Arrival Amy Adams stars in this elegant, involving science-fiction drama that is simultaneously old and new, revisiting many alien-invasion conventions but with unexpected intelligence, visual style and heart. The Eagle Huntress A portrait of a 13-year-old Kazakh girl from Mongolia who defies eons of tradition by learning to hunt with fierce golden eagles is a documentary so satisfying it makes you feel good about feeling good. The Edge of Seventeen Hailee Steinfeld gives a superb performance as a high-school misfit in Kelly Fremon Craig's disarmingly smart teen dramedy, the rare coming-of-age picture that feels less like a retread than a renewal. Elle Paul Verhoeven's brilliantly booby-trapped thriller starring Isabelle Huppert is a gripping whodunit, a tour de force of psychological suspense and a wickedly droll comedy of manners. The Handmaiden The most absorbing feature in years from South Korean director Park Chan-wook ("Oldboy") is a teasingly witty and elegant puzzle-box of a thriller about two women (played by Kim Tae-ri and Kim Min-hee) pursuing their destinies in 1930s Japanese-occupied Korea.

Traditionally, marketing has been about broadcasting your generic message to as many people as possible with the hopes that someone will listen. But this approach is about as persuasive as the guy on the street corner dancing to Kanye West tunes and spinning around the discounted tax prep promotion sign. But for most of us, it's not going to drive action. So instead of a flashing sign with a broad message, it's a well-crafted, personalized message that works to remind you that yes, you did buy a house this year and yes, you can save $3,500 if you prepay your interest. Modern brands like Netflix, Uber, and Airbnb are already taking this personalized approach to marketing.

PDF: We made a fancy PDF of this post for printing and offline viewing. Note: The reason this post took three weeks to finish is that as I dug into research on Artificial Intelligence, I could not believe what I was reading. It hit me pretty quickly that what's happening in the world of AI is not just an important topic, but by far THE most important topic for our future. So I wanted to learn as much as I could about it, and once I did that, I wanted to make sure I wrote a post that really explained this whole situation and why it matters so much. Not shockingly, that became outrageously long, so I broke it into two parts. This is Part 1--Part 2 is here. We are on the edge of change comparable to the rise of human life on Earth. It seems like a pretty intense place to be standing--but then you have to remember something about what it's like to stand on a time graph: you can't see what's to your right. So here's how it actually feels to stand there: Imagine taking a time machine back to 1750--a time when the world was in a permanent power outage, long-distance communication meant either yelling loudly or firing a cannon in the air, and all transportation ran on hay. When you get there, you retrieve a dude, bring him to 2015, and then walk him around and watch him react to everything.

Machine learning has evolved from science fiction movie plots to everyday reality in just a short time.

IBM on Wednesday announced it was teaming with General Motors to put Watson artificial intelligence to work to personalize the driving experience for motorists. OnStar software built into GM cars will be imbued with Watson smarts, enabling them to get to know their drivers, IBM chief executive Ginni Rometty told the WSJD Live technology conference in California. "It learns from how you behave and what you do," Rometty said. "It knows how you like your coffee and orders it for you; it reminds you to pick up your children." The resulting OnStar Go system will be the auto industry's first "cognitive mobility platform," according to the companies.

This is the last – for now – installment of my mini-series on sentiment analysis of the Stanford collection of IMDB reviews (originally published on recurrentnull.wordpress.com). So far, we've had a look at classical bag-of-words models and word vectors (word2vec). We saw that from the classifiers used, logistic regression performed best, be it in combination with bag-of-words or word2vec. We also saw that while the word2vec model did in fact model semantic dimensions, it was less successful for classification than bag-of-words, and we explained that by the averaging of word vectors we had to perform to obtain input features on review (not word) level. So the question now is: How would distributed representations perform if we did not have to throw away information by averaging word vectors?

Last year was an eventful one in terms of all the interviews I conducted. Here are a few highlights of the interviews I did last year for you to browse at your leisure. The latest in digital print technology brings Bruce Lee's face to life with this S.H. Figuarts rendition. I started the year talking with Bandai about their toy and figure output, specifically at the collector's end of the spectrum. In the time since, we've had all manner of major new announcements in terms of Bandai's Tamashii Nation line as well as a move into more worldwide markets.

As a species, we've always been used to having choices. Whether it's browsing the shelves of a bookshop or scanning the menu of a restaurant, we're naturally accustomed to making decisions based on a finite number of options in front of us. But within the online world, those finite choices have started evolving towards the infinite, and we're left with more choice than we've ever been used to. We shop on websites that sell everything we could possibly dream of buying, and we listen to music on streaming services that gives us access to tens of millions of records -- far more than we could ever listen to in our lifetime. This newfound wealth of opportunities should be liberating us, but instead it's overwhelming us and making it more difficult for us to actually make choices.

In the previous blog on machine learning, we learnt about applying machine learning techniques to recommendation engines and an overview of collaborative filtering (CF) algorithms implemented in Apache Mahout. In this post, we'll discuss how to build a recommendation engine using Mahout. Let us take an example of a movie rating application that allows users to rate movies and suggests other movies that they might like. Following could be a data set where some users have rated some movies on a scale of 1 to 5 (highest). The empty cells denote that the user has not rated the movie.

"Fake news" vexed the media classes greatly in 2016, but the tech world perfected the art long ago. With "the internet" no longer a credible vehicle for Silicon Valley's wild fantasies and intellectual bullying of other industries – the internet clearly isn't working for people – "AI" has taken its place. Almost everything you read about AI is fake news. The AI coverage comes from a media willing itself into a mind of a three year old child, in order to be impressed. For example, how many human jobs did AI replace in 2016?