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?

Tomorrow's retail stores want to take a page from their online rivals by embracing advanced technology -- everything from helpful robots to interactive mirrors to shelves embedded with sensors. The goal: Use these real-world store features to lure shoppers back from the internet, and maybe even nudge them to spend more in the process. Amazon's new experimental grocery store in Seattle, opening in early 2017, will let shoppers buy goods without needing to stop at a checkout line. Sensors track items as shoppers put them into baskets or return them to the shelf. The shopper's Amazon account gets automatically charged.

First, I offer you a review of the movie "Sully" -- and don't worry, there aren't any spoilers. Because you know how the story goes: Birds hit plane. Pilot, nicknamed "Sully," lands plane on Hudson. So how do you make a movie out of something when everyone knows how it turns out? Yes, you can bring up the success of the film "Titanic," but it's not like there was actual footage of that disaster on YouTube.

"We are Now Controlling the Transmission" If you aren't familiar with the 60's TV series "The Outer Limits" you need to watch this intro (its 58 seconds long). Artificial Intelligence is controlling more than you realize, and in 2017, it's going to accelerate. AI algorithms are already affecting which products & services you see when you perform a search – regardless of the device or interface. AI is pushing messages, offers, & advice to you that you may never have asked for. Artificial Intelligence is deciding what shows up in your news feeds, on the sites you frequent, and in the apps you use.

"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?

I've been following the idea of algorithm-driven design for several years now and have collected some practical examples. The tools of the approach can help us to construct a UI, prepare assets and content, and personalize the user experience. The information, though, has always been scarce and hasn't been systematic. However, in 2016, the technological foundations of these tools became easily accessible, and the design community got interested in algorithms, neural networks and artificial intelligence (AI). Now is the time to rethink the modern role of the designer. One of the most impressive promises of algorithm-driven design was given by the infamous CMS The Grid3. It chooses templates and content-presentation styles, and it retouches and crops photos -- all by itself. Moreover, the system runs A/B tests to choose the most suitable pattern.