"A combat helicopter successfully intercepted an Iranian UAV that was launched from Syria and infiltrated Israel. The aircraft was identified by the Aerial Defense Systems at an early phase and was under surveillance until the interception," the military said in a statement.

"A combat helicopter successfully intercepted an Iranian UAV that was launched from Syria and infiltrated Israel. The aircraft was identified by the Aerial Defense Systems at an early phase and was under surveillance until the interception," the military said in a statement.

Linear classification has been widely used in many high-dimensional applications like text classification. To perform linear classification for large-scale tasks, we often need to design distributed learning methods on a cluster of multiple machines. In this paper, we propose a new distributed learning method, called feature-distributed stochastic variance reduced gradient (FD-SVRG) for high-dimensional linear classification. Unlike most existing distributed learning methods which are instance-distributed, FD-SVRG is feature-distributed. FD-SVRG has lower communication cost than other instance-distributed methods when the data dimensionality is larger than the number of data instances. Experimental results on real data demonstrate that FD-SVRG can outperform other state-of-the-art distributed methods for high-dimensional linear classification in terms of both communication cost and wall-clock time, when the dimensionality is larger than the number of instances in training data.

We consider a novel stochastic multi-armed bandit problem called {\em good arm identification} (GAI), where a good arm is defined as an arm with expected reward greater than or equal to a given threshold. GAI is a pure-exploration problem that a single agent repeats a process of outputting an arm as soon as it is identified as a good one before confirming the other arms are actually not good. The objective of GAI is to minimize the number of samples for each process. We find that GAI faces a new kind of dilemma, the {\em exploration-exploitation dilemma of confidence}, which is different difficulty from the best arm identification. As a result, an efficient design of algorithms for GAI is quite different from that for the best arm identification. We derive a lower bound on the sample complexity of GAI that is tight up to the logarithmic factor $\mathrm{O}(\log \frac{1}{\delta})$ for acceptance error rate $\delta$. We also develop an algorithm whose sample complexity almost matches the lower bound. We also confirm experimentally that our proposed algorithm outperforms naive algorithms in synthetic settings based on a conventional bandit problem and clinical trial researches for rheumatoid arthritis.

Bangalore: In a bid to empower the business leader with the decision-making process and also democratise data science, Lymbyc Solutions has launchesd'Lymbyc'- world's first virtual data scientist. It takes inspiration from the limbic brain that helps guide humans on'why' we do things. Lymbyc is a self-service, predictive, insights platform that is driven by an adaptive machine learning engine. It helps improve decision-making in organizations by curating embedded intelligence across all data sources, while tackling enormous amounts of data, organizational silos and information complexity. Data Science is a growing market and Lymbyc is all set to grab its market share.

An artificial intelligence program developed by Weill Cornell Medicine and NewYork-Presbyterian researchers can distinguish types of cancer from images of cells with almost 100 percent accuracy, according to a new study. This new technology has the potential to augment cancer diagnosis techniques that currently require the human eye. Currently, cancer is diagnosed by visual examination of tissue samples under a microscope. Pathologists consider variables like cell shape, number, mass and appearance when determining whether tissue appears malignant or benign. While accurate analysis is critical to making the right diagnosis, the process can become complicated. "The diversity among cancer cells is very high," said co-senior author Dr. Olivier Elemento, director of the Caryl and Israel Englander Institute for Precision Medicine at Weill Cornell Medicine, who also leads joint precision medicine efforts at Weill Cornell Medicine and NewYork-Presbyterian/Weill Cornell Medical Center.

Sören Schwertfeger finished his postdoctorate research on autonomous robots in Germany, and seemed set to go to Europe or the United States, where artificial intelligence was pioneered and established. Instead, he went to China. "You couldn't have started a lab like mine elsewhere," Mr. Schwertfeger said. The balance of power in technology is shifting. China, which for years watched enviously as the West invented the software and the chips powering today's digital age, has become a major player in artificial intelligence, what some think may be the most important technology of the future.

Relations between Washington and Islamabad have frayed in recent months after Trump's angry tweet on Jan. 1 about Pakistan's "lies and deceit" over its alleged support for the Afghan Taliban and their allies. Last month, the United States suspended about $2 billion assistance to Islamabad.

Ehang's CEO, Hu Huazhi, says these passenger drones will first focus on carrying wealthy customers to establish a customer base (making it much like Elon Musk's strategy for Tesla). Then, ideally, economies of scale would democratize passenger drones, so everyone else can take to the sky. That, at least, is the futuristic vision. Peter Warren Singer is a strategist and senior fellow at the New America Foundation. He has been named by Defense News as one of the 100 most influential people in defense issues.

Software that powers facial recognition generally uses a two-step process, says David Alexander Forsyth, an artificial intelligence expert and chair of computer science at the University of Illinois at Urbana-Champaign. Step one is to figure out where the faces are in the image in question; the system is looking for a window-like section of the image that also has someone's countenance in it, and not the other stuff of modern life, like stop signs and cars. Step two: it needs to see if it can match the face to any in its database. "Turns out, that's a harder problem," Forsyth says, in comparison to step one. "People tend to look like each other."