A fearless Frenchman, Franky Zapata, thinks one day people will be able to ride his hoverboard to pick up bread in the morning (it's a French thing). The jet ski champion on Saturday set a new Guinness World Record for the farthest hoverboard flight – yes, just like in the movies – off the coast of Sausset-les-Pins in the south of France. Mr. Zapata rode the 1,000 horsepower drone, standing on top of it, for 7,388 feet, or more than a mile. He hovered 165 feet above the surface of the water, "trailed by a fleet of boats and jet skis," as Guinness reports. His feat shattered the previous hoverboard travel record of 905 feet and 2 inches, set last year by Canadian inventor Catalin Alexandru Duru.

Artificial intelligence is a tool that allows technology to go a step further. Throughout the history of Artificial Intelligence, there have been several subfields developed as methods for problem solving. One of them is Deep Learning. Today we will be delving into TensorFlow. TensorFlow is an open source python library that uses deep learning and graph computation.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

During the last couple of weeks, I spent some time in San Francisco and Napa Valley. Next week, I will revert to the Napa Valley trip, but this Monday, I will focus on the visit to the world's technology centre and the start up environment there. Now, clearly it is a relatively new focus area for me, as you all know and therefore, I am still easily impressed. But, boy, when you are used to the toxic and money-starved startup environment in Europe, and you then experience the amazing energy and huge money floating around in Silicon Valley, it is really hard not to be impressed. Or rather, it is hard not to reconsider your priorities, and this is exactly what I intend to do.

AILA, or Artificial Intelligence Lightweight Android, prepares to press switches on a panel it recognizes during a demonstration at the German Research Center for Artificial Intelligence GmbH (Deutsches Forschungszentrum fuer Kuenstliche Intelligenz GmbH) stand at the 2013 CeBIT technology trade fair on March 5, 2013 in Hanover, Germany. Artificial intelligence (AI) in fictional films is a thing of the past. In fact, the ever-evolving field of AI has become ubiquitous, making big strides in technological innovations. But as artificial intelligence revolutionizes healthcare, education, businesses and becomes a "meta-solution" to the world's biggest problems, its ubiquity spawns one major question: Will future AI wipe out humanity? Artificial intelligence has a more useful presence in humanity today.

From safety issues to technical problems, there are many issues that need to be addressed before self-driving cars can hit the roads. But one, possibly unexpected, consequence of the autonomous cars is that they could give sex lives a boost. A Canadian expert believes people will have'a lot more sex in cars', once a computer takes over and this could be dangerous as the'drivers' won't be paying attention to the road. From safety issues to technical problems, there are many issues that need to be addressed before self-driving cars can hit the roads. Now a Canadian expert believes people will have'a lot more sex in cars', once a computer takes over and this could be dangerous as the'drivers' won't be paying attention to the road The claims were made by Barrie Kirk from the Canadian Automated Vehicles Centre of Excellence.

When teenage carpet salesman Lee Biggins decided to set up a jobseekers website, he wasn't going to let the fact he didn't have any computer skills hold him back. This was back in 1999, and the then 19-year-old had big ambitions for his business idea. So he went out and spent 899 on a computer, and an internet how-to book. Mr Biggins, who had left school at 15 "with some terrible grades - Es, Fs, and Gs", also enrolled on a computer literacy course in his hometown of Fleet, in Hampshire, 45 miles south west of London. But realising he could still do with some technical assistance, he says he went down his local pub one evening, and asked everyone: "Does anyone know someone who can build a website?"

In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of the ABC structure and hybridizations with interpolation strategies. The latter are inspired by the quadratic trust region approach for local investigation and by an efficient global optimizer for separable problems. Each modification and their combined effects are studied with appropriate metrics on a numerical benchmark, which is also used for comparing AsBeC with some effective ABC variants and other derivative-free algorithms. In addition, the presented algorithm is validated on two recent benchmarks adopted for competitions in international conferences. Results show remarkable competitiveness and robustness for AsBeC.

Bandits are becoming an essential tool in modern recommenders systems [9, 12]. Most recommendation setting involve an ever changing dynamic set of items, in many domains such as news and ads recommendation the item set is changing so rapidly that is impossible to use standard collaborative filtering techniques. In these settings bandit algorithms such as contextual bandits have been proven to work well [10] since they provide a principled way to gauge the appeal of the new items. Yet, one drawback of contextual bandits is that they mainly work in a content-dependent regime, the user and item content features determine the preference scores so that any collaborative effects (joint user preferences over groups of items) that arise are being ignored. Incorporating collaborative effects into bandit algorithms can lead to a dramatic increase in the quality of recommendations. In bandit algorithms this has been mainly done by clustering the user. For instance, we may want to serve content to a group of users by taking advantage of an underlying network of preference relationships among them. These preference relationships can either be explicitly encoded in a graph, where adjacent nodes/users are deemed similar to one another, or implicitly contained in the data, and given as the outcome of an inference process that recognizes similarities across users based on their past behavior. To deal with this issue a new type of bandit algorithms has been developed which work under the assumption that users can be grouped (or clustered) based on their selection of items e.g.

Distributional (or distribution-valued) data are a new type of data arising from several sources and are considered as realizations of distributional variables. A new set of fuzzy c-means algorithms for data described by distributional variables is proposed. The algorithms use the $L2$ Wasserstein distance between distributions as dissimilarity measures. Beside the extension of the fuzzy c-means algorithm for distributional data, and considering a decomposition of the squared $L2$ Wasserstein distance, we propose a set of algorithms using different automatic way to compute the weights associated with the variables as well as with their components, globally or cluster-wise. The relevance weights are computed in the clustering process introducing product-to-one constraints. The relevance weights induce adaptive distances expressing the importance of each variable or of each component in the clustering process, acting also as a variable selection method in clustering. We have tested the proposed algorithms on artificial and real-world data. Results confirm that the proposed methods are able to better take into account the cluster structure of the data with respect to the standard fuzzy c-means, with non-adaptive distances.