Symantec has launched Endpoint Protection 14, a new security solution which harnesses artificial intelligence to protect clients. Announced on November 1, the new security offering is powered by AI and machine learning on the endpoint and in the cloud. Symantec says that by harnessing machine learning to collate data and detect patterns and anomalies which may indicate a cyberattack, AI provides "a multi-layered solution able to stop advanced threats and respond at the endpoint regardless of how the attack is launched." Symantec Endpoint Protection combines machine learning, memory exploit mitigation, and threat intelligence provided by Symantec and Blue Coat, which combined their research and security operations in October after Symantec completed the acquisition of Blue Coat for $4.6 billion. The company also says that the solution is capable of 99.9 percent efficacy, low false positives, and a 70 percent carbon footprint reduction in comparison to past endpoint software.

SAP acquired procurement software vendor Ariba in 2011, but the company's history dates back two decades. This week, SAP Ariba executives briefed analysts in Boston, giving an overview of recent roadmap milestones as well as a look ahead at what's yet to come. Growth markers: There are now 2.4 million suppliers on Ariba's business network, with more than $1 trillion in commerce transactions each year. In addition, Ariba has a presence in 190 countries. Yet Ariba has set some lofty goals for additional growth.

Anthony Foxx waits for the countdown, then hits the plunger. The catapult releases its bungee cord, slinging the drone from to a standstill to 50 mph in half a second. The drone spins up its twin propellers and flies a few hundred feet up, circling overhead. "That's amazing," Foxx says, as the UAV drops its package within a few feet of the practice delivery zone, then belly flops onto a brown landing pad that resembles the base of a jumping castle. During the closing months of his four-year run as US Secretary of Transportation, Foxx has come to California on a fact finding mission.

Hidden Blade, a CASC offering, is a 60mm caliber, 4kg antiair/Anti-armor portable missile. It has a reported range of 2km against aerial targets like helicopters and UAVs, and a 3km against ground targets. Its dual use 500 g shaped warhead creates a high speed metal slug that can punch through walls, or the light armor of AFVs and attack helicopters. Hidden Blade uses a rather novel targeting system; the launcher emits electromagnetic energy to'paint' the target, so the missile's electro-optical sensor can lock onto it. While the use of a wide spectrum of electromagnetic energy renders the missile highly resistant to laser and infrared jammers, the target must be out in the open to avoid defusing the electromagnetic energy against background clutter. Of note, this tiny missile would enable a basic infantry squad to gain organic firepower against 21st century threats like drones, as well as enemy fortifications.

We present a methodology for clustering N objects which are described by multivariate time series, i.e. several sequences of real-valued random variables. This clustering methodology leverages copulas which are distributions encoding the dependence structure between several random variables. To take fully into account the dependence information while clustering, we need a distance between copulas. In this work, we compare renowned distances between distributions: the Fisher-Rao geodesic distance, related divergences and optimal transport, and discuss their advantages and disadvantages. Applications of such methodology can be found in the clustering of financial assets. A tutorial, experiments and implementation for reproducible research can be found at www.datagrapple.com/Tech.

It is widely believed that the prediction accuracy of decision tree models is invariant under any strictly monotone transformation of the individual predictor variables. However, this statement may be false when predicting new observations with values that were not seen in the training-set and are close to the location of the split point of a tree rule. The sensitivity of the prediction error to the split point interpolation is high when the split point of the tree is estimated based on very few observations, reaching 9% misclassification error when only 10 observations are used for constructing a split, and shrinking to 1% when relying on 100 observations. This study compares the performance of alternative methods for split point interpolation and concludes that the best choice is taking the mid-point between the two closest points to the split point of the tree. Furthermore, if the (continuous) distribution of the predictor variable is known, then using its probability integral for transforming the variable ("quantile transformation") will reduce the model's interpolation error by up to about a half on average. Accordingly, this study provides guidelines for both developers and users of decision tree models (including bagging and random forest).

Behavioural change is a very tricky thing. We humans are so fickle. We see a bright shiny wearable device that can track our every move and we think it's our "silver bullet", a "ticket" to achieving our health and fitness dreams. Only for guilt to set in, as after a short time, the wearable device winds up in our top drawer. We knew the fitness data was great, but we really didn't know what to do with it. The truth is, behaviour change requires much more than data. Many programs have realized the magnitude of the problem and created incentive programs to reward people for being active, so they get a small pay-off on the road to achieving fitness. But in spite of these rewards, the drop-out rate remains problematic.

Hedge funds have been in the doldrums and face mounting pressure to justify their fees. Will artificial intelligence come to the rescue? A growing number of hedge funds are putting money behind the idea that a branch of AI called machine learning could provide a way to get back on top. A software program that searches for patterns in more data than even the most sleep-deprived junior analyst could examine, and then tests its hypotheses against even more data. What can satellite shots of mall parking lots tell you when combined with in-store sales data?

Artificial intelligence and robotics are coming into our lives more than ever before and have the potential to transform healthcare, transport, manufacturing, even our domestic chores. Mary "Missy" Cummings, Director of the Humans and Autonomy Lab (HAL) at Duke University, and co-chair of the Global Future Council on Artificial Intelligence and Robotics, says the technology will work best in collaboration with humans. While cab drivers may fear for their jobs, she envisages a worldwide shortage of roboticists in 2030. Artificial intelligence and robotics are showing up in every part of life, anywhere from driving, to the cellphones we use, how our data is managed in the world, how our homes are going to be built in the future. So given its ubiquity, it really is important to start addressing the strengths and limitations of artificial intelligence.

Under, behind and inside many of the apps we use every day, a revolution is underway. It's a revolution that started decades ago but today is empowering companies to deliver better, smarter services with greater ease and on broader scales than ever before. At Singularity University's inaugural Global Summit, Neil Jacobstein, chair of Artificial Intelligence and Robotics, provided a primer showing how artificial intelligence literally transforms everything it touches. First of all, it's critical to define the scope of artificial intelligence (AI), which can be categorized into four areas: techniques in pattern recognition, software agency (that is, software that acts like real users), an exponential technology that is accelerating other exponential technologies, and a vision of a future superhuman intelligence (that fortunately hasn't happened yet). Anyone who has seen a science fiction film is likely familiar with this last area, but it's the other three areas where AI is making huge strides at a revolutionary pace.