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Now your videos of surfing, skiing, snowboarding, motocross…and all those other zany activities will be easier than ever to edit, thanks to machine learning and a new attachment known as Pik'd. Pik'd is meant to help you ensure that fun and adrenaline are the protagonists of your footage, and not much time or concern needs to be devoted to post-production. The communication between the technologies works automatically, so the user doesn't have to manipulate the device. All you have to do is attach the tech to your GoPro and record. Once the images have been captured, connect the camera to the computer and open the Pik'd software, which will be supplied free of charge.

We've long known that there are many tasks that computers can perform faster – and better – than humans. Of course, we still have to teach computers HOW to do these tasks, and when using conventional programming techniques we have to be very specific about what computers should do and when. With machine learning, we're essentially teaching computers how to learn what to do, and some of them are becoming better than we are at complex tasks. For example, machine learning is a key enabler of self-driving cars and experts predict that they will eventually be safer than human-driven vehicles. That's just one example of how machine learning is letting us use computers in new ways to do new things.

Trendytoutdoor pizza ovens have been labelled an environmental menace by scientists. The wood burning stoves used to cook pizzas churn out dangerous emissions which may be polluting some built up urban areas where the crusty favourites are particularly popular. It is not just pizzas that could be taking a bite out of attempts to clean up the environment, the study says. Similar wood burners are also used by many steakhouse restaurants too. The wood burning stoves used to cook pizzas churn out dangerous emissions which may be polluting some built up urban areas where the crusty favourites are particularly popular.

Tekstum provides real-time scientific analysis of the opinions and feelings that readers are expressing online. Using a state-of-the-art algorithm built with big data and artificial intelligence, the company promises to revolutionise how publishers market, and how customers search for and buy books, by synthesising, analysing and visualising readers' moment-to-moment emotions and experiences. Spanish founder and CEO Marc Santandreu worked for international publishing companies such as Author Solutions and Regió7. Lauren Romeo (PhD in Computational Linguistics) is the Lead Scientist, pioneering the Natural Language Processing research and development of the engine, while Juanjo Fernandez is the engineer responsible for the development of the software. Every year the number of new books in the market grows exponentially, generating huge amounts of data at ever-faster rates.

Scientists believe its only a matter of time before a devastating earthquake hits southern California. The'Big One', expected to be triggered by the San Andreas fault, could leave thousands dead or displaced. Now, scientists are preparing for the worst - and the latest defence is a steel frame building that experts believe could withstand a major tremor. Researchers at the University of California in San Diego rocked and rattled a six-story steel frame building on a giant shake table to see how the structure would withstand major earthquakes. The shaking simulated an earthquake of the 6.7 magnitude that occurred in 1994 in Los Angeles, causing significant damage.

The increasing interest in complex networks research has been a consequence of several intrinsic features of this area, such as the generality of the approach to represent and model virtually any discrete system, and the incorporation of concepts and methods deriving from many areas, from statistical physics to sociology, which are often used in an independent way. Yet, for this same reason, it would be desirable to integrate these various aspects into a more coherent and organic framework, which would imply in several benefits normally allowed by the systematization in science, including the identification of new types of problems and the cross-fertilization between fields. More specifically, the identification of the main areas to which the concepts frequently used in complex networks can be applied paves the way to adopting and applying a larger set of concepts and methods deriving from those respective areas. Among the several areas that have been used in complex networks research, pattern recognition, optimization, linear algebra, and time series analysis seem to play a more basic and recurrent role. In the present manuscript, we propose a systematic way to integrate the concepts from these diverse areas regarding complex networks research. In order to do so, we start by grouping the multidisciplinary concepts into three main groups, namely features, similarity, and network connectivity. Then we show that several of the analysis and modeling approaches to complex networks can be thought as a composition of maps between these three groups, with emphasis on nine main types of mappings, which are presented and illustrated. Such a systematization of principles and approaches also provides an opportunity to review some of the most closely related works in the literature, which is also developed in this article.

Innovation is among the key factors driving a country's economic and social growth. But what are the factors that make a country innovative? How do they differ across different parts of the world and different stages of development? In this work done in collaboration with the World Economic Forum (WEF), we analyze the scores obtained through executive opinion surveys that constitute the WEF's Global Competitiveness Index in conjunction with other country-level metrics and indicators to identify actionable levers of innovation. The findings can help country leaders and organizations shape the policies to drive developmental activities and increase the capacity of innovation.

Despite the hardware news this week, E3 2016 is still all about the games. For gamers, that means a bounty of new trailers revealing gameplay and cinematic introductions to new worlds. Whether you're a fan of the PC, Xbox One or PlayStation, it's hard not to get excited about what's ahead. This may be the most buzzed-about trailer coming out of E3 2016. The first game from "Metal Gear Solid" creator Hideo Kojima after his departure from Konami was always going to be a big deal, but then you add some really interesting visuals and a naked Norman Reedus, better known as Daryl from "The Walking Dead," holding a baby and that's a recipe for success.

Once a year, when Slava Epstein was growing up in Moscow, his mother took him to the Exhibition of the Achievements of the National Economy, a showcase for the wonders of Soviet life. The expo featured many things--from industrial harvesters to Uzbek wine--but Epstein, who began going in the nineteen-sixties, when he was eight or nine, was interested primarily in one: the Cosmos Pavilion, a building the size of a hangar, with a ceiling shaped like a giant inverted parabola. Space fever was running high in the city. Since 1961, when Yuri Gagarin orbited the globe, unmanned vessels had been launched toward Mars and Venus. Beside the expo's entrance, the towering Monument to the Conquerors of Space depicted a probe swooping up to the heavens. The Pavilion displayed futuristic technology--Vostok rockets and Soyuz orbiters--but Epstein was less interested in the glories of advanced thruster design than in the glories of space. He wanted to devote himself to astronomy. When a textbook that he found on the topic began with algebraic formulas, he prodded his older brother to explain them. During high school, he enrolled in classes in physics and math at Moscow State University. His parents disapproved of his desired career: because he is half Jewish, Epstein would face harsh Soviet quotas limiting Jews in the study of physics, a field deemed relevant to national security. But after his first lecture the professor invited him for a walk, and affirmed what they had been saying all along. "Don't do it," he warned. Soviet Russia may have been a fatalist's paradise, but from a young age Epstein felt that he was hardwired for optimism. He convinced himself that what is truly important in science is the ability to connect ideas, no matter the field, and so he took up biology. Rather than telescopes, he would use microscopes, which he began taking with him on trips to the White Sea, near the Arctic Circle, to study protozoa along the shore--research that could be conducted with minimal state interference. Over time, he grew interested in even smaller, more ancient forms of life: bacteria. Studying microbes inevitably causes a reordering of one's perceptions: for more than two billion years, they were the only life on this planet, and they remain in many ways its dominant life form. To a remarkable extent, the microbial cosmos was less explored than the actual cosmos: precisely how the organisms evolve, replicate, fight, and communicate remains unclear. Nearly all of microbiology, Epstein eventually learned, was built on the study of a tiny fraction of microbial life, perhaps less than one per cent, because most bacteria could not be grown in a laboratory culture, the primary means of analyzing them. By the time he matured as a scientist, many researchers had given up trying to cultivate new species, writing off the majority as "dark matter"--a term used in astronomy for an inscrutable substance that may make up most of the universe but cannot be seen.