The early waves of artificial intelligence are already lapping at the shores of local TV news. For now, AI is largely confined to the realm of metadata and closed captioning among larger, international media companies, according to Ethan Dreilinger, solutions engineer for Watson Media and IBM Cloud Video. But the next wave could see more AI functionality -- and affordability -- soon enough. Dreilinger told NAB Show attendees here on Sunday that Watson's primary role at present is retrieving metadata within a media organization's video -- essentially a search and discovery operation. Say, for instance, tags have been misspelled in videos long languishing in a media company's asset management system.

Matrix completion is one of the key problems in signal processing and machine learning, with applications ranging from image pro- cessing and data gathering to classification and recommender sys- tems. Recently, deep neural networks have been proposed as la- tent factor models for matrix completion and have achieved state- of-the-art performance. Nevertheless, a major problem with existing neural-network-based models is their limited capabilities to extend to samples unavailable at the training stage. In this paper, we propose a deep two-branch neural network model for matrix completion. The proposed model not only inherits the predictive power of neural net- works, but is also capable of extending to partially observed samples outside the training set, without the need of retraining or fine-tuning. Experimental studies on popular movie rating datasets prove the ef- fectiveness of our model compared to the state of the art, in terms of both accuracy and extendability.

Explainable Recommendation refers to the personalized recommendation algorithms that address the problem of why -- they not only provide the user with the recommendations, but also make the user aware why such items are recommended by generating recommendation explanations, which help to improve the effectiveness, efficiency, persuasiveness, and user satisfaction of recommender systems. In recent years, a large number of explainable recommendation approaches -- especially model-based explainable recommendation algorithms -- have been proposed and adopted in real-world systems. In this survey, we review the work on explainable recommendation that has been published in or before the year of 2018. We first high-light the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W, i.e., what, when, who, where, and why. We then conduct a comprehensive survey of explainable recommendation itself in terms of three aspects: 1) We provide a chronological research line of explanations in recommender systems, including the user study approaches in the early years, as well as the more recent model-based approaches. 2) We provide a taxonomy for explainable recommendation algorithms, including user-based, item-based, model-based, and post-model explanations. 3) We summarize the application of explainable recommendation in different recommendation tasks, including product recommendation, social recommendation, POI recommendation, etc. We devote a chapter to discuss the explanation perspectives in the broader IR and machine learning settings, as well as their relationship with explainable recommendation research. We end the survey by discussing potential future research directions to promote the explainable recommendation research area.

There is no editorial team at Google News. There is no building filled with hundreds of moderators monitoring the thousands of stories hitting the web every second, making sure the full story is presented. Instead, Google uses artificial intelligence algorithms, as well as its partnerships with fact-checking organizations providing headlines from credible, authoritative sources. "Humans are generating the content," Trystan Upstill, Google News engineering and product lead, told Digital Trends. "We think of the whole app as a way to use artificial intelligence to bring forward the best in human intelligence. In a way, the A.I. is controlling this fire hose of human stuff going on."

Three buzzwords you have likely seen in the media lately.

This should be a good time to catch up on all the news from Microsoft Build and Google I/O, as well as sort out your feelings about advances surrounding DNA, AI and robots. Time to start training.The dog-like SpotMini robot will go on sale next year Boston Dynamics President Marc Raibert said that the SpotMini robot would go on sale next year. The company has some 10 prototypes of the quadruped already, with a plan to build 100 by the end of the year as it tried to increase production before the bot goes on sale in 2019. It's a surprisingly strong start.Life with the Android P beta has been (mostly) painless It's early, but Chris Velazco calls this "the most thorough update to the Android experience in years." He's spent three days living with Android P and its webOS-like app switcher just so you can get an idea of what it's like, so dive in.

When director Stanley Kubrick released 2001: A Space Odyssey 50 years ago, he intended it to be a "nonverbal experience." The movie's dialogue was sparse, and it relied heavily on visuals and score. It was, Kubrick told Playboy in 1968, a subjective film, meant to reach audiences "at an inner level of consciousness, just as music does." The same cannot be said of its 1976 comic book adaptation. Marvel's oversized Treasury Edition, which was written, drawn, and edited by the late Jack Kirby--legendary co-creator of Captain America, the X-Men, Black Panther, and dozens of others--left little up to interpretation.

Just because Netflix's films got shut out of competition at Cannes doesn't mean the streaming titan stayed home. The company won a worldwide (except for China) distribution deal worth $30 million for the animated film Next Gen with a star-studded cast about a pair of "two unlikely friends in a world filled with robots," according to Deadline. Not much else is known about the film, although Deadline surmised it's one of the biggest deals to come out of the film festival thus far. The voice cast includes Charlyne Yi, Michael Peña, Constance Wu, Jason Sudeikis and David Cross. It's unclear when Netflix will distribute the film, but we know when it's coming out in China: Alibaba and Wanda, which won rights to distribute in the country, will give it a wide release this summer.