This could mean being more conservative in the S8 features, though it also needs some breakout features to restore excitement in the brand. Llamas said that could come through software features, such as S Voice, a voice assistant rivaling Apple's Siri, or Samsung Flow, a way for multiple devices to work together. The new tablets will come with Samsung Flow so that you can make phone calls and answer texts using the tablet as a bridge to a phone sitting on the table or in your pocket.

Bill Paxton leaves behind the legacy of a total badass. The actor, who died unexpectedly on Saturday, has the unusual and entirely amazing distinction of being the only actor to play characters killed by a Terminator, a Predator and a Xenomorph (of Alien fame). SEE ALSO: Bill Paxton, star of '80s and '90s genre films and TV, dead at 61 The grim achievement speaks to Paxton's ubiquity as a character actor in some of the biggest genre films of the '80s and '90s. His victim status in three of the biggest sci-fi releases of the 20th century earns him the cult hero equivalent of an EGOT. The below video highlights Paxton's final moments in The Terminator, Predator 2, and Aliens.

Drones are becoming big business. DJI -- a company that has built drones for entry-level fliers (the Mavic Pro), prosumers (the Phantom line) and photo and cinematography professionals (Inspire) -- is hoping to ignite the drone-in-business revolution with its first business-class drone, the M200. The company unveiled its new M200 line of enterprise-class drones at Mobile World Congress in Barcelona, Spain on Sunday. However, DJI gave Mashable a tabletop preview of the drone weeks earlier at its offices in Manhattan. When we saw the drone, it was still in development (by the time you read this, it'll be flight-ready and examining a cell tower in Barcelona).

Just over a week ago, Donald Trump gathered members of the world's press before him and told them they were liars. "The press, honestly, is out of control," he said. "The public doesn't believe you any more." CNN was described as "very fake news… story after story is bad". That night I did two things. First, I typed "Trump" in the search box of Twitter. My feed was reporting that he was crazy, a lunatic, a raving madman. But that wasn't how it was playing out elsewhere. The results produced a stream of "Go Donald!!!!", and "You show'em!!!" There were star-spangled banner emojis and thumbs-up emojis and clips of Trump laying into the "FAKE news MSM liars!" Trump had spoken, and his audience had heard him. Then I did what I've been doing for two and a half months now. I Googled "mainstream media is…" And there it was.

Machine Learning is accelerating its transition from academia to industry. We see more and more media outlets reporting about it, but most of the time they exclusively focus on the final results and don't look into all the human-powered tasks that happen behind the scenes... the stuff that really makes the magic possible. So for most people, Machine Learning continues to be some sort of elusive magic. We were recently approached by One, the Vodafone-sponsored section of El Pais, to explain how Machine Learning works and, after giving it some thought, we decided to explain it using a simple example in a domain everyone is familiar with. As the 89th annual Academy of Motion Picture Arts and Sciences Award ceremony draws near and movie fans all over the world are getting ready for their office pools, we couldn't resist the temptation to take a stab at predicting the 2017 Oscars by applying some BigML-powered Machine Learning courtesy of our own Teresa Álvarez and Cándido Zuriaga.

Editor's note: This is the first chapter of a book I'm working on called Demystifying Artificial Intelligence. The goal of the book is to demystify what modern AI is and does for a general audience. So something to smooth the transition between AI fiction and highly mathematical descriptions of deep learning. I'm developing the book over time - so if you buy the book on Leanpub know that there is only one chaper in there so far, but I'll be adding more over the next few weeks and you get free updates. The cover of the book was inspired by this amazing tweet by Twitter user @notajf. Feedback is welcome and encouraged!

A blind man who narrowly escaped death when he was gunned down in the Philippines has developed software he hopes will offer life-changing independence to blind people. Marx Melencio was buying fried rice with his wife at a roadside store in Manila when he was shot in the chest and the head in an apparently random attack. The first bullet hit him 3mm from his heart. The second missed his brain by 2mm but singed his optic nerve, rendering him blind. Witnesses say the man who fired the gun was under the influence of both alcohol and drugs, but he's never been convicted.

What would it look like to ask a deep learning AI system to watch every political television advertisement of the 2016 presidential campaign season for two months and describe what it sees? That was the question I asked last February when I collaborated with the Internet Archive to take all 267 political ads they had identified (which had aired a collective 72,807 times as monitored by the Archive) and ran them frame-by-frame through Google's Cloud Vision API, producing what is likely the first large-scale application of production deep learning algorithms to describe the visual narratives of political advertising on television. Now, what if we took this same approach and instead of examining television, we looked at a quarter billion news photographs compiled from online news outlets in nearly every country of the world over the course of 2016? What would AI see in that vast archive of the visual narratives of the world's media? Google's Cloud Vision API is a commercial cloud service that accepts as input any arbitrary photograph and uses deep learning algorithms to catalog a wealth of data about each image, including a list of objects and activities it depicts, recognizable logos, OCR text recognition in almost 80 languages, levels of violence, an estimate of visual sentiment and even the precise location on earth the image appears to depict.

In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence - both semantic and syntactic - but might face difficulty remembering long-range dependencies. Intuitively, these long-range dependencies are of semantic nature. In contrast, latent topic models are able to capture the global underlying semantic structure of a document but do not account for word ordering. The proposed TopicRNN model integrates the merits of RNNs and latent topic models: it captures local (syntactic) dependencies using an RNN and global (semantic) dependencies using latent topics. Unlike previous work on contextual RNN language modeling, our model is learned end-to-end. Empirical results on word prediction show that TopicRNN outperforms existing contextual RNN baselines. In addition, TopicRNN can be used as an unsupervised feature extractor for documents. We do this for sentiment analysis on the IMDB movie review dataset and report an error rate of $6.28\%$. This is comparable to the state-of-the-art $5.91\%$ resulting from a semi-supervised approach. Finally, TopicRNN also yields sensible topics, making it a useful alternative to document models such as latent Dirichlet allocation.

The 89th annual Academy Awards ceremony is Sunday, and Alexa and Google Assistant have opinions about who will win Best Picture, Best Actor, and Best Actress. The Oscars ceremony is Sunday at 5:30 p.m. Pacific. Red carpet action begins at 4 p.m. Pacific. When you ask Google Assistant "Who will win Best Picture at the Oscars?" it says: "I'm rooting for Arrival -- a movie about translation is right up my alley. Plus I loved those aliens." "Best film is tough this year.