About a year ago, the idea began creeping into executive discussions and podcast interviews that "enterprise content management" just doesn't cut it for describing what is possible and happening with today's technologies and approaches. Indeed, just as old-school handles like "document management, "imaging" or "the paperless office" eventually gave way to newer concepts and designations, is it possible our old friend ECM is on the way out of industry favor? In January of this year, Gartner posted a blog post titled, "Death of ECM and the Birth of Content Services" from Research Director Michael Woodbridge. The assertion caused quite a stir… and for good reason. "I have been working with my team to kill off a market definition I have spent the most significant portion of my career serving," said Woodbridge in the post. "ECM is now dead (kaput, finite, an ex-market name), at least in how Gartner defines the market." Gartner is instead advocating for "Content Services" as a replacement construct that includes content, platforms and components. Forrester also chimed in to support the idea, splitting the market into two parts, Transactional Content Services and Business Content Services. But Woodbridge is quick to point out that the change in perspective is what's important, not the terminology. "It is only a definition; however, it articulates a different way of thinking about the problem that can be liberating for organizations paralyzed by the apparent need for consolidation." AIIM International stepped forward recently to propose the term "Intelligent Information Management" (IIM) as a suitable replacement. In his e-book "The Next Wave: Moving from ECM to Intelligent Information Management," John Mancini, Chief Evangelist at AIIM, puts it this way: "The role we expect content and information management to play in our organizations is clearly more than traditional data-centric ECM, and it is clearly more than Content Services.

Let me start this week by recounting a joke I once read in the Reader's Digest, I think it was.

When shooting in low light, you need to either shoot at long exposures on a tripod or crank up the ISO if you want to shoot handheld. A group of researchers at the University of Illinois Urbana-Champaign and Intel are bringing the best of both worlds. They've trained AI to process low-light images so they're much cleaner and more usable than grainy photos where ISO is too high. This tech could let you shoot at faster shutter speeds and lower ISO and still get with sharp, clear photos. When captured at short exposure times, the photos would appear almost black.

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.