New technology could lead humans to relinquish control over decisions to use lethal force. As artificial intelligence advances, the possibility that machines could independently select and fire on targets is fast approaching. Fully autonomous weapons, also known as "killer robots," are quickly moving from the realm of science fiction toward reality. The unmanned Sea Hunter gets underway. At present it sails without weapons, but it exemplifies the move toward greater autonomy.

Most of us carry a tracking device around with us every day in the form of the mobile phones, but some people are going further and having microchips embedded in their bodies. Now the US Navy has grown so concerned over the practice that is drawing up an official policy to help it deal with personnel who have chips implanted. Officials consulted with American presidential candidate and'transhumanist' Zoltan Istvan to discuss the implications of fitting humans with microchips to enhance their powers. Officials from the US Navy met with US presidential candidate and'transhumanist' Zoltan Istvan to discuss the'merger of humans and machines'. The US Defense Advanced Research Projects Agency is already working on microchips that can be implanted into soldiers' brains to make them more resilient to warfare.

The FBI's facial recognition database has more than 400 million pictures to help its criminal investigations, but lacks adequate safeguards for accuracy and privacy protection, a congressional audit has revealed. Totalling 411.9 million images, privacy campaigners have slammed the'unprecedented number of photographs, most of which are of Americans and foreigners who have committed no crimes.' The huge database - which enables investigators to automatically search images for criminal suspects - 'is far greater than had previously been understood' and raises concerns'about the risk of innocent Americans being inadvertently swept up in criminal investigations,' said Senator Al Franken, who requested the study. The FBI's facial recognition database includes some 30 million criminal mugshots and 140 million images from visa applications by foreign nationals The FBI's database includes some 30 million criminal mugshots and 140 million images from visa applications by foreign nationals, the GAO found. It also contains drivers' license pictures from 16 US states and 6.7 million photos from the Defense Department's biometric identification system of individuals detained by US forces abroad, among others.

The EFF pointed out that the "unprecedented number of photographs" isn't the only problem. GAO's report also said that the FBI didn't test its system thoroughly for accuracy. It "has done little to make sure that its search results... do not include photos of innocent people," the nonprofit org wrote in its post. Since facial recognition technologies aren't perfect and still has issues recognizing people of color, FACE could return results with law-abiding people in the mix when law-enforcement agencies use the system to search for suspects. It could cause even more issues if the government grants the feds' request for its databases to be exempted from several key provisions of the Privacy Act. For instance, the FBI doesn't want to tell people who ask if they're in the database and wants to be legally allowed to withhold that information.

Google is starting a research unit in Europe focused solely on machine learning, a major branch of artificial intelligence. The Zurich-based project, announced today (June 16), will be key to the company's ambitions, as it bets big on machine learning to power its next generation of products. These include the digital assistant inside its Allo chat app, its driverless car efforts, and enhancements to its ubiquitous search engine. The new unit, called Google Research, Europe, comes at a time when the search giant is facing serious scrutiny from European authorities. The European commission has the company in its crosshairs, as it faces two antitrust charges, over a search product and Android, which could rack up billions in fines.

As far as anyone can tell, today marks the 200th anniversary of Mary Wollstonecraft Godwin getting up after a sleepless night and declaring: "I've found it! What will terrify me will terrify others. I need only describe the spectre which had haunted my midnight pillow". She had hit upon the idea that would become Frankenstein, the Modern Prometheus, the cautionary tale that has provided a vocabulary for the relationship between science and society ever since. Appropriately, it has been a dark and stormy (OK, rainy) night on the shores of Lake Geneva, where I and other Frankenstein-botherers have been gathering at the Brocher Foundation, a few miles from the grand villa where Mary was staying with Lord Byron, her future husband Percy and associated hangers-on.

Political elections – for better or worse – have become a game show. The more social media, the more clickbait headlines ... whatever generates a lot of buzz is one way to make our mark in an election,

Tokyo, December 10, 2015 - NEC Corporation (NEC; TSE: 6701) today announced the development of a'system operations-visualization and anomaly-analysis technology' that uses artificial intelligence (AI) to automatically detect unknown cyber-attacks against social infrastructure and enterprise systems. The new technology learns (through machine learning) the normal state of OS-level operations (program start-up, file access, communications, etc.) for entire ICT systems, including PCs and servers. It then carries out real-time comparisons and analysis of current operations in the system's normal state and automatically isolates particular points that deviate from the normal state by using system operation tools and Software-Defined Networking (SDN). Further, a detailed knowledge of the system behavior makes it possible to identify the extent of damage 90% faster than the time required in conventional manual investigation. Accurate anomaly detection and quick specification of damaged areas by the new technology minimize the damage from cyber-attacks and enable recovery without stopping an entire user-system.

The increasing interest in complex networks research has been a consequence of several intrinsic features of this area, such as the generality of the approach to represent and model virtually any discrete system, and the incorporation of concepts and methods deriving from many areas, from statistical physics to sociology, which are often used in an independent way. Yet, for this same reason, it would be desirable to integrate these various aspects into a more coherent and organic framework, which would imply in several benefits normally allowed by the systematization in science, including the identification of new types of problems and the cross-fertilization between fields. More specifically, the identification of the main areas to which the concepts frequently used in complex networks can be applied paves the way to adopting and applying a larger set of concepts and methods deriving from those respective areas. Among the several areas that have been used in complex networks research, pattern recognition, optimization, linear algebra, and time series analysis seem to play a more basic and recurrent role. In the present manuscript, we propose a systematic way to integrate the concepts from these diverse areas regarding complex networks research. In order to do so, we start by grouping the multidisciplinary concepts into three main groups, namely features, similarity, and network connectivity. Then we show that several of the analysis and modeling approaches to complex networks can be thought as a composition of maps between these three groups, with emphasis on nine main types of mappings, which are presented and illustrated. Such a systematization of principles and approaches also provides an opportunity to review some of the most closely related works in the literature, which is also developed in this article.

We present a new trace estimator of the matrix whose explicit form is not given but its matrix multiplication to a vector is available. The form of the estimator is similar to the Hutchison stochastic trace estimator, but instead of the random noise vectors in Hutchison estimator, we use small number of probing vectors determined by machine learning. Evaluation of the quality of estimates and bias correction are discussed. An unbiased estimator is proposed for the calculation of the expectation value of a function of traces. In the numerical experiments with random matrices, it is shown that the precision of trace estimates with $\mathcal{O}(10)$ probing vectors determined by the machine learning is similar to that with $\mathcal{O}(10000)$ random noise vectors.