Recently the Manufacturing Media Consortium and its founder, TR Cutler (TRC) set up a new division examining the role of manufacturing and technology in the entertainment industry. What follows is an interview of Jacob Kyle Young (JKY) actor and founder of Post Modern Entertainment. In an interview with Jacob Kyle Young (JKY), actor and founder of Post Modern Entertainment, TR Cutler describes how virtual reality, machine learning, and other industrial automation is being applied in the entertainment industry. Virtual reality (VR) is projected to be the rapidly growing segment in the media and entertainment space, according to Global Entertainment, which reported that 68 million VR headsets would be sold in the USA before the end of 2021, raising VR content revenue to $ 5.0 billion. During COVID, it became evident that using AI can help create VR interactive content. Using AI techniques, the entertainment industry creates remarkable scenes with a pair of goggles.

We began to speak and we learnt to express. We began to get curious, and we uncovered the mysteries of the universe. We climbed the food chain, we proclaimed our superiority. But some thirsts are insatiable. To match our own intelligence and surpass the pace, we crafted Artificial Intelligence. From self-driving cars to smart voice assistants, face recognition to endless Newsfeed at Facebook, AI defeating former Go champion Lee Se-dol to Netflix movie recommendations; we have awed in the possibilities of AI.

Billie Eilish dropped behind-the-scenes footage from her special concert, "Happier than Ever: A Love Letter to Los Angeles," detailing how she made an animated segment for the event. The concert premiered on Disney Plus on Sept. 3. The 19-year-old singer took to Instagram Story on Sunday to share an impressive video that she recorded on June 25. During the special concert, there were animated sequences shown for a brief period to convey images like bleeding and driving a car. In the video, the singer could be seen wearing a black and red motion capture suit and taking a selfie video to flaunt the entire setup.

Music artists can find inspiration and new creative directions for their song writing with technology developed by Waterloo researchers. LyricJam, a real-time system that uses artificial intelligence (AI) to generate lyric lines for live instrumental music, was created by members of the University's Natural Language Processing Lab. The lab, led by Olga Vechtomova, a Waterloo Engineering professor cross-appointed in Computer Science, has been researching creative applications of AI for several years. The lab's initial work led to the creation of a system that learns musical expressions of artists and generates lyrics in their style. Recently, Vechtomova, along with Waterloo graduate students Gaurav Sahu and Dhruv Kumar, developed technology that relies on various aspects of music such as chord progressions, tempo and instrumentation to synthesize lyrics reflecting the mood and emotions expressed by live music.

In 2021, technology's role in how art is generated remains up for debate and discovery. From the rise of NFTs to the proliferation of techno-artists who use generative adversarial networks to produce visual expressions, to smartphone apps that write new music, creatives and technologists are continually experimenting with how art is produced, consumed, and monetized. BT, the Grammy-nominated composer of 2010's These Hopeful Machines, has emerged as a world leader at the intersection of tech and music. Beyond producing and writing for the likes of David Bowie, Death Cab for Cutie, Madonna, and the Roots, and composing scores for The Fast and the Furious, Smallville, and many other shows and movies, he's helped pioneer production techniques like stutter editing and granular synthesis. This past spring, BT released GENESIS.JSON, a piece of software that contains 24 hours of original music and visual art.

We train an object detector built from convolutional neural networks to count interference fringes in elliptical antinode regions in frames of high-speed video recordings of transient oscillations in Caribbean steelpan drums illuminated by electronic speckle pattern interferometry (ESPI). The annotations provided by our model aim to contribute to the understanding of time-dependent behavior in such drums by tracking the development of sympathetic vibration modes. The system is trained on a dataset of crowdsourced human-annotated images obtained from the Zooniverse Steelpan Vibrations Project. Due to the small number of human-annotated images and the ambiguity of the annotation task, we also evaluate the model on a large corpus of synthetic images whose properties have been matched to the real images by style transfer using a Generative Adversarial Network. Applying the model to thousands of unlabeled video frames, we measure oscillations consistent with audio recordings of these drum strikes. One unanticipated result is that sympathetic oscillations of higher-octave notes significantly precede the rise in sound intensity of the corresponding second harmonic tones; the mechanism responsible for this remains unidentified. This paper primarily concerns the development of the predictive model; further exploration of the steelpan images and deeper physical insights await its further application.

A key component of mathematical reasoning is the ability to formulate interesting conjectures about a problem domain at hand. In this paper, we give a brief overview of a theory exploration system called QuickSpec, which is able to automatically discover interesting conjectures about a given set of functions. QuickSpec works by interleaving term generation with random testing to form candidate conjectures. This is made tractable by starting from small sizes and ensuring that only terms that are irreducible with respect to already discovered conjectures are considered. QuickSpec has been successfully applied to generate lemmas for automated inductive theorem proving as well as to generate specifications of functional programs. We give an overview of typical use-cases of QuickSpec, as well as demonstrating how to easily connect it to a theorem prover of the user's choice.

In this paper, we present our Alexa Prize Grand Challenge 4 socialbot: Proto. Leveraging diverse sources of world knowledge, and powered by a suite of neural and rule-based natural language understanding modules, state-of-the-art neural generators, novel state-based deterministic generators, an ensemble of neural re-rankers, a robust post-processing algorithm, and an efficient overall conversation strategy, Proto strives to be able to converse coherently about a diverse range of topics of interest to humans, and provide a memorable experience to the user. In this paper we dissect and analyze the different components and conversation strategies implemented by our socialbot, which enables us to generate colloquial, empathetic, engaging, self-rectifying, factually correct, and on-topic response, which has helped us achieve consistent scores throughout the competition.

Users of social networks tend to post and share content with little restraint. Hence, rumors and fake news can quickly spread on a huge scale. This may pose a threat to the credibility of social media and can cause serious consequences in real life. Therefore, the task of rumor detection and verification has become extremely important. Assessing the veracity of a social media message (e.g., by fact checkers) involves analyzing the text of the message, its context and any multimedia attachment. This is a very time-consuming task that can be much helped by machine learning. In the literature, most message veracity verification methods only exploit textual contents and metadata. Very few take both textual and visual contents, and more particularly images, into account. In this paper, we second the hypothesis that exploiting all of the components of a social media post enhances the accuracy of veracity detection. To further the state of the art, we first propose using a set of advanced image features that are inspired from the field of image quality assessment, which effectively contributes to rumor detection. These metrics are good indicators for the detection of fake images, even for those generated by advanced techniques like generative adversarial networks (GANs). Then, we introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features (i.e., text, social context, and image features) by supervised machine learning. Such algorithms provide interpretability and explainability in the decisions taken, which we believe is particularly important in the context of rumor verification. Experimental results show that MONITOR can detect rumors with an accuracy of 96% and 89% on the MediaEval benchmark and the FakeNewsNet dataset, respectively. These results are significantly better than those of state-of-the-art machine learning baselines.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Indiana's lackluster performance in its season opener Saturday was foreshadowed by a peculiar mistake off the field -- a spelling mistake that is. The jersey of freshman running back David Holloman read "Indinia," according to a video posted -- and then deleted -- by the Hoosiers' official athletics Twitter account. Viewers took screenshots of the embarrassing error and it quickly made its rounds on the internet.