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.

In this excerpt from a #TalkingTech Live broadcast, the panelists weigh in on Uber's bad week, and what's next in ride hailing. LOS ANGELES -- This week's tech news was dominated by charges of sexual harassment and discrimination at ride-hailing service Uber, a potential push from Google to bring ride-sharing to the mass market and a move from Facebook to bring more ads to the social network. But Uber owned the most headlines, which started on Sunday with a blog from a former female engineer who described how the company's human resources department repeatedly deflected her and other women's reports of sexual harassment from their colleagues, even telling her that she could expect a negative performance review if she stayed on her alleged harasser's team. The blog post was seen as a wake-up call for Silicon Valley, where six out of 10 women say they've experienced unwanted sexual advances, according to a survey released last year. Still, many pointed out that the male-dominated tech industry has had several such "wake-up calls" -- to little avail.

The award-winning show inspired by a singular graphic memoir. Plus: SCI-Arc in Mexico City, Oscar-nominated films that emerged from important plays, and a longtime curator leaves the downtown gallery he helped establish. I'm Carolina A. Miranda, staff writer for the Los Angeles, and I'm in your inbox with a weekly digest of everything culture: On March 7, Los Angeles will head to the polls to vote on a development measure that could affect the profile of the city. Measure S (formerly known as the Neighborhood Integrity Initiative) seeks to put a two-year moratorium on development projects that require an amendment to the city's general plan, among other factors. Times architecture critic Christopher Hawthorne parses the measure and its backers, whose roots lie in anti-growth initiatives from the 1980s -- and whose vision of Los Angeles seems to lie squarely in the 1960s.

From giant bronze automatons to artificial intelligence capable of beating humans at their own games, Adam Rutherford asks whether or not humanity will be enslaved by robots.