Fashion-model pose is an important attribute in the fashion industry. Creative directors, modeling production houses, and top photographers always look for professional models able to pose. without the skill to correctly pose, their chances of landing professional modeling employment are regrettably quite little. There are occasions when models and photographers are unsure of the best pose to strike while taking photographs. This research concentrates on suggesting the fashion personnel a series of similar images based on the input image. The image is segmented into different parts and similar images are suggested for the user. This was achieved by calculating the color histogram of the input image and applying the same for all the images in the dataset and comparing the histograms. Synthetic images have become popular to avoid privacy concerns and to overcome the high cost of photoshoots. Hence, this paper also extends the work of generating synthetic images from the recommendation engine using styleGAN to an extent.

A significant share of political discourse occurs online on social media platforms. Policymakers and researchers try to understand the role of social media design in shaping the quality of political discourse around the globe. In the past decades, scholarship on political discourse theory has produced distinct characteristics of different types of prominent political rhetoric such as deliberative, civic, or demagogic discourse. This study investigates the relationship between social media reaction mechanisms (i.e., upvotes, downvotes) and political rhetoric in user discussions by engaging in an in-depth conceptual analysis of political discourse theory. First, we analyze 155 million user comments in 55 political subforums on Reddit between 2010 and 2018 to explore whether users' style of political discussion aligns with the essential components of deliberative, civic, and demagogic discourse. Second, we perform a quantitative study that combines confirmatory factor analysis with difference in differences models to explore whether different reaction mechanism schemes (e.g., upvotes only, upvotes and downvotes, no reaction mechanisms) correspond with political user discussion that is more or less characteristic of deliberative, civic, or demagogic discourse. We produce three main takeaways. First, despite being "ideal constructs of political rhetoric," we find that political discourse theories describe political discussions on Reddit to a large extent. Second, we find that discussions in subforums with only upvotes, or both up- and downvotes are associated with user discourse that is more deliberate and civic. Third, social media discussions are most demagogic in subreddits with no reaction mechanisms at all. These findings offer valuable contributions for ongoing policy discussions on the relationship between social media interface design and respectful political discussion among users.

Content moderation is the process of flagging content based on pre-defined platform rules. There has been a growing need for AI moderators to safeguard users as well as protect the mental health of human moderators from traumatic content. While prior works have focused on identifying hateful/offensive language, they are not adequate for meeting the challenges of content moderation since 1) moderation decisions are based on violation of rules, which subsumes detection of offensive speech, and 2) such rules often differ across communities which entails an adaptive solution. We propose to study the challenges of content moderation by introducing a multilingual dataset of 1.8 Million Reddit comments spanning 56 subreddits in English, German, Spanish and French. We perform extensive experimental analysis to highlight the underlying challenges and suggest related research problems such as cross-lingual transfer, learning under label noise (human biases), transfer of moderation models, and predicting the violated rule. Our dataset and analysis can help better prepare for the challenges and opportunities of auto moderation.

Microsoft has limited the number of "chat turns" you can carry out with Bing's AI chatbot to five per session and 50 per day overall. Each chat turn is a conversation exchange comprised of your question and Bing's response, and you'll be told that the chatbot has hit its limit and will be prompted to start a new topic after five rounds. The company said in its announcement that it's capping Bing's chat experience because lengthy chat sessions tend to "confuse the underlying chat model in the new Bing." Indeed, people have been reporting odd, even disturbing behavior by the chatbot since it became available. New York Times columnist Kevin Roose posted the full transcript of his conversation with the bot, wherein it reportedly said that it wanted to hack into computers and spread propaganda and misinformation.

The novelist Gabrielle Zevin, whose Tomorrow, and Tomorrow, and Tomorrow appeared on many of 2022's books of the year lists, was born in New York in 1977. She studied English at Harvard, where she met her partner, the film director Hans Canosa. Zevin wrote the screenplay for Canosa's 2005 film, Conversations With Other Women, and the pair adapted two of Zevin's novels for the screen, most recently The Storied Life of AJ Fikry. She is working on a film version of Tomorrow, and Tomorrow, and Tomorrow, which follows two childhood friends as they reunite in adulthood to create video games. Hauss is a French visual artist who works with ballpoint pens to create hyperrealistic drawings about pop culture and women's sexuality.

The first time Captain Kirk had a conversation with the ship's computer was in 1966 during Episode 13 of Season 1 in the classic Star Trek series. Calling it a "conversation" is quite generous, for it was really a series of stiff questions from Kirk, each prompting an even stiffer response from the computer. There was no conversational back-and-forth, no questions from the AI asking for elaboration or context. And yet, for the last 57 years, computer scientists have not been able to exceed this stilted 1960s vision of human-machine dialog. Even platforms like Siri and Alexa, created by some of the world's largest companies at great expense have not allowed for anything that feels like real-time natural conversation.

"Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world."1 "A people that no longer can believe anything cannot make up its mind. It is deprived not only of its capacity to act but also of its capacity to think and to judge. And with such a people you can then do what you please."2

The music industry has always been subject to change and innovation, with each new era of music bringing new sounds, trends, and technologies.

As you can see from this interview of Moonlair360, ultimate Content Creator Bro, ChatGPT is the new Tinder. This crazy dude, to whom I happen to be distantly related, has figured out the percentages to respond to every Tinder match with ChatGPT. I use it for news writing. ChatGPT can't replace investigative journalism, but it's perfect for the regurgitated not-so-happy meals put out as news on platforms that shall remain unnamed. They know who they are. I give my new best friend -- ChatGP T-- a topic on a local subject, and watch it spit out content like the coins from the slot machine that one time you hit it big.

The sole aim of this book is to give a self-contained introduction to concepts and mathematical tools in Bayesian matrix decomposition in order to seamlessly introduce matrix decomposition techniques and their applications in subsequent sections. However, we clearly realize our inability to cover all the useful and interesting results concerning Bayesian matrix decomposition and given the paucity of scope to present this discussion, e.g., the separated analysis of variational inference for conducting the optimization. We refer the reader to literature in the field of Bayesian analysis for a more detailed introduction to the related fields. This book is primarily a summary of purpose, significance of important Bayesian matrix decomposition methods, e.g., real-valued decomposition, nonnegative matrix factorization, Bayesian interpolative decomposition, and the origin and complexity of the methods which shed light on their applications. The mathematical prerequisite is a first course in statistics and linear algebra. Other than this modest background, the development is self-contained, with rigorous proof provided throughout.