Just as it takes a tough man to make a tender chicken, it takes a smart filmmaker to make a stupid movie, which I mean in the best possible way. Science-fiction films, once a cinematic counterpart to pulp fiction, are today often big-budget, overproduced spectacles that substitute grandiosity for imagination. M. Night Shyamalan's new film, "Old" (which opens in theatres on Friday), is different. His frequent artistic pitfall is complication--the burdening of stories with extravagant yet undeveloped byways in order to endow them with ostensible significance and to stoke exaggerated effects. With "Old," facing the constraints of filming during the pandemic--on a project that he'd nonetheless planned before it--Shyamalan has created a splendid throwback of a science-fiction thriller that develops a simple idea with stark vigor and conveys the straight-faced glee of realizing the straightforward logic of its enticing absurdity.

Washington University researchers are working to develop artificial intelligence (AI) systems for health care, which have the potential to transform the diagnosis and treatment of diseases, helping to ensure that patients get the right treatment at the right time. In a new Viewpoint article published Dec. 10 in the Journal of the American Medical Association (JAMA), two AI experts at Washington University School of Medicine in St. Louis--Philip Payne, the Robert J. Terry Professor and director of the Institute for Informatics; and Thomas M. Maddox, MD, a professor of medicine and director of the Health Systems Innovation Lab--discuss the best uses for AI in health care and outline some of the challenges for implementing the technology in hospitals and clinics. In health care, artificial intelligence relies on the power of computers to sift through and make sense of reams of electronic data about patients--such as their ages, medical histories, health status, test results, medical images, DNA sequences, and many other sources of health information. AI excels at the complex identification of patterns in these reams of data, and it can do this at a scale and speed beyond human capacity. The hope is that this technology can be harnessed to help doctors and patients make better health-care decisions.

Washington University researchers are working to develop artificial intelligence (AI) systems for health care, which have the potential to transform the diagnosis and treatment of diseases, helping to ensure that patients get the right treatment at the right time. In a new Viewpoint article published Dec. 10 in the Journal of the American Medical Association (JAMA), two AI experts at Washington University School of Medicine in St. Louis -- Philip Payne, PhD, the Robert J. Terry Professor and director of the Institute for Informatics; and Thomas M. Maddox, MD, a professor of medicine and director of the Health Systems Innovation Lab -- discuss the best uses for AI in health care and outline some of the challenges for implementing the technology in hospitals and clinics. In health care, artificial intelligence relies on the power of computers to sift through and make sense of reams of electronic data about patients -- such as their ages, medical histories, health status, test results, medical images, DNA sequences, and many other sources of health information. AI excels at the complex identification of patterns in these reams of data, and it can do this at a scale and speed beyond human capacity. The hope is that this technology can be harnessed to help doctors and patients make better health-care decisions.

Let me preface this by saying it has been my experience, that barring the obvious bad apples, most people are basically good and want to do the right thing. So in 2018, here in our comfortable Western (and litigious) society let me submit that a hiring manager is unlikely to look at a resume and say to himself, "I don't want a woman in this role." And let me finally submit that this otherwise decent hiring manager might look at the same resume and think enthusiastically that this female or African American candidate, "would be a great fit for another position" which happens to be lower level or less technical. It is called unconscious bias and it is the subject of growing interest in both academia and human resource departments. Likewise, battling this tendency within all humans is a new trend in software for HR -- there have been many vendors coming to market with AI-driven products that promise to weed out unconscious bias from the hiring system.

In 2016, 37,461 people died in traffic accidents in the US, a 5.6 percent increase over 2015, according to the US Department of Transportation (DoT). This is down from 1970, when around 60,000 people died in traffic accidents in the US. The addition of safety features such as seat belts and air bags have reduced the number of deaths, and new technology from autonomous vehicles could help even more as driver error is eliminated. This ebook, based on a special feature from ZDNet and TechRepublic, looks at emerging autonomous transport technologies and how they will affect society and the future of business. DoT researchers estimate that fully autonomous vehicles, also known as self-driving cars, could reduce traffic fatalities by up to 94 percent by eliminating those accidents that are due to human error.

Twenty years ago today, Skynet, the fictional artificial intelligence network and antagonist of the "Terminator" movie franchise, became self-aware. In the films, a Silicon Valley tech company built the network of supercomputers for the U.S. military to replace the humans who control America's nuclear arsenal. The idea was that the program would offer better reaction times and fewer mistakes than humans and engage in strategic warfare unencumbered by emotion, political incentives or ego. According to dates cited in "Terminator 2: Judgment Day," the network was activated on Aug. 4, 1997 and started thinking on its own on Aug. 29, 1997. But Skynet had a problem.

The ex-bomber plant and home of Rosie the Riveter will transform this year into an autonomous vehicle technology test site. It once housed one of the largest factories in the world, pumping out B24 bombers to help America and her allies win World War II, and later transmissions when it was owned by General Motors. It once housed one of the largest factories in the world, pumping out B24 bombers to help America and her allies win World War II, and later transmissions when it was owned by General Motors. The former Willow Run bomber plant in Ypsilanti Township is mostly a memory now, demolished following GM's 2009 bankruptcy, except for a piece that houses the Yankee Air Museum. Land at the former 335-acre Willow Run site in Ypsilanti Township where the American Center for Mobility is located on in January 2017 that will be used for testing autonomous vehicles.

An unusual fleet of self-driving cars will take to the road in coming months. Unlike most automated vehicles, which are programmed to deal with the situations they may encounter, these cars will have taught themselves, in simulation, how to handle tricky scenarios safely. The cars will learn to navigate busy intersections, crowded highways, and packed rotaries using reinforcement learning, an approach inspired by the way animals learn to associate a reward with the behavior that led to it. Mobileye, an Israeli company that provides vehicle safety systems to many carmakers, announced at CES in Las Vegas last week that it will test the approach on the road, in collaboration with the German automaker BMW and the chip company Intel, in the second half of this year. In reinforcement learning, a computer is not hand-coded, or given specific examples to learn from; instead, it experiments, altering its own programming in light of the behavior that most reliably leads to a certain result.

When Joshua Brown switched on the Autopilot feature of his Tesla Model S on May 7, he would have been warned not to trust it. "Always keep your hands on the wheel. Be prepared to take over at any time," says the standard warning presented when Autopilot is turned on. But later that day Brown was killed when his car drove itself into the side of a semi-trailer that Autopilot had not detected. Federal investigations into the crash by the National Highway Traffic Safety Administration (NHTSA) and the National Transportation Safety Board (NTSB) may now question whether Tesla's design asks too much of drivers.