The music streaming service Deezer is developing an AI tool to help determine whether new songs added to its library should be flagged as explicit. The new tool is still in development, but company executives hope it will help them sort through the up to 40,000 new tracks it receives every day from record companies, most of which are unlabeled. Once finalized, the tool won't automatically label tracks, but instead flag tracks for review, which will be conducted by one of the company's executives. 'When it comes to figuring out what explicit lyrics are, there is no general consensus,' Deezer's Manuel Moussallam wrote in a blog post outlining the new project. 'It's obviously a cultural issue, with lots of considerations about the intended audience and the listening context.' 'As is the case with movies, the primary objective of tagging a piece as "explicit" is to provide guidance to determine how suitable it is for an intended audience.'

"Deepfakes" are digitally altered images that make incidents appear real when they are not. Such altered files could have broad implications for politics. "Deepfakes" are digitally altered images that make incidents appear real when they are not. Such altered files could have broad implications for politics. Sophisticated fake media hasn't emerged as a factor in the disinformation wars in the ways once feared -- and two specialists say it may have missed its moment.

"For AI and Deep Learning applications, that simply will not do anymore.

"For AI and Deep Learning applications, that simply will not do anymore.

Deep learning is an intense machine learning tool that indicates extraordinary execution in numerous fields.

Results of a new study involving machine learning can help align patient and surgeon expectations for clinical improvement after surgery.

Up Your Game: The Mavic Air 2 camera drone takes power and portability to the next level. It combines a powerful camera with intelligent shooting modes for stunning results. Push your imagination to its limits because aerial photography has never been this easy. Next-Level Content: Capture impressive 48MP photos with a 1/2-inch CMOS sensor while the 3-axis gimbal provides 4K/60fps video. The secret to incredible HDR video is a high-performance Quad Bayer image sensor.

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.

Despite recent progress on computer vision and natural language processing, developing video understanding intelligence is still hard to achieve due to the intrinsic difficulty of story in video. Moreover, there is not a theoretical metric for evaluating the degree of video understanding. In this paper, we propose a novel video question answering (Video QA) task, DramaQA, for a comprehensive understanding of the video story. The DramaQA focused on two perspectives: 1) hierarchical QAs as an evaluation metric based on the cognitive developmental stages of human intelligence. 2) character-centered video annotations to model local coherence of the story. Our dataset is built upon the TV drama "Another Miss Oh" and it contains 16,191 QA pairs from 23,928 various length video clips, with each QA pair belonging to one of four difficulty levels. We provide 217,308 annotated images with rich character-centered annotations, including visual bounding boxes, behaviors, and emotions of main characters, and coreference resolved scripts. Additionally, we provide analyses of the dataset as well as Dual Matching Multistream model which effectively learns character-centered representations of video to answer questions about the video. We are planning to release our dataset and model publicly for research purposes and expect that our work will provide a new perspective on video story understanding research.

Welcome to the Newspaper Navigator dataset! This dataset consists of extracted visual content for 16,358,041 historic newspaper pages in Chronicling America. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the Beyond Words crowdsourcing project. The dataset also includes text corresponding to the visual content, identified by extracting the Optical Character Recognition, or OCR, within each predicted bounding box. For example, if the visual content recognition model predicted a bounding box around a headline, the corresponding textual content provides a machine-readable version of the headline; likewise, for a photograph, illustration, or map, this textual representation often contains the title and caption.