The military is launching tests on the world's largest unmanned surface vessel. Dubbed the'Sea Hunter', the 132ft ship is designed to travel thousands of miles out at sea without a single crew member on board. Experts say the vessel has the potential to revolutionise not only the military's maritime service but also commercial shipping. The 132ft-long (40-metre) unarmed prototype, dubbed Sea Hunter, is the naval equivalent of Google's self-driving car, designed to cruise on the ocean's surface without a crew. The ship's projected 20 million ( 14.2 million) price tag and its 20,000 ( 14,300) daily operating cost make it relatively inexpensive for the navy The 132ft-long (40-metre) unarmed prototype, dubbed Sea Hunter, is the naval equivalent of Google's self-driving car, designed to cruise on the ocean's surface without a crew.

Network analysis offers a new set of techniques to tackle the persistent and growing problem of complex fraud. Network analysis supplements traditional techniques by providing a mechanism to bridge investigative and analytics methods. Beyond base visualization, network analysis provides a standardized platform for complex fraud pattern storage and retrieval, pattern discovery and detection, statistical analysis, and risk scoring. This article gives an overview of the main challenges and demonstrates a promising approach using a hands-on example. With swelling globalization, advanced digital communication technology, and international financial deregulation, fraud investigators face a daunting battle against increasingly sophisticated fraudsters.

When you think of artificial intelligence, the first image that likely comes to mind is one of sentient robots that walk, talk and emote like humans. It's known as machine learning, and it revolves around enlisting computers in the task of sorting through the massive amounts of data that modern technology has allowed us to generate (a.k.a. One of the places machine learning is turning out to be the most beneficial is in the environmental sciences, which have generated huge amounts of information from monitoring Earth's various systems -- underground aquifers, the warming climate or animal migration, for example. A slew of projects have been popping up in this relatively new field, called computational sustainability, that combine data gathered about the environment with a computer's ability to discover trends and make predictions about the future of our planet. This is useful to scientists and policy-makers because it can help them develop plans for how to live and survive in our changing world.

The military is launching tests on the world's largest unmanned surface vessel -- a self-driving, 132-foot ship designed to travel thousands of miles out at sea without a single crew member on board. The so-called "Sea Hunter" has the potential to revolutionize not only the military's maritime service but commercial shipping -- marking the first step toward sending unmanned cargo vessels between countries, according to military officials, who showed off the ship in San Diego on Monday before it was put in the water. The Pentagon's research arm, the Defense Advanced Research Projects Agency, or DARPA, developed the ship along with Virginia-based Leidos. DARPA will test it in conjunction with the Navy over the next two years off California's coast. The tests will largely focus on its ability to react on its own to avoid collisions with seafaring traffic.

Elon Musk's AI research group has opened a "gym" for robots, to ensure that they can be properly tested. The new project is an attempt to bring together training for artificially intelligent machines, allowing them to be fairly compared with each other – and avoid any problem results. The gym has been launched as the first project from OpenAI, a research group that is funded by backers including Mr Musk as well as a range of other tech leaders. The project launched in December and aims to "advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return". Boston Dynamics describes itself as'building dynamic robots and software for human simulation'.

As expected, the European and Russian space agencies have delayed their next mission to Mars from 2018 to 2020. It is a decision that has been well telegraphed in recent months, with both officials and industry chiefs expressing their doubts that all the hardware could be made ready in time. The mission would see a rover landed on the Red Planet. Capable of drilling up to 2m below the surface, it would search for signs of past or present life. It is the second part of the so-called ExoMars programme. The first part - a satellite to study the atmosphere of the planet - was launched successfully in March and should arrive in October.

Our moon is scarred with a series of so-called'lunar swirls' that stretch tens of miles across. They are only found on the moon and their origin has remained a mystery since their discovery in 1970s. Previous observations suggested comets may have caused the'tattoos', while others blamed the mystery marks on solar wind. Now, using computer models to test these theories, Nasa has discovered the most likely cause is how charged particles respond in the satellite's magnetic field. Previous observations suggested comets may have caused the'tattoos' (pictured), while others blamed the mystery marks on solar wind.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

In my last post on the subject of AI, values,and socially beneficial simulation, I looked at one way of approaching the problem of AI values, society, and institutions through simulation and modeling. It occurred to me while writing it that there was also another way that might be interesting to think about, and it stems from a rejected piece I wrote last year on the subject of the computer as a "moral mirror." Though that piece was rejected for a reason (it wasn't that well-written), it has come into my mind again over the last week as the Internet has collectively reflected on my Slate article about AI values. I have some commonalities and differences with Stuart Russell's view of AI value alignment. I've looked at them here, here, here, and here.

In this paper, we extend graph-based identification methods by allowing background knowledge in the form of non-zero parameter values. Such information could be obtained, for example, from a previously conducted randomized experiment, from substantive understanding of the domain, or even an identification technique. To incorporate such information systematically, we propose the addition of auxiliary variables to the model, which are constructed so that certain paths will be conveniently cancelled. This cancellation allows the auxiliary variables to help conventional methods of identification (e.g., single-door criterion, instrumental variables, half-trek criterion), as well as model testing (e.g., d-separation, over-identification). Moreover, by iteratively alternating steps of identification and adding auxiliary variables, we can improve the power of existing identification methods via a bootstrapping approach that does not require external knowledge. We operationalize this method for simple instrumental sets (a generalization of instrumental variables) and show that the resulting method is able to identify at least as many models as the most general identification method for linear systems known to date. We further discuss the application of auxiliary variables to the tasks of model testing and z-identification.