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.

Demand for non-equity trading algorithms serving institutional asset managers and retail investors is expanding the prevalence of artificial intelligence in the world's financial markets. A recent report by Thomson Reuters estimates that algorithmic trading systems now handle 75 per cent of the volume of global trades worldwide and this figure is predicted, by those in the industry, to grow steadily. Firstly, while the institutional market has enjoyed a large variety of "algos" serving the equity markets to date, other areas such as futures are still witnessing huge product demand and innovation as a result. Secondly, regulations affecting the institutional investment market, such as the European Union Markets in Financial Instruments Directive II or MiFID II, are pushing for greater automation of trades in some asset classes which traditionally were not executed electronically. The fixed income market is a prime example and negotiations between industry groups are ongoing as to how practical a fully automated fixed income could really be, given the magnitude of the required shift from telephone to electronic trading.

Responses to a White House request for information about the future of artificial intelligence show a continued... This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent. By submitting your email address, you agree to receive emails regarding relevant topic offers from TechTarget and its partners. You can withdraw your consent at any time.

I recently consulted with the US Navy on all things "transhuman." In those conversations about how science and technology can help the human race evolve beyond its natural limits, it was clear that military is keen on replacing human soldiers with both fighting and peacekeeping machines so American military lives never have to come under fire or be in harm's way. However, it's the peacekeeping technology that is particularly interesting for many civilians. While you wouldn't want an armed Terminator in your home, you might like a robot that travels with you and offers personal protection, like a bodyguard. In a survey by Travelzoo of 6,000 participants, nearly 80 percent of people said they expect robots to be a significant part of their lives by 2020 -- and that those robots might even join them on holidays.

The more data you have, the more likely you are to hallucinate patterns. Supposedly, the more phone records the NSA looks at, the more likely it is to flag an innocent as a potential terrorist because he accidentally matched a terrorist detection rule. Mining more attributes of the same entities can indeed increase the risk of hallucination, but machine learning experts are very good at keeping it to a minimum. On the other hand, mining more entities with the same set of attributes decreases the risk, because the rules learned from them will have stronger support. And some learning algorithms can find patterns involving multiple entities, which makes them even more robust: a person videotaping the New York City Hall may not be suspicious, and another buying large quantities of ammonium nitrate may not be either; but if the two are in close phone contact, perhaps the FBI should take a look, just to make sure it's not a bomb plot.