The following piece is a guest post from Jessica Rovello, the CEO and co-founder of Arkadium, which provides interactive content to brands and publishers. Guest posts do not necessarily reflect the opinions of this publication. Is the growing adoption of Artificial intelligence (AI) products by digital publishers a much-needed ...

There are many newspapers available in the New York City area that cater to different segments of the population. This project focuses on the Wall Street Journal (WSJ), an international newspaper with a high circulation in the New York area. For this project, I scraped details of articles over a three-week period in order to analyze some basic metrics of the newspaper as well as some of the topics that the WSJ focuses on. In total, the project encompassed a total of 4,126 articles from three consecutive weeks in August 2016. As a result of collecting these metrics, several interesting findings emerged.

Filmmakers have been trying to adapt Stephen King's The Dark Tower series for more than a decade. But with time-jumping metanarratives and compulsive genre-switching, the eight novels proved tough to wrangle into one film-able narrative. Director Nikolaj Arcel's version of King's events finally hits theaters today. Written by no fewer than four writers (not including King), the movie arrives with a lean 95-minute runtime and the kind of Rotten Tomatoes score (21% and barely climbing) that studios fear. But is it possible the critics aren't being fair?

We have a long history of "moral panics." Things that we fear, whether we should or not. In most cases, these fears aren't entirely irrational, but based on exaggerations or predictions that could, but probably won't, come true or simply are not nearly as horrific as may first appear. Many of us remember the Y2K scare of 1999, when we were told that the power grid, ATMs and our transportation systems could come to a screeching halt at midnight on Jan.1, 2000 because computers weren't programmed to recognize a new century. And, yes, there were a handful of problems, but the world didn't come to an end. There was a panic that our personal privacy was over in 1988 when Kodak introduced the first portable camera.

Promotion was easy in the 1960s. Things have changed a bit since then. Not to mention, technological disruption that (a) erodes, (b) replaces, or (c) reverses most of the current best practices every 6-12 months. That makes a marketer's role… complicated. But you ain't seen nothing yet.

Summary: A new artificial neural network can assess a viewer's reaction to movies based on patterns of facial expressions. With enough information, researchers say the ANN will be able to assess how an audience is reacting to a movie and predict an individual's response based on a few minutes of observation. Software automatically discovers patterns in facial expressions. Engineers have created a new deep-learning software capable of assessing complex audience reactions to movies using the viewer's facial expressions. Developed by Disney Research in collaboration with Yisong Yue of Caltech and colleagues at Simon Fraser University, the software relies on a new algorithm known as factorized variational autoencoders (FVAEs).

Robotic process automation (RPA) is a family of technologies designed to replicate human actions in order to complete a task or series of tasks. Unlike traditional programming constructs, RPA is designed to allow everyday workers to quickly deploy virtual counterparts to learn from and mimic the actions they use to accomplish routine tasks, therefore freeing the worker to handle more complex, and in some cases, more fulfilling work activities. According to a new report from Tractica, the market for RPA is developing rapidly, and the market intelligence firm forecasts that worldwide revenue in the sector will increase from $151 million in 2016 to more than $5.1 billion by 2025. The key industry sectors that are embracing RPA implementations include financial services & banking, utilities & telecommunications, retail & commercial, and healthcare & insurance. The market opportunity for RPA will be largest in Europe during the forecast period, with Asia Pacific and North America not far behind.

Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions. In the present work, we extend the usage of LRP to recurrent neural networks. We propose a specific propagation rule applicable to multiplicative connections as they arise in recurrent network architectures such as LSTMs and GRUs. We apply our technique to a word-based bi-directional LSTM model on a five-class sentiment prediction task, and evaluate the resulting LRP relevances both qualitatively and quantitatively, obtaining better results than a gradient-based related method which was used in previous work.

In one of the many conversations that animate "Columbus," a serenely intelligent first feature from the Korean American writer-director Kogonada, a part-time librarian named Casey (Haley Lu Richardson) and her co-worker Gabriel (Rory Culkin) discuss a tricky double standard. As Gabriel notes, someone who loves video games but finds books boring is criticized for having a short attention span, while someone with the opposite inclination is praised for having a long one. "It's not a matter of attention span, but of interest," he says. "Are we losing interest in things that matter?" Mercifully, he does not go on to extol the importance of gentle, gorgeously contemplative independent films like this one, though by that point "Columbus" has already made the case in much more delicate and persuasive terms.

A bunch of people have been asking for an implementation of Deep Reinforcement Learning from Human Preferences [Christiano et al., 2017] that came out last month. This contains a simplified system designed to be easy to read and understand, plus the webapp that we used for collecting feedback from humans. Happy to answer any questions that you have here!