Do androids dream of electric sheep? Sure, if the sheep graze in the Westworld theme park. This season, the HBO show's main titles designer, Patrick Clair, recruited an A.I. expert, A.I. Fiction's Dr. Pinar Yanardag, to connect him with that dreaming android, in this case, with a generative adversarial network (GAN). Yanardag and her team had used neural networks for all sorts of purposes -- to create nightmares, horror, graffiti, music, fashion, perfume, cocktails, pizza, and even chocolate. "My mind was blown by the kinds of things she's doing, combining creativity with A.I.," Clair told SYFY WIRE, so he thought, why not let a neural network try television next?

Music generation is always interesting in a sense that there is no formalized recipe. In this work, we propose a novel dual-track architecture for generating classical piano music, which is able to model the inter-dependency of left-hand and right-hand piano music. Particularly, we experimented with a lot of different models of neural network as well as different representations of music, and the results show that our proposed model outperforms all other tested methods. Besides, we deployed some special policies for model training and generation, which contributed to the model performance remarkably. Finally, under two evaluation methods, we compared our models with the MuseGAN project and true music.

Distinguishing between misinformation and real information is one of the most challenging problems in today's interconnected world. The vast majority of the state-of-the-art in detecting misinformation is fully supervised, requiring a large number of high-quality human annotations. However, the availability of such annotations cannot be taken for granted, since it is very costly, time-consuming, and challenging to do so in a way that keeps up with the proliferation of misinformation. In this work, we are interested in exploring scenarios where the number of annotations is limited. In such scenarios, we investigate how tapping on a diverse number of resources that characterize a news article, henceforth referred to as "aspects" can compensate for the lack of labels. In particular, our contributions in this paper are twofold: 1) We propose the use of three different aspects: article content, context of social sharing behaviors, and host website/domain features, and 2) We introduce a principled tensor based embedding framework that combines all those aspects effectively. We propose HiJoD a 2-level decomposition pipeline which not only outperforms state-of-the-art methods with F1-scores of 74% and 81% on Twitter and Politifact datasets respectively but also is an order of magnitude faster than similar ensemble approaches.

Benjamin Lee, a second-year Ph.D. student in the Allen School's Artificial Intelligence group working with professor Daniel Weld, has been named a 2020 Innovator in Residence by the Library of Congress. Now in its second year, the Innovator in Residence program aims to enlist artists, researchers, journalists, and others in developing new and creative ways of using the library's digital collections. During his residency, Lee will apply deep learning to enable the automatic extraction and tagging of photographs and illustrations contained in the more than 15 million newspaper scans comprising the library's Chronicling America collection. His goal is to produce interactive visualizations, searchable by topic, that will make the content more accessible to users and support cultural heritage research. "A primary motivation behind my project is to excite the American public by demonstrating the possibilities of applying machine learning to library collections," Lee explained in an interview posted on the library's blog.

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.