregon Health Authority (OHA) officials said Friday that the state's case was "presumptive," as it hadn't yet been confirmed by the CDC, Fox 12 Oregon reported.
San Diego's Lytx, the maker of DriveCam video monitoring technology for commercial truck fleets, has expanded its machine vision and artificial intelligence capabilities to detect when drivers are looking at cellphones on the road. The company's latest update to its in-cab camera technology recognizes when a driver is distracted by a mobile device or other behaviors. That triggers the camera to issue a warning and start recording video, which can be shared with fleet managers through an online portal. Others video telematics companies also have products that can detect cellphone use in the cab of commercial vehicles. But Lytx says its artificial intelligence technology has been developed using millions of miles of video data from its library collected over many years.
Artificial intelligence possesses the capacity to achieve incredible results, but cannot always work alone. Researchers identified two key components in successful human-machine collaboration that may enhance how the U.S. Army will fight in the future. To achieve dominance in what is known as multi-domain operations, warfighters will need a layered intelligence, surveillance and reconnaissance, or ISR, network that maintains a functional relationship between autonomous sensors, human intelligence and friendly special operations forces. Multi-domain operations, known as MDO, is a joint warfighting concept that foresees conflict occurring in multiple domains: land, air, sea, cyber and space. The concept has many nuances, but basically describes how the Army, as part of the joint force, will solve the problem of layered standoff in all domains.
More than 60 years after the discipline's birth,2 artificial intelligence (AI) has emerged as a preeminent issue in business, public affairs, science, health, and education. Algorithms are being developed to help pilot cars, guide weapons, perform tedious or dangerous work, engage in conversations, recommend products, improve collaboration, and make consequential decisions in areas such as jurisprudence, lending, medicine, university admissions, and hiring. But while the technologies enabling AI have been rapidly advancing, the societal impacts are only beginning to be fathomed. Until recently, it seemed fashionable to hold that societal values must conform to technology's natural evolution--that technology should shape, rather than be shaped by, social norms and expectations. For example, Stewart Brand declared in 1984 that "information wants to be free."3 In 1999, a Silicon Valley executive told a group of reporters, "You have zero privacy … get over it."4 In 2010, Wired magazine cofounder Kevin Kelly published a book entitled What Technology Wants.5 "Move fast and break things" has been a common Silicon Valley mantra.6 But this orthodoxy has been undermined in the wake of an ever-expanding catalog of ethically fraught issues involving technology. While AI is not the only type of technology involved, it has tended to attract the lion's share of discussion about the ethical implications.
Artificial intelligence (AI) can be used to identify outbreaks such as the coronavirus, which, to date, has resulted in nearly 1,800 reported deaths and more than reported 71,000 infections. In a February 13 webinar, Casey Ross, national technology correspondent for STAT, pointed to efforts by John Brownstein, PhD, chief innovation officer at Boston Children's Hospital, to use machine learning to review social media posts, reports by physicians, news outlets, and information released by official public health entities to assess the condition's outbreak beyond China's borders. Brownstein's work is proof that AI is showing its value in tracking the outbreak of the disease, says Ross. Closer to home, healthcare systems around the country use AI to inform operational tasks such as scheduling. Some healthcare organizations use AI to pinpoint patients who need additional care, says Ross. For example, it's used in sepsis detection and prediction, the assessment of readmission risk, and the identification of patients who are deteriorating.