By building on a recently introduced genetic-inspired attribute-based conceptual framework for safety risk analysis, we propose a novel methodology to compute construction univariate and bivariate construction safety risk at a situational level. Our fully data-driven approach provides construction practitioners and academicians with an easy and automated way of extracting valuable empirical insights from databases of unstructured textual injury reports. By applying our methodology on an attribute and outcome dataset directly obtained from 814 injury reports, we show that the frequency-magnitude distribution of construction safety risk is very similar to that of natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state-of-the-art techniques in hydroclimatology and insurance, we introduce univariate and bivariate nonparametric stochastic safety risk generators, based on Kernel Density Estimators and Copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithfully to the original data, allowing safetyrelated decision-making under uncertainty to be grounded on extensive empirical evidence. Just like the accurate modeling and simulation of natural phenomena such as wind or streamflow is indispensable to successful structure dimensioning or water reservoir management, we posit that improving construction safety calls for the accurate modeling, simulation, and assessment of safety risk. The underlying assumption is that like natural phenomena, construction safety may benefit from being studied in an empirical and quantitative way rather than qualitatively which is the current industry standard. Finally, a side but interesting finding is that attributes related to high energy levels and to human error emerge as strong risk shapers on the dataset we used to illustrate our methodology.

Trucking paid for Scott Spindola to take a road trip down the coast of Spain, climb halfway up Machu Picchu, and sample a Costa Rican beach for two weeks. The 44-year-old from Covina now makes up to 70,000 per year, with overtime, hauling goods from the port of Long Beach. He has full medical coverage and plans to drive until he retires. But in a decade, his big rig may not have any need for him. Carmaking giants and ride-sharing upstarts racing to put autonomous vehicles on the road are dead set on replacing drivers, and that includes truckers.

Transport ministers from the Group of Seven advanced economies agreed Sunday to strengthen cooperation in the railway and airline sectors as they wrapped up their three-day meeting in the resort town of Karuizawa, Nagano Prefecture. Prior to the conclusion of the gathering, ministers from Britain, Canada, France, Germany, Italy, Japan and the United States plus the European Union adopted a declaration Saturday pledging to reinforce international cooperation in creating safety regulations to promote self-driving cars. The conference was the last of the ministerial meetings related to May's G-7 leaders' Ise-Shima summit in Mie Prefecture. "We will cooperate with each other and exercise leadership to support the early commercialization of automated and connected vehicle technologies," the declaration adopted at the Saturday meeting said. "We obtained a common understanding to make efforts in the same direction to create regulation frameworks that (will) tend to vary depending on region," transport minister Keiichi Ishii told a news conference after the meeting.

A startup technology company, AppZen, has introduced new technology that is leveraging artificial intelligence to examine expense reports for signs of fraud. "We are an AI company and we've built a solution for back-office automation," AppZen CEO Anant Kale told me. "As part of that, our first focus area is back office expense processing, which is essentially how to find compliance issues within expenses. We are focusing on automating the research and reasoning that human auditors do today and putting it into machines." Kale noted that most companies don't really audit anywhere near all of their expense reports.

Government officials say autonomous vehicles will make transportation safer, more accessible, more efficient and cleaner and last week, the Department of Transportation released guidelines for the testing and deployment of automated vehicles, which detail how the vehicles should perform, and include a model for state policies. Self-driving vehicles are just the tip of the autonomous revolution. In 2016, autonomous robot doctors perform surgery; algorithms invest your money; robocops patrol shopping malls; and if you end up in hospital, a computer system can determine how quickly you get treated. Many decisions made by autonomous machines have moral implications -- yet little is determined about what ethics machines follow, or who decides what those ethical assumptions should be. In Florida in May, Joshua Brown died when an autopilot system did not recognize a tractor-trailer turning in front of his Tesla Model S and his car plowed into it -- the first fatality involving an autonomous vehicle.

Robot cars in the US will have to be fitted with black boxes that record what happens if they crash, under US policy covering the vehicles. The demand is part of a newly issued US Transportation Department policy covering autonomous vehicles. The guidelines will replace a patchwork of different, and often contradictory, rules drawn up by separate states. The US government plans to vet the code controlling robot cars before they win permission to drive alongside humans. "If a self-driving car isn't safe, we have the authority to pull it off the road," wrote President Barack Obama in an editorial for the Pittsburgh Post Gazette outlining the policy.

Contrary to what some people will try to tell you, there are Canadian values. We aren't just a collection of disparate people who happen to occupy the same country. We're one of the few nations on earth with a native-born and immigrant population that welcomes people from all over the world to come here, work hard, obey the law, live in peace and freedom, and help each other. To pick one of countless examples, Canada is Jamaican-Canadians extending a helping hand to a brilliant young Albanian-born computer student, whose family immigrated here 17 years ago when he was four, leaving behind the turmoil of the Kosovo war. Jurgen Aliaj, 21, is the student and the Jamaican-Canadians who are helping him head the Independent United Order of Solomon, a charity run by my friends Lloyd and Madaine Seivright.

As many noted during the AI Now Experts' Workshop, the means to create and train AI systems are expensive and limited to a handful of large actors. Or, put simply, it's not possible to DIY AI without significant resources. Training AI models requires a huge amount of data – the more the better. It also requires significant computing power, which is expensive. This limits fundamental research to those who can afford such access, and thus limits the possibility of democratically creating AI systems that serve the goals of diverse populations.

Now envision a million of these pixels-a megapixel's worth-in an array that covers a thumbnail. Take one more mental trip: dive down onto the surface of the semiconductor hosting all of these pixels and marvel at each pixel's associated tech-mesh of more than 1,000 integrated transistors, which provide each and every pixel with a tiny reprogrammable brain of its own. That is the vision for DARPA's new Reconfigurable Imaging (ReImagine) program. "What we are aiming for," said Jay Lewis, program manager for ReImagine, "is a single, multi-talented camera sensor that can detect visual scenes as familiar still and video imagers do, but that also can adapt and change their personality and effectively morph into the type of imager that provides the most useful information for a given situation." This could mean selecting between different thermal (infrared) emissions or different resolutions or frame rates, or even collecting 3-D LIDAR data for mapping and other jobs that increase situational awareness.

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In order to foster better exploration of the state space, specially in high-dimensional applications, several schemes employing multiple parallel MCMC chains have been recently introduced. In this work, we describe a novel parallel interacting MCMC scheme, called {\it orthogonal MCMC} (O-MCMC), where a set of "vertical" parallel MCMC chains share information using some "horizontal" MCMC techniques working on the entire population of current states. More specifically, the vertical chains are led by random-walk proposals, whereas the horizontal MCMC techniques employ independent proposals, thus allowing an efficient combination of global exploration and local approximation. The interaction is contained in these horizontal iterations. Within the analysis of different implementations of O-MCMC, novel schemes in order to reduce the overall computational cost of parallel multiple try Metropolis (MTM) chains are also presented. Furthermore, a modified version of O-MCMC for optimization is provided by considering parallel simulated annealing (SA) algorithms. Numerical results show the advantages of the proposed sampling scheme in terms of efficiency in the estimation, as well as robustness in terms of independence with respect to initial values and the choice of the parameters.