When Robyn Exton first launched her dating and social networking app for lesbians and bisexual women, a lack of cash for advertising meant she'd go to nightclubs armed with bottles of spirits. "In the early days I'd go to nightclubs with a bottle of sambuca in one hand, and tequila in the other, and encourage girls to download the app in return for a shot," says the 29-year-old. Then at UK lesbian, gay, bisexual and transgender (LGBT) festivals Manchester and Brighton Pride, she targeted women by standing outside the portable toilets handing out toilet paper with flyers promoting the app. This was back in 2013, and Ms Exton's low cost, but innovative, approach to marketing soon saw user numbers rise steadily, then further gaining traction thanks to positive word of mouth. Founded in London, but with its headquarters moving to San Francisco last year in order to be closer to US investors, and to be in the thick of the burgeoning social network scene, the Her app now has more than one million female users around the world.

This sunday we are running a workshop at ACM CHI 2016 called "Human Centered Machine Learning". I thought I would write an article to explain the general idea (though the workshop itself is a way of better understanding the idea). Statistical Machine Learning is one of the most successful set of techniques to come out of Computer Science in the last decades, and one that a lot of people are thinking about at the moment. It's often presented as quite an impersonal process: machines that learn for themselves, even AI that risk taking over the world. But, in fact, there is a lot of human work that goes into machine learning and not enough people have been talking about that.

This video will get you up and running with your first AI composer in just 10 lines of Python. This is'a' way to generate music, it's not necessarily the absolute best way. In a future video, I'll discuss how to easily use cloud GPU computing. Much more to come so please subscribe, like, and comment.

The Big G wants its app to be more conversational, so it's feeding a neural network with steamy sex scenes and hot encounters. According to Buzzfeed News, the network has been devouring a collection of 2,865 romance novels over the past few months, with saucy titles like Fatal Desire and Jacked Up. It seems to be working too: it was able to write sentences resembling passages in the books during the researchers' tests. While the AI now has what it takes to become an erotic novelist, the team's real goal is to use its newly acquired conversational tone with the Google app.

Lucy gave Luc Besson's career a little bit of a boost. It's not like the producer of countless action movies needed the help, but Lucy was the director's biggest hit in years, and it most likely afforded him the opportunity to make Valerian and the City of a Thousand Planets. The pricey sci-fi film is currently shooting, but once that picture wraps, Besson will be turn his attention to Artificial Intelligence, an original show from TNT and EuroCorp TV USA. EuroCorp TV USA has sold Artificial Intelligence to TNT. Besson will co-write, co-create, and executive produce the series with Bill Wheeler, who worked on the upcoming Ghost in the Shell remake.

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

Public discourse on environmental and health issues has risenon social media. Upon an environmental crisis, various chatterssuch as breaking news, misinformation, and rumor couldaggravate social confusion and proliferate negative publicsentiment. In an effort to study public sentiments on environmentalissues in South Korea, we analyzed 158,964 tweetsgenerated over a 4-year period following the Fukushima accidentin 2011, the largest release of radioactivity to environmentin recent history. This event led to a significant increasein public’s interest on environmental and nuclear issues inKorea. We employed Bayesian network and recursive partitioningto observe the classification regression tree structureof major topics. Topics on health and environment were interlinkedclosely and represented both apprehension and concernabout health threats and pollution. Our methodologyhelps analyze large online discourse efficiently and offers insightto crisis response organizations.

Information spreading on social media contributes to the formation of collective opinions. Millions of social media users are exposed every day to popular memes — some generated organically by grassroots activity, others sustained by advertising, information campaigns or more or less transparent coordinated efforts. While most information campaigns are benign, some may have nefarious purposes, including terrorist propaganda, political astroturf, and financial market manipulation. This poses a crucial technological challenge with deep social implications: can we detect whether the spreading of a viral meme is being sustained by a promotional campaign? Here we study trending memes that attract attention either organically, or by means of advertisement. We designed a machine learning framework capable to detect promoted campaigns and separate them from organic ones in their early stages. Using a dataset of millions of posts associated with trending Twitter hashtags, we prove that remarkably accurate early detection is possible, achieving 95% AUC score. Feature selection analysis reveals that network diffusion patterns and content cues are powerful early detection signals.

Questions form an integral part of our everyday communication, both offline and online. Getting responses to our questions from others is fundamental to satisfying our information need and in extending our knowledge boundaries. A question may be represented using various factors such as social, syntactic, semantic, etc. We hypothesize that these factors contribute with varying degrees towards getting responses from others for a given question. We perform a thorough empirical study to measure effects of these factors using a novel question and answer dataset from the website Reddit.com. We also use a sparse non-negative matrix factorization technique to automatically induce interpretable semantic factors from the question dataset. Such interpretable factor-based analysis overcomes limitations faced by prior related research. We also document various patterns on response prediction we observe during our analysis. For instance, we found that preference-probing questions are rarely answered by actors.