This means that galaxies had to be closer to each other in the past. In 1964, Wilson and Penzias discovered the cosmic background radiation, which is a like a fossil of radiation emitted during the beginning of the universe, when it was hot and dense. The cosmic background radiation is observable everywhere in the universe. The composition of the universe - that is, the the number of atoms of different elements - is consistent with the Big Bang Theory. So far, this theory is the only one that can explain why we observe an abundance of primordial elements in the universe.

Television is often seen as a site for subcultural identification and subversive fantasy, including in queer cultures. How might we measure subversion, or the degree to which the depiction of social relationship between a dyad (e.g. two characters who are colleagues) deviates from its typical representation on TV? To explore this question, we introduce the task of stereotypic relationship extraction. Built on cognitive stylistics, linguistic anthropology, and dialogue relation extraction, in this paper, we attempt to model the cognitive process of stereotyping TV characters in dialogic interactions. Given a dyad, we want to predict: what social relationship do the speakers exhibit through their words? Subversion is then characterized by the discrepancy between the distribution of the model's predictions and the ground truth labels. To demonstrate the usefulness of this task and gesture at a methodological intervention, we enclose four case studies to characterize the representation of queer relationalities in the Big Bang Theory, Frasier, and Gilmore Girls, as we explore the suspicious and reparative modes of reading with our computational methods.

Never mind that it can pass the bar exam, ace medical tests and read bedtime stories with emotion, artificial intelligence will never match the marvel of the human mind without first mastering the art of sarcasm. But that art, it seems, may be next on the list of the technology's dizzying capabilities. Researchers in the Netherlands have built an AI-driven sarcasm detector that can spot when the lowest form of wit, and the highest form of intelligence, is being deployed. "We are able to recognise sarcasm in a reliable way, and we're eager to grow that," said Matt Coler at the University of Groningen's speech technology lab. "We want to see how far we can push it."

ChatGPT is an amazing Chatbot with the ability, competency, and confidence to construct paragraphs, sentences, essays, and more. However, this optimism can be deceiving because it succumbs to several common misconceptions among the general public, even though experts know much better. Can an astrophysicist teach ChatGPT to acquire new information and absorb it such that it gives exact responses when it previously gave confident but mistaken ones? When we try to understand anything more intensely, we all end up in an awkward situation: we believe we know how something performs, only to explore that we are wrong. This entails not just learning what is true, but also why what we assumed was true was, in actuality, false, and how we can avoid making the same mistakes again.

Neural conversational models have long suffered from the problem of inconsistency and lacking coherent personality. To address the issue, persona-based models capturing individual characteristics have been proposed, but they still face the dilemma of model adaption and data privacy. To break this dilemma, we propose a novel Federated Natural Language Generation (FedNLG) framework, which learns personalized representations from various dataset on distributed devices, and thus implements the personalized dialogue system efficiently and safely. FedNLG first pre-trains parameters of standard neural conversational model over a large dialogue corpus, and then fine-tune the model parameters and persona embeddings on specific datasets, in a federated manner. Thus, the model could simultaneously learn the persona embeddings in local clients and learn shared model parameters by federated aggregation, which achieves accuracyprivacy balance. By conducting extensive experiments, we demonstrate the effectiveness of our model by pre-training model over Cornell Movie-Dialogs Corpus and fine-tuning the model over two TV series dataset.

Television is an ever-evolving multi billion dollar industry. The success of a television show in an increasingly technological society is a vast multi-variable formula. The art of success is not just something that happens, but is studied, replicated, and applied. Hollywood can be unpredictable regarding success, as many movies and sitcoms that are hyped up and promise to be a hit end up being box office failures and complete disappointments. In current studies, linguistic exploration is being performed on the relationship between Television series and target community of viewers. Having a decision support system that can display sound and predictable results would be needed to build confidence in the investment of a new TV series. The models presented in this study use data to study and determine what makes a sitcom successful. In this paper, we use descriptive and predictive modeling techniques to assess the continuing success of television comedies: The Office, Big Bang Theory, Arrested Development, Scrubs, and South Park. The factors that are tested for statistical significance on episode ratings are character presence, director, and writer. These statistics show that while characters are indeed crucial to the shows themselves, the creation and direction of the shows pose implication upon the ratings and therefore the success of the shows. We use machine learning based forecasting models to accurately predict the success of shows. The models represent a baseline to understanding the success of a television show and how producers can increase the success of current television shows or utilize this data in the creation of future shows. Due to the many factors that go into a series, the empirical analysis in this work shows that there is no one-fits-all model to forecast the rating or success of a television show.

Adding canned laughter to a punchline increases how funny people find a joke, a new study suggests. The research indicates that artificial laughter - often used in shows such as Alan Partridge, Friends and The Big Bang Theory - makes people rate low-quality jokes as funnier than they actually are. However, spontaneous laughter was deemed more effective than pre-planned. Scientists from University College London said the findings also held up in those diagnosed with autism. In the study, 40 groan-worthy gags were given a baseline humour rating of between one - not funny - and seven - hilarious.

Remember the Jetsons' robot maid, Rosie? Massachusetts Institute of Technology researchers think her future real-life incarnations can learn a thing or two from Steve Carell and other sitcom stars. MIT says a computer that binge-watched YouTube videos and TV shows such as The Office, Big Bang Theory and Desperate Housewives learned how to predict whether the actors were about to hug, kiss, shake hands or slap high fives -- advances that eventually could help the next generation of artificial intelligence function less clumsily. "It could help a robot move more fluidly through your living space," lead researcher Carl Vondrick told The Associated Press in an interview. "The robot won't want to start pouring milk if it thinks you're about to pull the glass away."