One of the most captivating concepts in mathematics is now on Netflix.

By now, it will have been difficult to avoid the screenshots of the flirty messages Adam Levine sent to his fans - and the resulting memes. The Maroon 5 frontman is married to model Behati Prinsloo, with whom he has two children, and she is currently pregnant with their third. But, over the last week, five women have come forward accusing 43-year-old Levine of sending them explicit messages in recent years. The first was Instagram model Sumner Stroh, 23, who uploaded a bombshell TikTok video in which she claimed the two had had an affair last year and revealed a slew of flirtatious DMs. Then comedian Maryka and a woman named Alyson Rose shared screenshots of their alleged Instagram conversations with the singer. Levine's former yoga teacher, Alanna Zabel, next accused him of sending a dirty text to her, before fitness influencer Ashley Russell claimed he started messaging her on social media this year.

Artificial intelligence is the process of computer systems that can perform tasks typically that humans perform require a lot of intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. These programs act as humans do. While these programs have risen for years, you do not realize how many AI examples you face daily. Artificial intelligence is the copy of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction.

Some of the biggest names in the world have left us before their time and their fans have been left wondering what could have been today had they not passed away. Now, they can see what those figures would look like if they were still alive in 2022 thanks to an artistic project. Turkish artist Alper Yesiltashas used artificial intelligence to start a new project which he has called "As if nothing happened". "Behind this project lies the question of "how would people look photo-realistically if some great events had not happened to them"," he explains in an introduction to the project. Among those included are Princess Diana, John Lennon, Michael Jackson, Freddie Mercury, Elvis Presley and Tupac.

Chapter generation becomes practical technique for online videos nowadays. The chapter breakpoints enable users to quickly find the parts they want and get the summative annotations. However, there is no public method and dataset for this task. To facilitate the research along this direction, we introduce a new dataset called Chapter-Gen, which consists of approximately 10k user-generated videos with annotated chapter information. Our data collection procedure is fast, scalable and does not require any additional manual annotation. On top of this dataset, we design an effective baseline specificlly for video chapters generation task. which captures two aspects of a video,including visual dynamics and narration text. It disentangles local and global video features for localization and title generation respectively. To parse the long video efficiently, a skip sliding window mechanism is designed to localize potential chapters. And a cross attention multi-modal fusion module is developed to aggregate local features for title generation. Our experiments demonstrate that the proposed framework achieves superior results over existing methods which illustrate that the method design for similar task cannot be transfered directly even after fine-tuning. Code and dataset are available at https://github.com/czt117/MVCG.

Content has historically been the primary lens used to study language in online communities. This paper instead focuses on the linguistic style of communities. While we know that individuals have distinguishable styles, here we ask whether communities have distinguishable styles. Additionally, while prior work has relied on a narrow definition of style, we employ a broad definition involving 262 features to analyze the linguistic style of 9 online communities from 3 social media platforms discussing politics, television and travel. We find that communities indeed have distinct styles. Also, style is an excellent predictor of group membership (F-score 0.952 and Accuracy 96.09%). While on average it is statistically equivalent to predictions using content alone, it is more resilient to reductions in training data.

As free online encyclopedias with massive volumes of content, Wikipedia and Wikidata are key to many Natural Language Processing (NLP) tasks, such as information retrieval, knowledge base building, machine translation, text classification, and text summarization. In this paper, we introduce WikiDes, a novel dataset to generate short descriptions of Wikipedia articles for the problem of text summarization. The dataset consists of over 80k English samples on 6987 topics. We set up a two-phase summarization method - description generation (Phase I) and candidate ranking (Phase II) - as a strong approach that relies on transfer and contrastive learning. For description generation, T5 and BART show their superiority compared to other small-scale pre-trained models. By applying contrastive learning with the diverse input from beam search, the metric fusion-based ranking models outperform the direct description generation models significantly up to 22 ROUGE in topic-exclusive split and topic-independent split. Furthermore, the outcome descriptions in Phase II are supported by human evaluation in over 45.33% chosen compared to 23.66% in Phase I against the gold descriptions. In the aspect of sentiment analysis, the generated descriptions cannot effectively capture all sentiment polarities from paragraphs while doing this task better from the gold descriptions. The automatic generation of new descriptions reduces the human efforts in creating them and enriches Wikidata-based knowledge graphs. Our paper shows a practical impact on Wikipedia and Wikidata since there are thousands of missing descriptions. Finally, we expect WikiDes to be a useful dataset for related works in capturing salient information from short paragraphs. The curated dataset is publicly available at: https://github.com/declare-lab/WikiDes.

An image dataset contains specially selected digital images intended to help train, test, and evaluate an artificial intelligence (AI) or machine learning (ML) algorithm, usually a computer vision algorithm. A face dataset is a type of image dataset that includes images of curated human faces, typically for an ML project. There are several publicly available face datasets that you can leverage instead of collecting your own training data. Managing and optimizing datasets for machine learning is one of the crucial stages in a machine learning operations (MLOps) pipeline. Face datasets usually include faces in varying positions and lighting conditions, showing a full range of human emotions, ethnicities, ages, and additional characteristics.

In the distant future, a couple must come to terms with the loss of the eldest child, actually an A.I. purchased as an ethnically programmed companion for their adopted South East Asian daughter – SF mystery drama is on Sky Cinema from Thursday, September 22nd Memory is one of the great themes of cinema because when you point a moving image camera at someone, you capture and preserve their moving image for posterity. Or even if you write down their words on paper, a simpler, more primitive form of recording.) Memory is also one of the elements which defines us as human beings. As described in the parlance of the distant future world in which this is set, Yang is a technosapien (i.e. a robot), a purchased elder sibling of a family comprising father Jake (Colin Farrell), mother Kyra (Jodie Turner-Smith) and daughter Mika (Malea Emma Tjandrawidjaja). Mika is adopted, and her ever so Hollywood liberal parents – he a white man who has built a business around his passion for tea, she a black woman who is a hard-working, highly motivated high-flier in a demanding corporate business that's never really defined – are concerned that she connect with her South East Asian heritage.

How Everybody Can Be an Artist With #ArtificialIntelligence? AI has been used in Art for some time, and it isn't just about producing images like machines; instead, #artists are using AI to explore a new future of Art where we use #technology to express ourselves in new ways. With the development of artificial intelligence and #machinelearning, it's now within our reach to use our knowledge to create unique images with artificial intelligence. AI is still developing, but there are ways to express one's #creativity through Art. For example, some AI can generate music; others can create completely unique art-making, each piece different than the last. From helping artists find inspiration for their next piece to creating Art in a completely new and innovative way, AI is changing how we view and create Art forever.