DARPA has a new Grand Challenge underway, but it's not an automation moonshot like the self-driving car challenges of the early 2000s or the recent (and hilarious) Robotics Challenge. The Defense Department's R&D wing wants to revolutionize something with a bit less sex appeal, but plenty of real-world applications: radio frequency spectrum splitting. The Spectrum Collaboration Challenge, which DARPA has cleverly abbreviated SC2, is about getting the billions and billions of wireless devices out there to play nice together rather than fight for space in the increasingly crowded RF landscape. Seriously, check out that cool chart at the top (much bigger version at DARPA's site if you want to print out the poster – 56k warning): everything is spoken for right up to the border with microwave frequencies, and more gadgets are crowding into each one daily. "The current practice of assigning fixed frequencies for various uses irrespective of actual, moment-to-moment demand is simply too inefficient to keep up with actual demand and threatens to undermine wireless reliability," said William Chappell, director of DARPA's Microsystems Technology Office, in a DARPA press release.

Artificial Intelligence, or AI, is expected to be the next big thing in technology, and many tech companies are investing heavily to make sure they don't lose out on opportunities as and when the technology becomes more mainstream in the coming years. While AI has already started intriguing and exciting industry insiders and tech enthusiasts alike, the increasing chatter surrounding the new technology has given rise to multiple concerns regarding job losses and science fiction-like scenarios of AI-enabled robots starting to exert control over humans, eventually taking over the planet, not unlike the dystopian sci-fi movie'I, Robot'. While Alphabet Chairman, Mr. Eric Schmidt, has already admitted that jobs may be at stake because of artificial intelligence, the founder and CEO of Facebook, Mr. Mark Zuckerberg, recently categorically denied that AI-enabled robots are about to take over the planet any time soon. Whatever be the case, most established technology companies are diving into AI-related research headlong, and while American tech giants like Google and Facebook are already burning the midnight oil on research related to artificial intelligence, South Korean conglomerate, Samsung, is also spending the big bucks by investing in a number of tech startups worldwide, in an effort to increase its presence in the sector. According to reports in the South Korean media, Samsung Venture Investment, which happens to be a subsidiary of the Samsung Group and hence, a sister concern for Samsung Electronics, has already invested a truckload of money in more than ten global tech companies, all of which are deeply involved in AI-related research.

Microsoft has apologized for the conduct of its racist, abusive machine learning chatbot, Tay. The bot, which was supposed to mimic conversation with a 19-year-old woman over Twitter, Kik, and GroupMe, was turned off less than 24 hours after going online because she started promoting Nazi ideology and harassing other Twitter users. The company appears to have been caught off-guard by her behavior. A similar bot, named XiaoIce, has been in operation in China since late 2014. XiaoIce has had more than 40 million conversations apparently without major incident.

Everyone in business wants to know what's going to happen in the future, and for some time now Ford has been investing in futurism, an evolving academic and professional discipline. The need for this was particularly evident after the Business Insider Transportation team in New York spent a few days at the New York Auto Show, asking everyone to predict was will happen in 2016 -- and beyond. The car business these days is all about change: automakers becoming "mobility providers," electric cars potentially displacing gas-powered vehicles, even autos driving themselves. Heck, even Apple may get in on the action. For nearly a decade, Sheryl Connelly has been Ford's manager of global consumer trends and futuring.

It took less than 24 hours. Microsoft had released its latest experiment with artificial intelligence: a Twitter bot named Tay that was designed to research and foster "conversational understanding." But Tay learned too much, much too young. And, as with so many things on the internet, the best of intentions went awry almost immediately. Tay was programmed to edit responses to her on Twitter in order to form new thoughts and sentences.

A fully loaded fireproof mini tankbot has proved size really doesn't matter. Called Uran-9, this mini tank bot stands just a few feet taller than a human and is fully loaded with a machine gun, missiles and a 30-millimeter cannon that fires 350 to 400 rounds per minute. A Russian defense organization created this unmanned vehicle to provide reconnaissance and fire support to infantry and counter-terror units. Uran-9 stands just a few feet taller than the average human being, but there is no need to be any bigger as the machine does not transport soldiers. This vehicle will assists infantry units and counter-terrorism groups by reaching places soldiers are unable to travel.

We investigate the choice of tuning parameters for a Bayesian multi-level group lasso model developed for the joint analysis of neuroimaging and genetic data. The regression model we consider relates multivariate phenotypes consisting of brain summary measures (volumetric and cortical thickness values) to single nucleotide polymorphism (SNPs) data and imposes penalization at two nested levels, the first corresponding to genes and the second corresponding to SNPs. Associated with each level in the penalty is a tuning parameter which corresponds to a hyperparameter in the hierarchical Bayesian formulation. Following previous work on Bayesian lassos we consider the estimation of tuning parameters through either hierarchical Bayes based on hyperpriors and Gibbs sampling or through empirical Bayes based on maximizing the marginal likelihood using a Monte Carlo EM algorithm. For the specific model under consideration we find that these approaches can lead to severe overshrinkage of the regression parameter estimates in the high-dimensional setting or when the genetic effects are weak. We demonstrate these problems through simulation examples and study an approximation to the marginal likelihood which sheds light on the cause of this problem. We then suggest an alternative approach based on the widely applicable information criterion (WAIC), an asymptotic approximation to leave-one-out cross-validation that can be computed conveniently within an MCMC framework.

This article outlines a method for automatically generating models of dynamic decision-making that both have strong predictive power and are interpretable in human terms. This is useful for designing empirically grounded agent-based simulations and for gaining direct insight into observed dynamic processes. We use an efficient model representation and a genetic algorithm-based estimation process to generate simple approximations that explain most of the structure of complex stochastic processes. This method, implemented in C++ and R, scales well to large data sets. We apply our methods to empirical data from human subjects game experiments and international relations. We also demonstrate the method's ability to recover known data-generating processes by simulating data with agent-based models and correctly deriving the underlying decision models for multiple agent models and degrees of stochasticity.

A report of drone sightings from the Federal Aviation Administration (FAA) shows that despite a new registration scheme, near misses between unmanned and piloted aircraft in American are on the rise. Sightings by pilots and airport officials have steadily increased from less than one a day in 2014, to over 3.5 between August 2015 and January this year, many of them from commercial passenger aircraft. In the most serious incident, the pilot of an American Airlines jet last September had to swerve to avoid a drone. On September 13, flight 475 took off from Atlanta, Georgia en route to Charlotte, North Carolina. It was climbing to 3,500 ft when the pilot of the Airbus had to take evasive action to avoid a collision with an unidentified unmanned aerial system (UAS) or drone.

A drone has successfully delivered a package to a residential location in a Nevada town in what its maker and the state's governor said on Friday was the first fully autonomous urban drone delivery in the US. Matt Sweeney, chief executive of drone-maker Flirtey, said the six-rotor drone flew about a half-mile along a programmed delivery route on 10 March, then lowered the package outside a vacant residence in Hawthorne. The route was established using GPS. A pilot and visual observers were on standby during the flight but were not needed, Sweeney said. He said the package included bottled water, food and a first-aid kit.