This article was posted by SmileJet on Dev Battles. She paints and writes poetry. She's also an artificial intelligence from the movie Her, which imagines how a juiced-up Siri will change our lives. Now, tech companies large and small are racing to make this a reality. You've heard the jargon: AI, machine learning, deep learning, neural networks, natural language processing.

Jefferson Graham reviews GoPro's new Karma drone, an affordable flying camera with stellar video quality. LOS ANGELES -- Welcome to the Drone Wars, where longtime action camera manufacturer GoPro has joined No. 1 drone manufacturer DJI with its own take on a flying camera. The bad: Karma is actually heavier than we are led to believe from marketing videos, (2.2 pounds vs. 1.6 for the new $750 DJI Mavic) and some of the cool operational drone features from the Mavic just aren't there in the Karma. That said, DJI knows drones, and GoPro knows photography. The Karma trumps the Mavic with a superior camera, but Mavic is the better drone.

While Sonos makes some decent speakers, many feel that its apps are lacklustre at best. If you fall into this camp, you'll be pleased to hear that Sonos hardware is now starting to play nice with Spotify Connect. If you sign up for the Sonos public beta, you'll be able to control your speakers from inside the Spotify app. That includes multi-speaker and multi-room setups -- change one or change the lot, it's your choice. If you have friends over that want to play DJ, they can also queue up songs and playlists from their own Spotify app, instead of downloading the Sonos equivalent.

Head of SK Telecom----s Corporate R&D Center Choi Jin-sung (left in the picture) and Intel Korea----s Chief Executive Officer Kwon Myung-sook [Photo provided by SK Telecom Co.] South Korea----s top wireless carrier SK Telecom Co. said it will work with the world----s biggest chipmaker Intel Corp. to develop connected car system piloted by artificial intelligence. SK Telecom last Thursday signed a memorandum of understanding with Intel to cooperate on developing Vehicle-to-Everything (V2X) communication and deep-learning based video recognition technologies, the telecommunication company said on Monday. The V2X communication refers to wireless technology transferring information from a vehicle to everything that may affect the vehicle. The Korean telecom company will provide vehicular communications and video recognition technologies while Intel contribute its expertise in 5G networks and deep-learning, or data-based machine learning, system to develop a platform for automated connected car. The two firms aim to demonstrate the new connected car technology as early as next year.

Ars is excited to be hosting this online debut of Sunspring, a short science fiction film that's not entirely what it seems. You know it's the future because H (played with neurotic gravity by Silicon Valley's Thomas Middleditch) is wearing a shiny gold jacket, H2 (Elisabeth Gray) is playing with computers, and C (Humphrey Ker) announces that he has to "go to the skull" before sticking his face into a bunch of green lights. It sounds like your typical sci-fi B-movie, complete with an incoherent plot. Except Sunspring isn't the product of Hollywood hacks--it was written entirely by an AI. To be specific, it was authored by a recurrent neural network called long short-term memory, or LSTM for short. The AI named itself Benjamin. Knowing that an AI wrote Sunspring makes the movie more fun to watch, especially once you know how the cast and crew put it together. Director Oscar Sharp made the movie for Sci-Fi London, an annual film festival that includes the 48-Hour Film Challenge, where contestants are given a set of prompts (mostly props and lines) that have to appear in a movie they make over the next two days. Sharp's longtime collaborator, Ross Goodwin, is an AI researcher at New York University, and he supplied the movie's AI writer, initially called Jetson. As the cast gathered around a tiny printer, Benjamin spat out the screenplay, complete with almost impossible stage directions like "He is standing in the stars and sitting on the floor."

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,

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.