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.

NVIDIA is hiring Machine Learning Framework software engineers for its GPU-accelerated Machine Learning team. Academic and commercial groups around the world are using GPUs to power a revolution in machine learning, enabling breakthroughs in problems from image classification to speech recognition to natural language processing. The group will be responsible for developing core deep learning algorithms for both internal and 3rd party codebases. Framework Software Engineers will be active members of the open source deep learning software engineering community, and will contribute directly to software packages such as Caffe, Theano, Torch, and KALDI.

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.

Every weekend we select a handful of in-depth articles we think are worth a bit of your valuable time, either because they peel back the layers on a compelling business story, or somehow make us look at business in a different light. AI may undermine big-company advantages. Machine learning – software that can improve itself without human intervention – may mean trouble for big companies that depend on their heft to outmaneuver smaller upstarts, writes Howard Yu for the Harvard Business Review. And for a sneak preview of where the world is headed, one need not look further than the success story of AlphaGo, an artificial intelligence that beat a champion of the ancient game of Go, something that was previously thought to be impossible. "It is easy to imagine a world where self-taught algorithms will play a much bigger role in coordinating economic transactions; AlphaGo simply shows us what is possible in the near future. With instantaneous adjustment, automatic optimization, and continuous improvement all quietly managed by unsupervised algorithms, the redundancy of production facilities and wastage in the supply chain should become headaches of the past."

Lately there seems to be a surge in the term machine learning. Much like big data a few years ago, machine learning is the new buzzword -- and the two terms actually go hand in hand. With increasing volumes of data now stored in distributed environments such as Hadoop, it's possible to quickly produce models that can analyze bigger, more complex data, and deliver faster and more accurate results – two critical elements in the battle against fraud. The more time it takes to discover an instance of fraud, the more the victim organization loses. Association of Certified Fraud Examiners (ACFE) estimates fraud costs organizations 5 percent of annual revenues worldwide.

Technology is the future of retail. Digital data, machine learning, cloud-powered analytics and the Internet of Things (IoT) will separate the wheat from the chaff in tomorrow's retail industry. A recent Sector Insights government report (PDF) said that retailers will need to embrace the latest technology trends, such as big data, and have the skills to work with digital systems if they are to be successful in the future. The retail industry is on the whole a voracious adopter of modern data-centric technology, aping the manufacturing world by using big data analytics to streamline supply chains, and using smartphone apps and wireless beacons to harvest customer data to deliver better service. However, Robert Hetu, retail research director at analyst house Gartner, noted that, despite having the technology to collect and access large amounts of digital data, retailers fail to put it to effective use.