The internet of things (IoT) promises many advantages - smart cities with integrated transport systems, for instance - but it comes with a significantly increased cybersecurity risk. So how should we be tackling this new threat? Christoph Brandstatter is managing director of the four-star Seehotel, Jagerwirt, in Austria's Alps. His hotel's electronic door locks and other systems were hacked for ransom four times, between December 2016 and January 2017. "We got a ransomware mail which was hidden in a bill from Telekom Austria," says Mr Brandstatter.

The same machine learning approaches that Google is bringing to many of its enterprise technologies and products recently helped scientists at NASA discover two new planets far outside the solar system. One of the planets, Kepler-90i, is the eighth planet that NASA has discovered orbiting Kepler 90, a star that is very similar to the Sun some 2,545 light-years away from Earth. The discovery means that the solar system is not the only planetary system with eight planets circling a star, NASA said in a statement Thursday. Kepler-90i is about 30 percent larger than earth has a surface temperature of around 800 F and orbits its star once ever 14 days. Finding exoplanets can be enormously challenging even with the most sophisticated technologies.

Our solar system now is tied for most number of planets around a single star, with the recent discovery of an eighth planet circling Kepler-90, a Sun-like star 2,545 light years from Earth. The planet was discovered in data from NASA's Kepler Space Telescope.

As Feds get smarter about Artificial Intelligence on the cyber frontier, seems agencies' IT defenders are suffering from schizophrenia about cyber cyborgs. Where 90 percent of cyber folks swoon about AI as the fix for the cyber sieve, almost half of Feds suffer AI anxiety disorder. With the exponential increase in cyber attacks and insider-threat nightmares, now's a fascinating time to consider AI's role in cybersecurity. We see Kevin Cox and the CDM program office exploring AI–and every cyber vendor's touting its new AI pixie dust. Clearly Feds recognize the challenges associated with Uncle Sam's thin-blue line on the cyber frontier.

"The science of AI has no borders, neither do its benefits," Fei-Fei Li, chief scientist at Google's AI business, said in a blog post Wednesday announcing the new center. But China's internet borders are fortified by the so-called Great Firewall, and most of Google's biggest products -- its search engine, YouTube and Gmail -- have been blocked by the country's vast censorship apparatus for years. Google (GOOGL) effectively left China in 2010, but the country's 730 million internet users make it too large a market to ignore. The company has made no secret of its desire to find ways to rebuild its presence there. Related: Google's man-versus-machine showdown blocked in China Its artificial intelligence unit DeepMind teamed up with Chinese authorities to hold a five-day festival in the country earlier this year.

A solar system with as many planets as our own has been discovered with the help of NASA's Kepler space telescope and artificial intelligence, the US space agency said. "Our solar system now is tied for most number of planets around a single star," NASA said in a statement yesterday. However, none of the planets are expected to be hospitable to life. The eight-planet system -- the largest known outside of ours -- orbits a star called Kepler 90 some 2,545 light-years away. "The Kepler-90 star system is like a mini version of our solar system," said Andrew Vanderburg, an astronomer at the University of Texas at Austin.

An eighth planet orbiting a Sun-like star over 2,500 light years away called Kepler-90 has been detected by running the data from NASA's Kepler Space Telescope through a Google neural network. The network was trained using 15,000 previously vetted signals from the Kepler exoplanet catalogue, NASA explained, before it moved on to learning how to detect weaker signals. "We got lots of false positives of planets, but also potentially more real planets," said NASA Sagan postdoctoral fellow Andrew Vanderburg. "It's like sifting through rocks to find jewels. If you have a finer sieve, then you will catch more rocks but you might catch more jewels, as well."

Recent progress in machine learning has enabled deep neural networks (DNNs) to advance the state of the art in a wide range of problem domains, from computer vision to high energy physics [3] [4]. As the applicability of DNNs has broadened, there have been efforts to develop userfriendly tools for building them. Software packages such as Keras [5] and TFLearn [6] facilitate the construction and training of deep neural networks, offering a flexible interface for combining common model components and configuring the optimization process. Large model sizes and long training times have motivated the development of distributed training algorithms for DNNs [7] [8]. These algorithms work by splitting the training task across multiple concurrent processes, which can be threads on a single machine or jobs spread across the nodes of a cluster. The speedup provided by distributed algorithms is relevant when fast training is critical, such as when iterating on model choice during development, or when retraining a model on new data in a production environment. Despite the rise of convenient model-building software packages such as Keras, there are few tools for interfacing these packages with distributed training algorithms. In this paper we introduce a lightweight Python framework, mpi learn, that provides a straightforward means of training Keras models in a distributed fashion. The framework is built on the Message Processing Interface (MPI) protocol [10] and can operate on personal machines, multi-GPU servers, and large supercomputing sites alike.

Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time. Our models decompose anomaly scores into the contributions of individual user behavior features for increased interpretability to aid analysts reviewing potential cases of insider threat. Using the CERT Insider Threat Dataset v6.2 and threat detection recall as our performance metric, our novel deep and recurrent neural network models outperform Principal Component Analysis, Support Vector Machine and Isolation Forest based anomaly detection baselines. For our best model, the events labeled as insider threat activity in our dataset had an average anomaly score in the 95.53 percentile, demonstrating our approach's potential to greatly reduce analyst workloads.

WASHINGTON – A recreational drone operator was at fault in the first confirmed midair collision in the U.S. between a drone and a manned aircraft, the National Transportation Safety Board said Thursday The operator was unaware the Federal Aviation Administration had temporarily banned drone flights in New York when his small drone collided with an Army Blackhawk helicopter on Sept. 21, the board said in a report on the incident. The U.N. General Assembly was meeting in New York at the time. The helicopter suffered minor damage while the DJI Phantom 4 drone was destroyed, the report said. The operator flew the drone 2.5 miles away despite a long-standing FAA prohibition on drone flights beyond the sight of an operator, the report said. The operator saw the helicopter on the tablet he was using to direct the drone and tried to move the drone out of the way, but it was too late to avoid the collision, the report said.