On average, when an AP sportswriter covers a game, she produces eight different versions of the same story. Aside from writing the main print story, they have to write story summaries, separate ledes for both teams, convert the story to broadcast format, and more. "It's a manual labor nightmare," Jim Kennedy, the AP's senior vice president for strategy and enterprise development, told me in his New York office. Collectively, AP journalists spend about 800 hours a week converting print stories to broadcast format. As a result, the AP is experimenting with machine learning in an attempt to automate some of those processes.

One of the biological side effects of being a human is the will to live. Luckily for us, one of the ways in which our brain gives the heads up to inform us of potentially dangerous situations is by invoking that little old survival instinct called "fear". Have you ever been stuck sitting next to someone in a cinema, completely unfazed by a horror movie, while you diverted your attention to the closest escape door? Everyone gets spooked by out by different stimuli – whether rational or irrational – clowns, gigantic spiders, or even marshmallows. Since we know that stimuli can evoke varying psychological responses, one group of researchers from our team at Data61 and MIT Media lab, set out to find what unites us in our phobia and terrifies us on a universal scale.

Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-words representation as a surrogate for the content information but they are neither effective nor natural enough. To address this problem, we develop a collaborative recurrent autoencoder (CRAE) which is a denoising recurrent autoencoder (DRAE) that models the generation of content sequences in the collaborative filtering (CF) setting. The model generalizes recent advances in recurrent deep learning from i.i.d. input to non-i.i.d. (CF-based) input and provides a new denoising scheme along with a novel learnable pooling scheme for the recurrent autoencoder. To do this, we first develop a hierarchical Bayesian model for the DRAE and then generalize it to the CF setting. The synergy between denoising and CF enables CRAE to make accurate recommendations while learning to fill in the blanks in sequences. Experiments on real-world datasets from different domains (CiteULike and Netflix) show that, by jointly modeling the order-aware generation of sequences for the content information and performing CF for the ratings, CRAE is able to significantly outperform the state of the art on both the recommendation task based on ratings and the sequence generation task based on content information.

Model-based collaborative filtering analyzes user-item interactions to infer latent factors that represent user preferences and item characteristics in order to predict future interactions. Most collaborative filtering algorithms assume that these latent factors are static, although it has been shown that user preferences and item perceptions drift over time. In this paper, we propose a conjugate and numerically stable dynamic matrix factorization (DCPF) based on compound Poisson matrix factorization that models the smoothly drifting latent factors using Gamma-Markov chains. We propose a numerically stable Gamma chain construction, and then present a stochastic variational inference approach to estimate the parameters of our model. We apply our model to time-stamped ratings data sets: Netflix, Yelp, and Last.fm, where DCPF achieves a higher predictive accuracy than state-of-the-art static and dynamic factorization models.

Ten years ago, at All Tomorrow's Parties, a now-defunct music festival held occasionally in the rain-harangued British seaside town of Camber Sands, I attended a show by Lightning Bolt, a noise-rock duo from Providence, Rhode Island. They had set up in the center of a grubby hall at Pontins, England's second-best-known budget holiday park. At the band's request, security had allowed only thirty or so festival-goers into a venue that could comfortably have accommodated a thousand, leaving plenty of room on the beery carpet for dancing, or possibly rioting. We clustered in the round as Brian Gibson began to flay his bass and Brian Chippendale, wearing a wrestler's mask, assaulted his drum kit, his voice blaring primally through a microphone taped to his cheek. The performance was disorienting, both intimate and savage, like the first moments after an accident, before time resumes its normal speed and the damage can be measured.

Dawn of the Sexbots: Is new TV show Westworld's future where tourists romance robots just a sick fantasy? Colin Morgan played Leo (right) in Channel 4's popular drama Humans, which returns on Sunday night The views expressed in the contents above are those of our users and do not necessarily reflect the views of MailOnline. By posting your comment you agree to our house rules.

Days after Apple announced the death of the function keys and the traditional USB port on the MacBook Pro, its been discovered that the machine meant the end of another iconic piece of technology. Apple ditched the startup chime in the new MacBook Pros since they automatically boot up when the lid is opened. The MacBook will also be available in a cheaper option with traditional function keys, Phil Schiller revealed. Apple has also slimmed down the new MacBook - so much so it is killing off the MacBook Air. The F-sharp sound was also used in the 2008 Pixar film Wall-E (pictured), when the animated robot used solar panels to charge itself.

People listen to the radio as the results of the presidential elections are announced in Kireka, Uganda, in February. Many rural Ugandans don't have Internet access, and the radio is a central source of news -- and platform for citizens' opinions. People listen to the radio as the results of the presidential elections are announced in Kireka, Uganda, in February. Many rural Ugandans don't have Internet access, and the radio is a central source of news -- and platform for citizens' opinions. Its omnipresence can make it easy to forget that making this technology has been really, really hard.

I sat down this week with tech columnists Fowler and Stern at the Wall Street Journal D Live conference here for an extended #TalkingTech podcast. What happens when 3 tech columnists get together? They talk gadgets and tech--or more specifically, our three highlights so far, for 2016. So with new MacBooks, Surface computers, Google Home and expected new entries from Amazon set to be released in the coming months, we look back at the previous 10 months of tech. Fowler loves the Eero, the router add-on to bring better wifi throughout the house, while Stern has fallen in love with dongles, those little doo-dads that connect devices back to devices after slots disappear.

The use of recommender systems has exploded over the last decade, making personalized recommendations ubiquitous online. Most of the major companies, including Google, Facebook, Twitter, LinkedIn, Netflix, Amazon, Microsoft, Yahoo!, eBay, Pandora, Spotify, and many others use recommender systems (RS) within their services. These systems are used to recommend a whole range of items, including consumer products, movies, songs, friends, news articles, restaurants and various others. Recommender systems constitute a mission-critical technology in several companies. For example, Netflix reports that at least 75% of its downloads and rentals come from their RS, thus making it of strategic importance to the company.a