Disney is experimenting with a deep learning AI that tracks movie goers' emotional reactions to films. The company's research branch developed neural networks that can assess viewers' reactions simply by monitoring their facial expressions as they watch movies like'Big Hero 6,' 'The Jungle Book' and'Star Wars: The Force Awakens.' While tests of the new method - called factorized variational autoencoders or FVAEs - are preliminary, it has already been demonstrated that that the new technique outperformed conventional methods. The data set ended up with 16 million facial landmarks from 3,179 viewers. Though preliminary, the experiment demonstrated a'very strong predictive performance' that could reliably guess a viewer's reactions to the remainder of a movie after just a few minutes Neural networks are computational models that learn similarly to humans, except with a lot more processing power.

Valerian and the City of a Thousand Planets flopped. At least that's the way things look after the opening weekend of the Luc Besson-written and -directed sci-fi film: It headed into Monday with just about $17 million in receipts and a splattery 54 percent on Rotten Tomatoes (and a better audience score of 60 percent, to be fair). Now, I spent bits and pieces of the last two years working on a print feature about Besson and Valerian. But it wasn't exactly about the film itself; by the time I had put my story to bed, I'd only seen about 20 minutes of the actual movie. This is a not-so-secret secret of print magazine features: The lead time of monthly magazines like WIRED means that the journalism is often done before the film.

What do you get when you combine the world's most valuable tech company and the self proclaimed "hardest working man in show business?" ENORMOUS ASTEROID DUBBED'THE ROCK' WILL MAKE ITS CLOSEST APPROACH TO EARTH YET Apple and Dwayne Johnson – better known as The Rock – have joined together to create an advertisment to teach people how to use Siri every day. Known as "The Rock x Siri Dominate The Day," the ad sees Johnson call up a Lyft car, fly a plane to Rome, appear at a fashion show and more, all using the iPhone 7. Johnson, who notoriously has a busy schedule, enlists Siri to help him with these tasks as Apple attempts to push the voice assistant to be a more active part of people's daily lives. "You should never, ever, under any circumstances, underestimate how much Dwayne Johnson can get done in a day with Siri," Apple put in a caption under the ad on its YouTube page. At its developer conference in June, Apple said Siri was being used on more than 375 million iOS devices each month.

Imagery taken by Google's Street View team is being turned into amazing artistic landscape photography, using an experimental piece of AI software. Experts from the firm have used machine learning to train its Creatism software to scour pictures of impressive views from around the world, which it then alters using visual effects. Many of the breathtaking panoramas that result from the process appear as if they have been captured by professional photographers. But can you spot the difference between the machine generated snaps and the pictures taken by people? Take a look at the images below and visit the bottom of the page to find out which is which.

Game of Thrones season 7 is here and two episodes down. And, the anxiety over the next major character deaths is sweeping across the fan base. While the internet is flooding with fan theories, a researcher has developed a machine learning-powered network to predict possible deaths in the highly popular and bloody series. Milan Janosov, a PhD candidate at the Central European University, built a sort of social network of major GoT characters to come up with a ranking system of probable character deaths. The network analyses previous deaths of characters and their social ties and conversations with characters that are still alive, looking for consistencies and similarities, to predict future deaths.

The artificial intelligence chipset market has a huge potential across various industry verticals such … Tweet According to the latest market research report titled Artificial Intelligence (AI) Chipset market, the overall artificial intelligence chipset market is expected to be worth USD 16.06billion by 2022, growing at a CAGR of 62.9% from 2016 to 2022. The artificial intelligence chipset market has a huge potential across various industry verticals such as retail, transportation and automation, manufacturing, BFSI, and agriculture, among others. The major factor driving the artificial intelligence market globally is the growing number of applications of AI technologies in various end-user verticals and the growing adoption of AI for the improvement of consumer services. The growth of the artificial intelligence market is also driven by the development of IT infrastructure and the penetration of Smartphone's and smart wearable in countries such as India and China. Artificial Intelligence products market expected to hold the largest market share from 2016 to 2022 The AI market by products is expected to hold largest market share from 2016 to 2022.

"There is one thing we say to death. Master sword fighter Syrio Forel's wisecrack in the first season of popular TV series, "Game of Thrones," made Miltos Yerolemou's short role -- as Arya Stark's sword instructor -- in the show memorable. Forel died in the eighth episode but her student, who got to hear the wisecrack during one of the training sessions, has turned out to become one of the strongest characters in the television adaptation of epic fantasy novel, "A Song of Ice and Fire," authored by show producer George R R Martin. Its seventh season premiered last week. Like Arya, most survivors on the wildly unpredictable show have defied death more than once. With a devoted fan base of millions across the world, "Game of Thrones" has its loyalists hooked for the penultimate season as the contenders to the Iron Throne get further embroiled in the power struggle. While the Harvard University may soon be getting a humanities course based on the saga, it has also inspired a research predicting the death of the lead characters using machine learning methods. In the research, author Milán Janosov used the show's subtitles, collected in dialogue format on a fan website, as the data source. "We have a set of 94 characters interesting enough to care about.

Think about your favorite work of art. Why do you like it so much? What does it do for you? Be it painting, sculpture, music, or writing, we love art not just for its beauty, but for the reactions and emotions it evokes in us. You probably feel a sort of kinship with your favorite artists even though you've never met them, because their work speaks to you in what feels like a unique and personal way.

Streetlib is an online self-publishing solution that is geared towards independent authors. When you register for an account you can upload your e-book use a free ISBN that they give you. There are a ton of distribution options, but Amazon, Apple, Kobo, Google Play and Tolino generate the most sales. There are over 180,000 titles from 75,000 authors and the average indie is earning $35,000 per year. The markets that Streetlib finds that are most successful are mainly outside the United States, such as Italy, France, Germany, Spain, UK, Mexico, Canada and India.

In this paper, we study the combinatorial multi-armed bandit problem (CMAB) with probabilistically triggered arms (PTAs). Under the assumption that the arm triggering probabilities (ATPs) are positive for all arms, we prove that a class of upper confidence bound (UCB) policies, named Combinatorial UCB with exploration rate $\kappa$ (CUCB-$\kappa$), and Combinatorial Thompson Sampling (CTS), which estimates the expected states of the arms via Thompson sampling, achieve bounded regret. In addition, we prove that CUCB-$0$ and CTS incur $O(\sqrt{T})$ gap-independent regret. These results improve the results in previous works, which show $O(\log T)$ gap-dependent and $O(\sqrt{T\log T})$ gap-independent regrets, respectively, under no assumptions on the ATPs. Then, we numerically evaluate the performance of CUCB-$\kappa$ and CTS in a real-world movie recommendation problem, where the actions correspond to recommending a set of movies, the arms correspond to the edges between the movies and the users, and the goal is to maximize the total number of users that are attracted by at least one movie. Our numerical results complement our theoretical findings on bounded regret. Apart from this problem, our results also directly apply to the online influence maximization (OIM) problem studied in numerous prior works.