At Netflix, the Content Platform Engineering and Global Product Creative teams know that imagery plays an incredibly important role in how viewers find new shows and movies to watch. We take pride in surfacing the unique elements of a story that connect our audiences to diverse characters and story lines. As our Original content slate continues to expand, our technical experts are tasked with finding new ways to scale our resources and alleviate our creatives from the tedious and ever-increasing demands of digital merchandising. One of the ways in which we do this is by harvesting static image frames directly from our source videos to provide a more flexible source of raw artwork. Merchandising stills are static video frames taken directly from the source video content used to broaden the reach of a title on the Netflix service.

On Articial Intelligence Day on 13 December 2017, 600 artificial intelligence experts and enthusiasts gathered in Dipoli, Aalto University's newly-renovated main building. The organiser, the new Finnish Center for Artificial Intelligence FCAI established by Aalto University and the University of Helsinki, wished to promote matchmaking, information sharing and cross-border collaboration with the event. Representatives of over 180 companies were offered matchmaking opportunities during pitching, demo and poster sessions. One of the large Finnish companies present was Elisa, who's vice president in business development Kimmo Pentikäinen met up with Samuel Kaski, Professor at Aalto University and Head of FCAI, in the AI Day networking area. They discussed the needs of Elisa as an eager partner for research institutions.

"Automated machine learning promised data scientists a better, faster way to build models, but the reality never matched the hype," said Dr. Ingo Mierswa, founder and president of RapidMiner. When I looked closely at automated machine learning solutions, I found them to be black boxes. They restricted my ability as a data scientist to understand how the models worked and tune them when necessary. We built Auto Model on top of RapidMiner Studio to improve the productivity of data scientists without hiding the ability to understand how and why a model works. As data scientists need to tune or tweak models, they have the full power of the RapidMiner Studio visual workflow designer at their disposal." RapidMiner Auto Model accelerates the entire data science lifecycle using automated machine learning.

Apple has now confirmed that its HomePod smart speaker won't come with manual EQ adjustment options. This means users will not have the ability to manually alter the sound of the device to match their bass or treble preference. Apple Senior VP of Internet Software and Services Eddy Cue announced Wednesday during a Pollstar Live conference that the highly anticipated HomePod won't make it possible for users to make EQ adjustments on their own. In the absence of manual adjustment controls, the Amazon Echo rival will rely on analytics to automatically set levels for each song that is played, according to Apple Insider. The announcement could be upsetting for audiophiles and people who prefer to make manual adjustments to their music.

Several top web firms are taking action against users who spread fake porn videos of celebrities on their sites, also known as'deepfakes.' Pornhub, Twitter and Reddit have all banned the AI-generated porn from being posted on their platforms, saying it falls under the guidelines of non-consensual porn, which violates site-wide policies. The moves come after deepfakes videos have popped up all over the internet. Star Wars lead Daisy Ridley has been featured in a fake video on the Reddit thread (shown above). Fueling the fire was a user-created application, called'FakeApp,' which lets users create their own deepfakes videos via an easy-to-use app that can be downloaded directly to your desktop computer. The deepfakes phenomenon got its start on Reddit last December, when a user community called'deepfakes' began posting the fake porn videos.

Reddit has banned "fake porn" - imagery and videos that superimpose a subject's face over an explicit photo or video without the person's permission. The move follows the development of artificial intelligence software that made it relatively easy to create clips featuring computer-generated versions of celebrities' faces. Reddit had become one of the most popular places to share and discuss so-called deepfake videos. The discussion site had been under growing pressure to act after other platforms - including Twitter, Gfycat and Pornhub - introduced their own deepfake bans. However, it may cause unease among some Reddit users who already feared the platform was becoming less "open" after its closure of two alt-right forums in 2017. Deepfakes involve the use of artificial intelligence software to create a computer-generated version of a subject's face that closely matches the original expressions of another person in a video.

The use of tempting and often misleading headlines (clickbait) to allure readers has become a growing practice nowadays among the media outlets. The widespread use of clickbait risks the reader's trust in media. In this paper, we present BaitBuster, a browser extension and social bot based framework, that detects clickbaits floating on the web, provides brief explanation behind its decision, and regularly makes users aware of potential clickbaits.

Human communication is multimodal and includes elements such as gesture and facial expression along with spoken language. Modern technology makes it feasible to capture all such aspects of communication in natural settings. As a result, similar to fields such as genetics, astronomy and neuroscience, scholars in areas such as linguistics and communication studies are on the verge of a data-driven revolution in their fields. These new approaches require analytical support from machine learning and artificial intelligence to develop tools to help process the vast data repositories. The Distributed Little Red Hen Lab project is an international team of interdisciplinary researchers building a large-scale infrastructure for data-driven multimodal communications research. In this paper, we describe a machine learning system developed to automatically annotate a large database of television program videos as part of this project. The annotations mark regions where people or speakers are on screen along with body part motions including head, hand and shoulder motion. We also annotate a specific class of gestures known as timeline gestures. An existing gesture annotation tool, ELAN, can be used with these annotations to quickly locate gestures of interest. Finally, we provide an update mechanism for the system based on human feedback. We empirically evaluate the accuracy of the system as well as present data from pilot human studies to show its effectiveness at aiding gesture scholars in their work.

Movies provide us with a mass of visual content as well as attracting stories. Existing methods have illustrated that understanding movie stories through only visual content is still a hard problem. In this paper, for answering questions about movies, we put forward a Layered Memory Network (LMN) that represents frame-level and clip-level movie content by the Static Word Memory module and the Dynamic Subtitle Memory module, respectively. Particularly, we firstly extract words and sentences from the training movie subtitles. Then the hierarchically formed movie representations, which are learned from LMN, not only encode the correspondence between words and visual content inside frames, but also encode the temporal alignment between sentences and frames inside movie clips. We also extend our LMN model into three variant frameworks to illustrate the good extendable capabilities. We conduct extensive experiments on the MovieQA dataset. With only visual content as inputs, LMN with frame-level representation obtains a large performance improvement. When incorporating subtitles into LMN to form the clip-level representation, we achieve the state-of-the-art performance on the online evaluation task of 'Video+Subtitles'. The good performance successfully demonstrates that the proposed framework of LMN is effective and the hierarchically formed movie representations have good potential for the applications of movie question answering.

Statistical relational AI (StarAI) aims at reasoning and learning in noisy domains described in terms of objects and relationships by combining probability with first-order logic. With huge advances in deep learning in the current years, combining deep networks with first-order logic has been the focus of several recent studies. Many of the existing attempts, however, only focus on relations and ignore object properties. The attempts that do consider object properties are limited in terms of modelling power or scalability. In this paper, we develop relational neural networks (RelNNs) by adding hidden layers to relational logistic regression (the relational counterpart of logistic regression). We learn latent properties for objects both directly and through general rules. Back-propagation is used for training these models. A modular, layer-wise architecture facilitates utilizing the techniques developed within deep learning community to our architecture. Initial experiments on eight tasks over three real-world datasets show that RelNNs are promising models for relational learning.