ChatGPT is artificial intelligence that writes for you, any kind of writing you like – letters, song lyrics, research papers, recipes, therapy sessions, poems, essays, outlines, even software code. And despite its clunky name (GPT stands for Generative Pre-trained Transformer), within five days of its launch, more than a million people were using it. How easy is it to use? Try typing in, "Write a limerick about the effect of AI on humanity." Or how about, "Tell the Goldilocks story in the style of the King James Bible." Microsoft has announced it will build the program into Microsoft Word. The first books written by ChatGPT have already been published.

'Woman reading book, under a night sky, dreamy atmosphere," I type into Deep Dream Generator's Text 2 Dream feature. In less than a minute, an image is returned to me showing what I've described. Welcome to the world of AI image generation, where you can create what on the surface looks like top-notch artwork using just a few text prompts, even if in reality your skills don't go beyond drawing stick figures. AI image generation seems to be everywhere: on TikTok, the popular AI Manga filter shows you what you look like in the Japanese comic style, while people in their droves are using it to create images for everything from company logos to picture books. It's already been used by one major publisher: sci-fi imprint Tor discovered that a cover it had created had used a licensed image created by AI, but decided to go ahead anyway "due to production constraints". The biggest players in AI include companies such as MidJourney, Stable Diffusion and Deep Dream Generator (DDG). They're free to use, up to a point, making them attractive to those just wanting to try them out. There's no denying that they're fun, but closer examination of the images they produce shows oddities. The face of the woman in my image has very odd features, and appears to be holding multiple books. The images also have a similarly polished, somewhat kitsch aesthetic. And, while there's an initial thrill at seeing an image appear, there's no creative satisfaction. The implications of AI image generation are far-reaching and could impact everything from film to graphic novels and more. Children's illustrators were quick to raise concerns about the technology on social media. Among them is author and illustrator Rob Biddulph, who says that AI-generated art "is the exact opposite of what I believe art to be.

Silence is the key word here. Remember those zombies Ellie mentioned that use echolocation? When the group makes it to the second floor, the ceiling caves in behind them, obstructing their way out. The commotion also attracts two zombies; Joel signals to Ellie that these infected can't see, and move around based on sound. At a certain point, Ellie and Tess split up, and the attention focuses back to Joel, who regroups with Ellie.

"How can we animate 3D-characters from a movie script or move robots by simply telling them what we would like them to do?" "How unstructured and complex can we make a sentence and still generate plausible movements from it?" These are questions that need to be answered in the long-run, as the field is still in its infancy. Inspired by these problems, we present a new technique for generating compositional actions, which handles complex input sentences. Our output is a 3D pose sequence depicting the actions in the input sentence. We propose a hierarchical two-stream sequential model to explore a finer joint-level mapping between natural language sentences and 3D pose sequences corresponding to the given motion. We learn two manifold representations of the motion -- one each for the upper body and the lower body movements. Our model can generate plausible pose sequences for short sentences describing single actions as well as long compositional sentences describing multiple sequential and superimposed actions. We evaluate our proposed model on the publicly available KIT Motion-Language Dataset containing 3D pose data with human-annotated sentences. Experimental results show that our model advances the state-of-the-art on text-based motion synthesis in objective evaluations by a margin of 50%. Qualitative evaluations based on a user study indicate that our synthesized motions are perceived to be the closest to the ground-truth motion captures for both short and compositional sentences.

Artificial intelligence (AI) technologies are widely deployed in smartphone photography; and prompt-based image synthesis models have rapidly become commonplace. In this paper, we describe a Research-through-Design (RtD) project which explores this shift in the means and modes of image production via the creation and use of the Entoptic Field Camera. Entoptic phenomena usually refer to perceptions of floaters or bright blue dots stemming from the physiological interplay of the eye and brain. We use the term entoptic as a metaphor to investigate how the material interplay of data and models in AI technologies shapes human experiences of reality. Through our case study using first-person design and a field study, we offer implications for critical, reflective, more-than-human and ludic design to engage AI technologies; the conceptualisation of an RtD research space which contributes to AI literacy discourses; and outline a research trajectory concerning materiality and design affordances of AI technologies.

Mention detection is an important component of coreference resolution system, where mentions such as name, nominal, and pronominals are identified. These mentions can be purely coreferential mentions or singleton mentions (non-coreferential mentions). Coreferential mentions are those mentions in a text that refer to the same entities in a real world. Whereas, singleton mentions are mentioned only once in the text and do not participate in the coreference as they are not mentioned again in the following text. Filtering of these singleton mentions can substantially improve the performance of a coreference resolution process. This paper proposes a singleton mention detection module based on a fully connected network and a Convolutional neural network for Hindi text. This model utilizes a few hand-crafted features and context information, and word embedding for words. The coreference annotated Hindi dataset comprising of 3.6K sentences, and 78K tokens are used for the task. In terms of Precision, Recall, and F-measure, the experimental findings obtained are excellent.

NBCUniversal owns and operates over 20 different businesses across 30 countries including a valuable portfolio of news and entertainment television networks, a premier motion picture company, significant television production operations, a leading television stations group, world-renowned theme parks and a premium ad-supported streaming service. Here you can be your authentic self. As a company uniquely positioned to educate, entertain and empower through our platforms, Comcast NBCUniversal stands for including everyone. We strive to foster a diverse and inclusive culture where our employees feel supported, embraced and heard. We believe that our workforce should represent the communities we live in, so that together, we can continue to create and deliver content that reflects the current and ever-changing face of the world.

After a long hiatus, "Saturday Night Live" returned with guest host Aubrey Plaza and musical guest Sam Smith. In the 10th episode of Season 48, the NBC sketch comedy show wasted no time in mocking congressman George Santos, the embattled New York Republican who for weeks has generated headlines over false statements about his background. The episode also featured cameos from President Joe Biden and former cast member Amy Poehler. Other cameos included actress Allison Williams, along with Jonathan and Drew Scott, otherwise known as the "Property Brothers," as well as skateboard legend Tony Hawk. German singer Kim Petras joined Smith in the first musical segment and actress Sharon Stone appeared in Smith's second performance.

We propose a deep attention-based alignment network, which aims to automatically predict lyrics and melody with given incomplete lyrics as input in a way similar to the music creation of humans. Most importantly, a deep neural lyrics-to-melody net is trained in an encoder-decoder way to predict possible pairs of lyrics-melody when given incomplete lyrics (few keywords). The attention mechanism is exploited to align the predicted lyrics with the melody during the lyrics-to-melody generation. The qualitative and quantitative evaluation metrics reveal that the proposed method is indeed capable of generating proper lyrics and corresponding melody for composing new songs given a piece of incomplete seed lyrics.

Bidirectional Transformer architectures are state-of-the-art sequential recommendation models that use a bi-directional representation capacity based on the Cloze task, a.k.a. Masked Language Modeling. The latter aims to predict randomly masked items within the sequence. Because they assume that the true interacted item is the most relevant one, an exposure bias results, where non-interacted items with low exposure propensities are assumed to be irrelevant. The most common approach to mitigating exposure bias in recommendation has been Inverse Propensity Scoring (IPS), which consists of down-weighting the interacted predictions in the loss function in proportion to their propensities of exposure, yielding a theoretically unbiased learning. In this work, we argue and prove that IPS does not extend to sequential recommendation because it fails to account for the temporal nature of the problem. We then propose a novel propensity scoring mechanism, which can theoretically debias the Cloze task in sequential recommendation. Finally we empirically demonstrate the debiasing capabilities of our proposed approach and its robustness to the severity of exposure bias.