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All major UK airports now have or will soon have military grade anti-drone equipment, the government says. It comes after the military were called in to help when drone sightings caused delays for around an hour at Heathrow on Tuesday. And drone sightings at Gatwick caused major disruption affecting 140,000 passengers before Christmas. Earlier, the defence secretary said it would "not be right" to ask the RAF to respond to similar incidents in future. Gavin Williamson said all commercial airports needed to invest in anti-drone technology.

Due to concerns about human error in crowdsourcing, it is standard practice to collect labels for the same data point from multiple internet workers. We here show that the resulting budget can be used more effectively with a flexible worker assignment strategy that asks fewer workers to analyze easy-to-label data and more workers to analyze data that requires extra scrutiny. Our main contribution is to show how the allocations of the number of workers to a task can be computed optimally based on task features alone, without using worker profiles. Our target tasks are delineating cells in microscopy images and analyzing the sentiment toward the 2016 U.S. presidential candidates in tweets. We first propose an algorithm that computes budget-optimized crowd worker allocation (BUOCA). We next train a machine learning system (BUOCA-ML) that predicts an optimal number of crowd workers needed to maximize the accuracy of the labeling. We show that the computed allocation can yield large savings in the crowdsourcing budget (up to 49 percent points) while maintaining labeling accuracy. Finally, we envisage a human-machine system for performing budget-optimized data analysis at a scale beyond the feasibility of crowdsourcing.

Due to the phenomenon of "posterior collapse," current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires augmenting the objective so it does not only maximize the likelihood of the data. In this paper, we propose an alternative that utilizes the most powerful generative models as decoders, whilst optimising the variational lower bound all while ensuring that the latent variables preserve and encode useful information. Our proposed $\delta$-VAEs achieve this by constraining the variational family for the posterior to have a minimum distance to the prior. For sequential latent variable models, our approach resembles the classic representation learning approach of slow feature analysis. We demonstrate the efficacy of our approach at modeling text on LM1B and modeling images: learning representations, improving sample quality, and achieving state of the art log-likelihood on CIFAR-10 and ImageNet $32\times 32$.

Convex clustering is a promising new approach to the classical problem of clustering, combining strong performance in empirical studies with rigorous theoretical foundations. Despite these advantages, convex clustering has not been widely adopted, due to its computationally intensive nature and its lack of compelling visualizations. To address these impediments, we introduce Algorithmic Regularization, an innovative technique for obtaining high-quality estimates of regularization paths using an iterative one-step approximation scheme. We justify our approach with a novel theoretical result, guaranteeing global convergence of the approximate path to the exact solution under easily-checked non-data-dependent assumptions. The application of algorithmic regularization to convex clustering yields the Convex Clustering via Algorithmic Regularization Paths (CARP) algorithm for computing the clustering solution path. On example data sets from genomics and text analysis, CARP delivers over a 100-fold speed-up over existing methods, while attaining a finer approximation grid than standard methods. Furthermore, CARP enables improved visualization of clustering solutions: the fine solution grid returned by CARP can be used to construct a convex clustering-based dendrogram, as well as forming the basis of a dynamic path-wise visualization based on modern web technologies. Our methods are implemented in the open-source R package clustRviz, available at https://github.com/DataSlingers/clustRviz.

Its constant technological evolution characterizes the contemporary world, and every day the processes, once manual, become computerized. Data are stored in the cyberspace, and as a consequence, one must increase the concern with the security of this environment. Cyber-attacks are represented by a growing worldwide scale and are characterized as one of the significant challenges of the century. This article aims to propose a computational system based on intelligent hybrid models, which through fuzzy rules allows the construction of expert systems in cybernetic data attacks, focusing on the SQL Injection attack. The tests were performed with real bases of SQL Injection attacks on government computers, using fuzzy neural networks. According to the results obtained, the feasibility of constructing a system based on fuzzy rules, with the classification accuracy of cybernetic invasions within the margin of the standard deviation (compared to the state-of-the-art model in solving this type of problem) is real. The model helps countries prepare to protect their data networks and information systems, as well as create opportunities for expert systems to automate the identification of attacks in cyberspace.

Cities generate lots of data. The exact amount depends on the size of the city and its sophistication and ambitions, but it's certainly more than mere humans can absorb and use. The Smart Cities movement, which looks for ways to find data-driven technological solutions to everyday urban challenges, is increasingly turning to artificial intelligence to deliver "services" to its residents--everything from locating gunshots and finding tumors to dispatching work crews to pick up trash. New York is one of about 90 cities worldwide that uses a system called ShotSpotter, which uses a network of microphones to instantly recognize and locate gunshots. In Moscow, all chest X-rays taken in hospitals are run through an AI system to recognize and diagnose tumors.

Today is the first day of CES 2019 and artificial intelligence (AI) "will pervade the show," says Gary Shapiro, chief executive of the Consumer Technology Association. One hundred and thirty years ago today (January 8, 1889), Herman Hollerith was granted a patent titled "Art of Compiling Statistics." The patent described a punched card tabulating machine which heralded the fruitful marriage of statistics and computer engineering--called "machine learning" since the late 1950s, and reincarnated today as "deep learning," or more popularly as "artificial intelligence." Commemorating IBM's 100th anniversary in 2011, The Economist wrote: In 1886, Herman Hollerith, a statistician, started a business to rent out the tabulating machines he had originally invented for America's census. Taking a page from train conductors, who then punched holes in tickets to denote passengers' observable traits (e.g., that they were tall, or female) to prevent fraud, he developed a punch card that held a person's data and an electric contraption to read it.

Artificial Intelligence (AI) is nothing new. The field of AI research was founded in 1956. To date, this field has been always covered with huge expectations. Surely, we are in such a hype phase, but Google CEO Sundar Pichai is also right, when he says AI is bigger than the invention of fire and electricity. As one of the largest industrial nations, Germany addresses this big promise and published an AI strategy this month (Nov 2018). But what exactly does that strategy look like and how competitive is it in a global comparison?

Facebook's AI is already able to recognize faces in million of pictures uploaded to the service - and its about to get a lot smarter. Intel has revealed it is working with Facebook on a new artificial intelligence chip to be released in the second half of this year. It will dramatically boost Facebook's ability to tagging friends in photos automatically, the firm hopes. The chips, revealed at the Consumer Electronics Show in Las Vegas, are Intel's gambit to retain hold of a fast-growing segment of the artificial intelligence computing market. It puts the pair into direct competition with similar chips from Nvidia Corp and Amazon.com