For the past decade, cloud has been all the rage in federal IT circles, as agencies look for ways to decrease the burden of legacy IT spending. Today, the IT modernization push continues, and agencies can now see the light at the end of the tunnel. So far in 2017, there have been positive signs from Congress and the White House that IT modernization will remain a critical part of our government's priorities moving forward. Earlier this year, President Donald Trump signed two executive orders -- one on IT modernization and one on cybersecurity -- that signaled his administration would be taking the push seriously. Similarly, the House of Representatives approved the Modernizing Government Technology Act, commonly referred to as the MGT Act, to set aside funding for federal agencies to upgrade to new platforms, such as cloud, to drastically overall its IT infrastructure.

Is machine learning a must-have for security analytics or is it window dressing that is irrelevant to a security manager's purchasing decision? The answer, much like the outputs derived through machine learning algorithms, is neither black nor white. The promise of machine learning in cybersecurity lies in its ability to detect as-yet-unknown threats, particularly those that may lurk in networks for long periods of time seeking their ultimate goals. Machine learning technology does this by distinguishing atypical from typical behavior, while noting and correlating a great number of simultaneous events and data points. But in order to know what constitutes typical activity on a website, endpoint or network at any given time, the machine learning algorithms must be trained on large volumes of data that have already been properly labelled, identified or categorized with distinguishing features that can be assigned and reassigned relative weights.

SAN FRANCISCO – Researchers have discovered a disturbing pattern: dozens of ships whose GPS signals tell them they're on land -- at an airport no less -- even when they're far out to sea. An investigation released this week by the Washington D.C.-based Resilient Navigation and Timing Foundation and Windward Ltd., a maritime data and analytics company, has found multiple instances of so-called GPS spoofing in Russian waters. As recently as Monday, two vessels' GPS told them they were at Sochi Airport near the site of the 2014 Sochi Olympics, 12 miles away from the harbor where the vessels actually were. Researchers are calling these "mass GPS interferences" and they appear to be linked to the intentional transmission of false GPS signals to provide incorrect time or location information, possibly to veil certain facilities from attack. Familiar to anyone using a smartphone or built-in auto navigation system to map out a route, the satellite-based system is also the main way ships and trucking fleets find their way.

China is developing a new range of killer cruise missiles fitted with technology which will effectively turn them into killer robots. Dubbed "death drones", the missiles will use artificial intelligence (AI) to guide themselves in flight and potentially even choose new targets. Wang Changqing, director of the General Design Department of the Third Academy of the China Aerospace Science and Industry Corp, told China Daily that his country was leading the world in the development of AI weapons.

Artificial intelligence (AI) is recognised to be the future of mankind, experts say, and China is said to be in the lead in years to come, by 2030, neck and neck with the US and Russia, according to Bloomberg. China has vowed to become a world leader in artificial intelligence by 2030 in full force, in every layer of education system. China plans to pump up to a billion to win this arms race. On 20 July, China had released a "national AI development plan" which committed it to spending $29.8 billion on AI research by 2020 and $79.48 billion by 2025. The "national AI development plan" consists of a step-by-step policy outlined, with a pledge to become the world best AI leader, aiming an industrial scale yield of RMB1 trillion (about S$206 billion).

The company announced it has received €30 million ($36 million) funding from the European Investment Bank, the lending arm of the European Union. "We're proud to receive this stamp of approval from the EIB. It's the type of offer you can't refuse and it will allow us to further accelerate our growth and continue to level the playing field for small businesses, giving them access to tools to take on the big corporations," said Jacob de Geer, CEO and co-founder of iZettle, in a statement. The funding follows the startup's most recent round, which was earlier this year, when it raised $63 million at the same $500 million valuation it had in its last equity round. It appears the valuation is staying the same with this latest round: the money, as with earlier this year, is coming in the form of debt funding and will be distributed over the next three years, the company said.

World's largest bank, the State Bank of India (SBI), has deployed a new artificial intelligence (AI) based chatbot to help its customers that include addressing customer queries and help them find right kind of services from the bank. The AI solution, named SBI Intelligent Assistant (SIA), is deployed by Payjo that also offers similar solutions to many other international banks including Yes Bank. The SIA, Payjo said addresses customer enquiries instantly and helps them with everyday banking tasks just like a bank representative. "With SIA, SBI will reduce significant operational expenditure over time," Payjo said in a statement. The SIA is setup, the company said, to handle nearly 10,000 enquiries per second or 864 Million in a day. "That is nearly 25% of the queries processed by Google every day," it added.

Worldwide spending on artificial intelligence and big data will reach the tens of billions by 2025. Michael Finnigan finds out what family-run operations need to know about the rise of the robots. The county of Wiltshire in the United Kingdom might seem like an unlikely setting for one of the world's most advanced artificial intelligence (AI) laboratories. It is best-known for its Neolithic monuments and iconic stone circles, most famously Stonehenge, but beneath its prehistoric landscape the future is unfolding. At family-run technology design firm Dyson, a team of engineers are using artificial intelligence to get a leg up on the competition.

This paper presents a technique for reduced-order Markov modeling for compact representation of time-series data. In this work, symbolic dynamics-based tools have been used to infer an approximate generative Markov model. The time-series data are first symbolized by partitioning the continuous measurement space of the signal and then, the discrete sequential data are modeled using symbolic dynamics. In the proposed approach, the size of temporal memory of the symbol sequence is estimated from spectral properties of the resulting stochastic matrix corresponding to a first-order Markov model of the symbol sequence. Then, hierarchical clustering is used to represent the states of the corresponding full-state Markov model to construct a reduced-order or size Markov model with a non-deterministic algebraic structure. Subsequently, the parameters of the reduced-order Markov model are identified from the original model by making use of a Bayesian inference rule. The final model is selected using information-theoretic criteria. The proposed concept is elucidated and validated on two different data sets as examples. The first example analyzes a set of pressure data from a swirl-stabilized combustor, where controlled protocols are used to induce flame instabilities. Variations in the complexity of the derived Markov model represent how the system operating condition changes from a stable to an unstable combustion regime. In the second example, the data set is taken from NASA's data repository for prognostics of bearings on rotating shafts. We show that, even with a very small state-space, the reduced-order models are able to achieve comparable performance and that the proposed approach provides flexibility in the selection of a final model for representation and learning.

Video-game voice actors have agreed to end a nearly yearlong strike against several major gaming publishers. The actors union SAG-AFTRA and a representative for the publishers said Monday that they reached a tentative agreement on Saturday to end the strike. It calls for actors who work multiple sessions on games to receive additional payments and contains a requirement that companies disclose to actors what game they will be working on. The actors began a strike against several video game companies, including Activision Productions Inc., Electronic Arts Productions Inc., Take 2 Productions Inc. and WB Games. The work stoppage focused on payments to the actors, as well as complaints that actors were not being told which projects they were being considered for until after they were hired.