Conceived in 2012, Polly is ASI's artificial intelligence that predicts human behaviour in many different instances. "Polly is artificial intelligence and what she does is, in this case, goes out to all the ridings in Ontario and creates representative samples of each of those ridings so that she can accurately gauge the sentiment of that population and figure out who is likely to win the election," said CEO of Advanced Symbolics, Erin Kelly, about their AI -- named Polly -- that's currently being used to predict who is most likely to win the Ontario election. Polly has also been working and analyzing over the course of the year, determining how people's minds -- and votes -- change when certain things happen. This technology, as Kelly said in this interview with Steve Paikin of The Agenda, is pulling from a much larger sample size (ridings and the popular vote) than a typical poll. ICYMI: NDP momentum continues in Ontario election campaign ASI has published Polly's methodology, which details how the AI operates.

A'Help Wanted' sign hangs on a window in New York City, May 4, 2018. U.S. unemployment has fallen to a near historic low of 3.9 percent and is even lower for computer professionals and engineers. The premise of the Trump administration's "Buy American and Hire American" executive order, which has unleashed numerous measures to restrict high-skilled immigration, is that U.S. professionals can't get jobs because of immigrants. This raises a legitimate question: Has anyone in the administration making U.S. immigration policy checked the government data on unemployment – or do they simply choose to ignore it? The unemployment rate among people with at least a bachelor's degree in "computer and math science" occupations was only 2% for the first quarter of 2018, according to estimates from the Department of Labor Bureau of Labor Statistics' Current Population Survey.

Lt. Col. Stanislav Petrov settled into the commander's chair in a secret bunker outside Moscow. His job that night was simple: Monitor the computers that were sifting through satellite data, watching the United States for any sign of a missile launch. It was just after midnight, Sept. 26, 1983. A single word flashed on the screen in front of him. The fear that computers, by mistake or malice, might lead humanity to the brink of nuclear annihilation has haunted imaginations since the earliest days of the Cold War.

We present a novel approach that enables photo-realistic re-animation of portrait videos using only an input video. In contrast to existing approaches that are restricted to manipulations of facial expressions only, we are the first to transfer the full 3D head position, head rotation, face expression, eye gaze, and eye blinking from a source actor to a portrait video of a target actor. The core of our approach is a generative neural network with a novel space-time architecture. The network takes as input synthetic renderings of a parametric face model, based on which it predicts photo-realistic video frames for a given target actor. The realism in this rendering-to-video transfer is achieved by careful adversarial training, and as a result, we can create modified target videos that mimic the behavior of the synthetically-created input. In order to enable source-to-target video re-animation, we render a synthetic target video with the reconstructed head animation parameters from a source video, and feed it into the trained network -- thus taking full control of the target. With the ability to freely recombine source and target parameters, we are able to demonstrate a large variety of video rewrite applications without explicitly modeling hair, body or background. For instance, we can reenact the full head using interactive user-controlled editing, and realize high-fidelity visual dubbing. To demonstrate the high quality of our output, we conduct an extensive series of experiments and evaluations, where for instance a user study shows that our video edits are hard to detect.

Fine particulate matter (PM$_{2.5}$) is one of the criteria air pollutants regulated by the Environmental Protection Agency in the United States. There is strong evidence that ambient exposure to (PM$_{2.5}$) increases risk of mortality and hospitalization. Large scale epidemiological studies on the health effects of PM$_{2.5}$ provide the necessary evidence base for lowering the safety standards and inform regulatory policy. However, ambient monitors of PM$_{2.5}$ (as well as monitors for other pollutants) are sparsely located across the U.S., and therefore studies based only on the levels of PM$_{2.5}$ measured from the monitors would inevitably exclude large amounts of the population. One approach to resolving this issue has been developing models to predict local PM$_{2.5}$, NO$_2$, and ozone based on satellite, meteorological, and land use data. This process typically relies developing a prediction model that relies on large amounts of input data and is highly computationally intensive to predict levels of air pollution in unmonitored areas. We have developed a flexible R package that allows for environmental health researchers to design and train spatio-temporal models capable of predicting multiple pollutants, including PM$_{2.5}$. We utilize H2O, an open source big data platform, to achieve both performance and scalability when used in conjunction with cloud or cluster computing systems.

TOLEDO, OHIO – No longer a novelty, drones are becoming an everyday tool for more police and fire departments, new research has found. The number of public safety agencies with drones has more than doubled since the end of 2016, according to data collected by the Center for the Study of the Drone at New York's Bard College. The center estimated that just over 900 police, sheriff, fire and emergency agencies now have drones, with Texas, California, and Wisconsin leading the way, the study showed. While many law enforcement drone units are just getting started and are in place in just a fraction of the public safety agencies around the country, police and fire departments are continuing to find new uses for the remote-controlled aircraft. They're being deployed to take photos of car accidents, guide firefighters through burning buildings and search for missing people and murder suspects.

While many companies view artificial intelligence as an integral part of their future success, very few have fully embraced the technology. According to a global Accenture survey, business executives believe that within the next two years, artificial intelligence will work next to humans in their organisations as a co-worker, collaborator and trusted advisor. Accenture predicts by 2022, firms that adopt AI can boost revenues by up to 38 per cent. But rolling out AI at scale across a company is far easier said than done. And despite the lofty ambitions, very few businesses locally have taken AI beyond the experimental stage. There are still significant hurdles to be overcome when adopting AI in a significant way.

This hamburger-flipping robot was so popular when it debuted, it couldn't keep up with demand. Now Flippy is back and even better than before, as USA TODAY's Jefferson Graham shows us in Talking Tech. After a rocky debut in March that only lasted one day, Flippy, the hamburger-flipping robot, is back in action at the Caliburger restaurant here in the heart of this Los Angeles suburb. "Now he moves like a ninja and is more reliable," says David Zito, the CEO of Miso Robotics, which created Flippy. Miso had convinced Caliburger that a $100,000 robot could take the place of short-order cooks, who often quit after just working a few weeks because it's so hot in the kitchen. With automated flipping of burgers, there was no break time and customers could get their orders consistently, working side by side with humans who prepared the patties and assembled them after cooking into buns.

'Artificial intelligence software capable of interpreting images, matching faces and analysing patterns of communication is being piloted by UK police forces to speed up examination of mobile phones seized in crime investigations. Cellebrite, the Israeli-founded and now Japanese-owned company behind some of the software, claims a wider rollout would solve problems over failures to disclose crucial digital evidence that have led to the collapse of a series of rape trials and other prosecutions in the past year. However, the move by police has prompted concerns over privacy and the potential for software to introduce bias into processing of criminal evidence. As police and lawyers struggle to cope with the exponential rise in data volumes generated by phones and laptops in even routine crime cases, the hunt is on for a technological solution to handle increasingly unmanageable workloads. Some forces are understood to have backlogs of up to six months for examining downloaded mobile phone contents.

The Trump administration wants to make artificial intelligence great again. We're not really sure -- the administration just announced a new AI task force that will promote American artificial intelligence efforts, but it's not yet clear what exactly this group will do. The Select Committee on Artificial Intelligence, an effort of the National Science and Technology Council, was announced at a May 10 White House event that hosted representatives from tech giants like Google, Amazon and Facebook. The announcement by Michael Kratsios, deputy chief technology officer and the president's deputy assistant, was thick with aspiration, describing how robotics efforts that have seen success -- such as "Robotics Row" in Pittsburgh -- could be used as models to spur job growth for workers hurt by automation. Yet it was thin on details of how the task force would help accomplish that.