The maker of the world's most deadly firearm has unveiled plans for a radical AI controlled gun for the Russian military. Kalashnikov, best known for its AK-47 rifle, is building'a range of products based on neural networks,' including a'fully automated combat module' that can identify and shoot at its targets. The new products were revealed in an interview with Kalashnikov spokeswoman Sofiya Ivanova by TASS, a Russian government information agency. The Kalashnikov'combat module' will consist of a gun connected to a console that constantly analyses image data to identify targets. According to Kalashnikov it will be able to'make decisions' on whether to shoot.

In an analysis that investigated and built upon several recent studies on the topic, researchers found that about half of jobs are at risk, with automation targeting low-wage, low-skilled positions in particular. In an analysis that investigated and built upon several recent studies on the topic, researchers found that about half of jobs are at risk, with automation targeting low-wage, low-skilled positions in particular. In the new study, the researchers from the Center for Business and Economic Research at Ball State University compared the threat of offshoring and automation in the United States, revealing which jobs and regions in the country are most at risk. In the new study, the researchers from Ball State University compared the threat of offshoring and automation in the United States, revealing which jobs and regions in the country are most at risk.

With just months to go until Apple is expected to unveils its latest iPhone, new claims have emerged warning the firm has yet to start mass production of any of the three expected models. Economic Daily News is reporting that all three new iPhones, overhauled iPhone 8 and the '7s' iterative updates to the iPhone 7 and iPhone 7 Plus are yet to start mass production. The report says that the new 4.7-inch and 5.5-inch phones are expected to ramp up in August, a month behind the normal new iPhone production timelines. New claims suggest that Apple is working'feverishly' to fix software problems with its wireless charging and 3D face recognition systems. It also claims the OLED 5.8-inch iPhone, which is expected to have a bezel-less screen, may not be available until November.

Technology may be getting smarter but humans have been getting less intelligent since Victorian times, according to a controversial study. The research claims that up until 180 years ago, people were getting smarter thanks to natural selection favouring'survival of the sharpest'. The emergence of farming, cities and government would have made it easier for smarter people to get on in life, have more children and pass on their genes more widely. But that trend has now being reversed, researchers in Brussels claim. Genes driving intelligence have become less common since Victorian times, because advances in medicine and nutrition means people with lower IQs can have more children that survive into adulthood.

Researchers have uncovered the writings inside a medieval manuscript using a new imaging technique. The book was a copy of Greek poet Hesiod's Work and Days printed in Venice in 1537, and it has two columns of writing surrounded by marginal comments on the book board - which the bookbinder likely tried to remove through washing or scraping. By combining two different imaging techniques, it was revealed that text on the book's board was sixth-century Roman Law code, with interpretive notes referring to the Canon Law written in the margins. It was revealed that combining the two methods initially used: Visible hyperspectral imaging (bottom left) with x-ray fluorescence imaging (top right) - provided the best image of the text. The study, conducted by researchers based at the Northwestern University-Art Institute of Chicago Center for Scientific Studies (NU-ACCESS), was published in the journal Analytics Chimica Acta.

A team of Afghan girls whose plight resounded with the world won a silver medal for "courageous achievement" at an international robotics contest in the United States, with judges praising the group's "can-do attitude". The First Global Challenge event in Washington ended on Tuesday, having attracted teams of teenagers from more than 150 nations. But all eyes were on the squad of girls from Afghanistan, who had twice travelled 800 kilometres to the American embassy in Kabul, only to have their visa applications turned down. They were finally granted entry with just one week to go until the event began after their story had gone viral. In an interview with Al Jazeera, before US officials decided to allow them in the country, team member Rodaba Noori said: "We wanted to show our talents to the world so they would know that we do have skills."

The Association for Advancing Automation (A3) cites that between 2010 and 2016, 136,748 robots were shipped to the US --the most in any seven-year period in the US robotics industry. At the same time, US manufacturing employment increased by 894,000 and the unemployment rate fell from 9.8% to 4.7%. Yet manufacturers, robotics associations, ethicists and media pundits are still fighting the robotics and jobs issue. Brett Brune, Editor in Chief of Smart Manufacturing magazine, argues that "the hand-wringing around robotics and jobs in the US really needs to stop." Manufacturers around the world, including in China, are busy figuring out how quickly to acquire robots.

Filters in convolutional networks are typically parameterized in a pixel basis, that does not take prior knowledge about the visual world into account. We investigate the generalized notion of frames designed with image properties in mind, as alternatives to this parametrization. We show that frame-based ResNets and Densenets can improve performance on Cifar-10+ consistently, while having additional pleasant properties like steerability. By exploiting these transformation properties explicitly, we arrive at dynamic steerable blocks. They are an extension of residual blocks, that are able to seamlessly transform filters under pre-defined transformations, conditioned on the input at training and inference time. Dynamic steerable blocks learn the degree of invariance from data and locally adapt filters, allowing them to apply a different geometrical variant of the same filter to each location of the feature map. When evaluated on the Berkeley Segmentation contour detection dataset, our approach outperforms all competing approaches that do not utilize pre-training. Our results highlight the benefits of image-based regularization to deep networks.

Neyman-Scott is a classic example of an estimation problem with a partially-consistent posterior, for which standard estimation methods tend to produce inconsistent results. Past attempts to create consistent estimators for Neyman-Scott have led to ad-hoc solutions, to estimators that do not satisfy representation invariance, to restrictions over the choice of prior and more. We present a simple construction for a general-purpose Bayes estimator, invariant to representation, which satisfies consistency on Neyman-Scott over any nondegenerate prior. We argue that the good attributes of the estimator are due to its intrinsic properties, and generalise beyond Neyman-Scott as well. Keywords: Neyman-Scott, consistent estimation, minEKL, Kullback-Leibler, Bayes estimation, invariance 1. Introduction In [24], Neyman and Scott introduced a problem in consistent estimation that has since been studied extensively in many fields (see [18] for a review).

Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics. Natural light harvesting in photosynthesis shows remarkable excitation energy transfer properties, which suggests that pigment-protein complexes could serve as blueprints for the design of nature inspired devices. Mechanistic insights into energy transport dynamics can be gained by leveraging numerically involved propagation schemes such as the hierarchical equations of motion (HEOM). Solving these equations, however, is computationally costly due to the adverse scaling with the number of pigments. Therefore virtual high-throughput screening, which has become a powerful tool in material discovery, is less readily applicable for the search of novel excitonic devices. We propose the use of artificial neural networks to bypass the computational limitations of established techniques for exploring the structure-dynamics relation in excitonic systems. Once trained, our neural networks reduce computational costs by several orders of magnitudes. Our predicted transfer times and transfer efficiencies exhibit similar or even higher accuracies than frequently used approximate methods such as secular Redfield theory