Neither love nor money could get reporters in to the Honor press conference at CES 2017 in Las Vegas January – unless they had preregistered. Keynote addresses at the world's biggest consumer electronics show routinely attract a full house, but in the country that gave the world Apple, the international press corps seemed fixated on the touted "epic" capabilities of the Honor Magic, the latest model in the budget smartphone brand by Chinese manufacturer Huawei. There are almost as many mobile phone subscribers in the world as there are people – 5 billion devices, for a population of 7.5 billion, according to the 2017 global edition of the GSMA's Mobile Economy report. But the fact that a new launch can elicit so much hype is evidence that we still can't get enough. Huawei's Honor Magic might be the first, but surely by no means the last, artificial intelligence (AI)-enabled smartphone.

For investors in Boo.com, WebVan and eToys, the bursting of the dotcom bubble came as a bit of a shock. Companies like this raised vast sums on the promise that the worldwide web would change everything. Then, in the spring of 2000, stock markets collapsed. Some economists had long been sceptical about the promise of computers. In 1987, we didn't have the web, but spreadsheets and databases were appearing in every workplace - and having, it seemed, no impact whatsoever.

BERLIN: Scientists have developed a new artificial intelligence system that can decode brain signals, an advance that may help severely paralysed patients communicate with their thoughts. Artificial intelligence has far outpaced human intelligence in certain tasks. Researchers from University Hospital Freiburg in Germany led by neuroscientist Tonio Ball showed how a self-learning algorithm decodes human brain signals that were measured by an electroencephalogram (EEG). It included performed movements, but also hand and foot movements that were merely thought of, or an imaginary rotation of objects. The system could be used for early detection of epileptic seizures, communicating with severely paralysed patients or make automatic neurological diagnosis.

Are you currently enrolled in either of my Core or Intermediate Spatial Data Analysis Courses? Or perhaps you have prior experience in GIS or tools like R and QGIS? You don't want to spend 100s and 1000s of dollars on buying commercial software for imagery analysis? The next step for you is to gain profIciency in satellite remote sensing data analysis. MY COURSE IS A HANDS ON TRAINING WITH REAL REMOTE SENSING DATA WITH OPEN SOURCE TOOLS!

The estimation of covariance matrices of gene expressions has many applications in cancer systems biology. Many gene expression studies, however, are hampered by low sample size and it has therefore become popular to increase sample size by collecting gene expression data across studies. Motivated by the traditional meta-analysis using random effects models, we present a hierarchical random covariance model and use it for the meta-analysis of gene correlation networks across 11 large-scale gene expression studies of diffuse large B-cell lymphoma (DLBCL). We suggest to use a maximum likelihood estimator for the underlying common covariance matrix and introduce an EM algorithm for estimation. By simulation experiments comparing the estimated covariance matrices by cophenetic correlation and Kullback-Leibler divergence the suggested estimator showed to perform better or not worse than a simple pooled estimator. In a posthoc analysis of the estimated common covariance matrix for the DLBCL data we were able to identify novel biologically meaningful gene correlation networks with eigengenes of prognostic value. In conclusion, the method seems to provide a generally applicable framework for meta-analysis, when multiple features are measured and believed to share a common covariance matrix obscured by study dependent noise.

We consider the problem of robustifying high-dimensional structured estimation. Robust techniques are key in real-world applications which often involve outliers and data corruption. We focus on trimmed versions of structurally regularized M-estimators in the high-dimensional setting, including the popular Least Trimmed Squares estimator, as well as analogous estimators for generalized linear models and graphical models, using possibly non-convex loss functions. We present a general analysis of their statistical convergence rates and consistency, and then take a closer look at the trimmed versions of the Lasso and Graphical Lasso estimators as special cases. On the optimization side, we show how to extend algorithms for M-estimators to fit trimmed variants and provide guarantees on their numerical convergence. The generality and competitive performance of high-dimensional trimmed estimators are illustrated numerically on both simulated and real-world genomics data.

This paper introduces a new probabilistic architecture called Sum-Product Graphical Model (SPGM). SPGMs combine traits from Sum-Product Networks (SPNs) and Graphical Models (GMs): Like SPNs, SPGMs always enable tractable inference using a class of models that incorporate context specific independence. Like GMs, SPGMs provide a high-level model interpretation in terms of conditional independence assumptions and corresponding factorizations. Thus, the new architecture represents a class of probability distributions that combines, for the first time, the semantics of graphical models with the evaluation efficiency of SPNs. We also propose a novel algorithm for learning both the structure and the parameters of SPGMs. A comparative empirical evaluation demonstrates competitive performances of our approach in density estimation.

Children as young as two should be introduced to the basics of coding, according to one of Britain's most eminent computing pioneers. Dame Stephanie Shirley, whose company was one of the first to sell software in the 1960s, said that engaging very young children – in particular girls – could ignite a passion for puzzles and problem-solving long before the "male geek" stereotype took hold. "I don't think you can start too early," she said, adding that evidence suggested that the best time to introduce children to simple coding activities was between the ages of two and seven years. "Most successful later coders start between five and six," she added. "In a sense, those years are the best for learning anything … and means that programming [hasn't] become set in your mind as geeky or nerdy."

As expected, Microsoft just divulged Xbox One X preorder details during a livestream prelude to the annual Gamescom show in Cologne, Germany. The $499 super-powered Xbox One games console, capable of running games at 4K native resolution, launches worldwide on November 7. The version you can preorder now is called the Xbox One X Project Scorpio Edition. It's still $499 with a 1 terabyte hard drive, but Microsoft describe this as a limited edition console with a custom design: the words "Project Scorpio" (the system's pre-unveiling codename) emblazoned on the console and included gamepad, as well as "sophisticated and dynamic graphic pattern" across the system exterior. "Get Xbox One X Project Scorpio Edition before it's gone forever," reads the marketing teaser--there'll be an unmarked $499 version at some point down the road, in other words. But Microsoft says the limited edition is only available until supplies run out.

This video, with the help of practical projects, highlights how TensorFlow can be used in different scenarios--this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with tensors. Simply pick a project in line with your environment and get stacks of information on how to implement TensorFlow in production. Rodolfo Bonnin is a Systems Engineer and PhD student at Universidad Tecnológica Nacional, Argentina. He also pursued parallel programming and image understanding postgraduate courses at Uni Stuttgart, Germany.