What do you think of when you hear the phrases "Future Tech" or "Science Fiction Technology?" Humanoid robots walking the streets? Today we're looking at science fiction #technology that was once just a fantasy, that is now part of our daily life. We are also going to take a peek at some of our favorite sci-fi tech, and see how close it is to being a reality. "Individual science fiction stories may seem as trivial as ever to the blinder critics and philosophers of today - but the core of science fiction, its essence, the concept around which it revolves, has become crucial to our salvation if we are to be saved at all." ("My Own View," The Encyclopedia of Science Fiction) Believe it or not, credit cards were first mentioned in science fiction. You might expect that the individual who envisioned the credit card to be a genius businessman or bank executive of some sort, however the person who first developed the idea of the modern credit card system was a Utopian science fiction author Edward Bellamy.

Many complex systems can be represented as networks, and the problem of network comparison is becoming increasingly relevant. There are many techniques for network comparison, from simply comparing network summary statistics to sophisticated but computationally costly alignment-based approaches. Yet it remains challenging to accurately cluster networks that are of a different size and density, but hypothesized to be structurally similar. In this paper, we address this problem by introducing a new network comparison methodology that is aimed at identifying common organizational principles in networks. The methodology is simple, intuitive and applicable in a wide variety of settings ranging from the functional classification of proteins to tracking the evolution of a world trade network.

In this paper, we consider stochastic dual coordinate (SDCA) {\em without} strongly convex assumption or convex assumption. We show that SDCA converges linearly under mild conditions termed restricted strong convexity. This covers a wide array of popular statistical models including Lasso, group Lasso, and logistic regression with $\ell_1$ regularization, corrected Lasso and linear regression with SCAD regularizer. This significantly improves previous convergence results on SDCA for problems that are not strongly convex. As a by product, we derive a dual free form of SDCA that can handle general regularization term, which is of interest by itself.

Online knowledge repositories typically rely on their users or dedicated editors to evaluate the reliability of their content. These evaluations can be viewed as noisy measurements of both information reliability and information source trustworthiness. Can we leverage these noisy evaluations, often biased, to distill a robust, unbiased and interpretable measure of both notions? In this paper, we argue that the temporal traces left by these noisy evaluations give cues on the reliability of the information and the trustworthiness of the sources. Then, we propose a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness. Furthermore, we develop an efficient convex optimization procedure to learn the parameters of the model from historical traces. Experiments on real-world data gathered from Wikipedia and Stack Overflow show that our modeling framework accurately predicts evaluation events, provides an interpretable measure of information reliability and source trustworthiness, and yields interesting insights about real-world events.

Above the Trend Line: machine learning industry rumor central, is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz. Our intent is to provide our readers a one-stop source of late-breaking news to help keep you abreast of this fast-paced ecosystem. We're working hard on your behalf with our extensive vendor network to give you all the latest happenings. Be sure to Tweet Above the Trend Line articles using the hashtag: #abovethetrendline.

Arithmetically, the problem is a combination of collapsing productivity and insufficient capital investment. On February 19, 2017, the New York Times ran a feature story on recent changes in the United States oil industry.2 The focus was on the recent "embrace" of technological innovation in the industry after the 2014 plunge in the global oil market. This was just one of a rash of such pieces in the popular press, relying, as is typical of such writing, on a smattering of skewed, decontextualized data, a healthy serving of the anecdotal, and a host of the worst tech journalism clichés ("a few icons on a computer screen," "a click of the mouse," video game marathons as job training, a compulsory reference to drones). Zeroing in on the effects of these changes on workers in west Texas, the article's upshot is unobjectionable enough: as oil prices recover, output rises, and production becomes more capital-intensive, many workers who lost jobs in the downturn will be replaced by machines. These workers, often Latino, are sure to be forced out of these semi-skilled, relatively well-paid jobs into other sectors of the labor market, where their skills and experience will serve little purpose. At first blush, the situation seems dire. We are told that some 30% of jobs in the industry were lost after the oil market crash of mid-2014, when employment in the industry was at its peak.

SAP Labs in India is the second largest R&D centre for the company after its centre in Walldorf, Germany and among the three hubs in the SAP Labs network of 19 Labs across 16 countries. Dilipkumar Khandelwal, MD for SAP Labs in India, has a dual role as he is also the EVP and Global Head of Enterprise Cloud Services for SAP. In an exclusive interview with Ayushman Baruah, Khandelwal talks about their India focus, latest technologies, and their emphasis on innovation. Excerpts: What is the SAP Lab's focus here? Over a period of 19 years, SAP Labs India has evolved to become an integral part of SAP's global strategy.

If you're a software engineer (or someone who's learning the craft), chances are that you've heard about deep learning (which we'll sometimes abbreviate as "DL"). It's an interesting and rapidly developing field of research that's now being used in industry to address a wide range of problems, from image classification and handwriting recognition, to machine translation and, infamously, beating the world champion Go player in four games out of five. A lot of people think you need a PhD or tons of experience to get a job in deep learning, but if you're already a decent engineer, you can pick up the requisite skills and techniques pretty quickly. Important point: You need motivation and the ability to code and problem solve well. Here at Deepgram we're using deep learning to tackle the problem of speech search.

We show that a simple modification of the 1-nearest neighbor classifier yields a strongly Bayes consistent learner. Prior to this work, the only strongly Bayes consistent proximity-based method was the k-nearest neighbor classifier, for k growing appropriately with sample size. We will argue that a margin-regularized 1-NN enjoys considerable statistical and algorithmic advantages over the k-NN classifier. These include user-friendly finite-sample error bounds, as well as time- and memory-efficient learning and test-point evaluation algorithms with a principled speed-accuracy tradeoff. Encouraging empirical results are reported.

There has been substantial interest in building technologies that can use social media postings to help forecast civil unrest [1]-[3]. The Arab Spring of 2011 compellingly illustrates how social media can both reflect and influence political (in)stability [4]. Since social media data is generated on such a large and rapid scale, computational tools are potentially extremely useful in helping to render meaning from that data. While previous work has focused on forecasting specific near-term unrest events [2], in this current paper we are interested in filtering social media content for postings that are relevant to social unrest, with the idea that downstream systems or human experts would use this filtered content for further analysis. In particular, we experiment with filtering tweets written in Arabic for relevance to social unrest.