People rely on news to know what is happening around the world and inform their daily lives. In today's world, when the proliferation of fake news is rampant, having a large-scale and high-quality source of authentic news articles with the published category information is valuable to learning authentic news' Natural Language syntax and semantics. As part of this work, we present a News Category Dataset that contains around 210k news headlines from the year 2012 to 2022 obtained from HuffPost, along with useful metadata to enable various NLP tasks. In this paper, we also produce some novel insights from the dataset and describe various existing and potential applications of our dataset.

People are regularly confronted with potentially deceptive statements (e.g., fake news, misleading product reviews, or lies about activities). Only few works on automated text-based deception detection have exploited the potential of deep learning approaches. A critique of deep-learning methods is their lack of interpretability, preventing us from understanding the underlying (linguistic) mechanisms involved in deception. However, recent advancements have made it possible to explain some aspects of such models. This paper proposes and evaluates six deep-learning models, including combinations of BERT (and RoBERTa), MultiHead Attention, co-attentions, and transformers. To understand how the models reach their decisions, we then examine the model's predictions with LIME. We then zoom in on vocabulary uniqueness and the correlation of LIWC categories with the outcome class (truthful vs deceptive). The findings suggest that our transformer-based models can enhance automated deception detection performances (+2.11% in accuracy) and show significant differences pertinent to the usage of LIWC features in truthful and deceptive statements.

Automatic extraction of narrative elements from text, combining narrative theories with computational models, has been receiving increasing attention over the last few years. Previous works have utilized the oral narrative theory by Labov and Waletzky to identify various narrative elements in personal stories texts. Instead, we direct our focus to informational texts, specifically news stories. We introduce NEAT (Narrative Elements AnnoTation) - a novel NLP task for detecting narrative elements in raw text. For this purpose, we designed a new multi-label narrative annotation scheme, better suited for informational text (e.g. news media), by adapting elements from the narrative theory of Labov and Waletzky (Complication and Resolution) and adding a new narrative element of our own (Success). We then used this scheme to annotate a new dataset of 2,209 sentences, compiled from 46 news articles from various category domains. We trained a number of supervised models in several different setups over the annotated dataset to identify the different narrative elements, achieving an average F1 score of up to 0.77. The results demonstrate the holistic nature of our annotation scheme as well as its robustness to domain category.

We introduce MyStyle, a personalized deep generative prior trained with a few shots of an individual. MyStyle allows to reconstruct, enhance and edit images of a specific person, such that the output is faithful to the person's key facial characteristics. Given a small reference set of portrait images of a person (~100), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space. We show that this manifold constitutes a personalized region that spans latent codes associated with diverse portrait images of the individual. Moreover, we demonstrate that we obtain a personalized generative prior, and propose a unified approach to apply it to various ill-posed image enhancement problems, such as inpainting and super-resolution, as well as semantic editing. Using the personalized generative prior we obtain outputs that exhibit high-fidelity to the input images and are also faithful to the key facial characteristics of the individual in the reference set. We demonstrate our method with fair-use images of numerous widely recognizable individuals for whom we have the prior knowledge for a qualitative evaluation of the expected outcome. We evaluate our approach against few-shots baselines and show that our personalized prior, quantitatively and qualitatively, outperforms state-of-the-art alternatives.

Headlines were made earlier this year when a new rapper dropped new music on – where else – TikTok. Tens of millions of followers signed up to listen to songs powered not by artistic nous but artificial intelligence (AI). Are rappers just the latest cohort of the jobs market to fall foul of AI and its staggering potential? We have been warned for years that AI is poised to take over the world of work, with many jobs simply ceasing to exist as machines finally win the war against man. We already know it can beat us at chess.

There are many things that James Bond is well known for, including martinis, a brassy theme song and an almost unbelievable amount of success with wooing the ladies. But what makes the fictional spy truly iconic is his vast array of high-tech, and occasionally absurd, gadgetry. The first Bond novel, 'Casino Royale', was published in 1952, but Ian Fleming's character didn't become properly associated with gizmos until he hit the screens a decade later. To celebrate 60 years since the first film premiered on October 5 1962, MailOnline takes a look at some of the Bond technologies that once seemed far-fetched, but now exist in real life. This includes underwater cars, such as that featured in The Spy Who Loved Me, jet packs like the one in 1965's Thunderball, and bionic hands like those used by the eponymous villain In Dr. No. To celebrate 60 years since the first film premiered on October 5 1962, MailOnline takes a look at some of the Bond technologies that once seemed far-fetched, but now exist in real life.

Are intelligent aliens living among us? A newly published novel just might lead you to think so -- and in this case, the aliens aren't visitors from another planet. Instead, they're octopuses, the eight-legged denizens of the deep that are celebrated in movies (including the Oscar-winning documentary "My Octopus Teacher") and on the ice rink (thanks to the Kraken, the Seattle hockey team that's getting set for its second NHL season.) Ray Nayler, who wrote the novel titled "The Mountain in the Sea," says he chose the octopus to serve as the designated alien for his science-fiction plot in part because it's "a creature that has a structure totally different from ours, but in whom we recognize curiosity, which is what I think we find often most human in ourselves." Nayler doesn't stop there: The promises and perils of artificial intelligence also figure prominently in the plot -- in a way that sparks musings about how we'll deal with AI, with kindred species on our planet, and perhaps eventually with extraterrestrial intelligence as well.

In June 2022, few media outlets shied away from reporting the story: an Artificial Intelligence (AI) engineer at Google named Blake Lemoine claimed that his machine had become conscious, sentient, which he revealed through The Washington Post and accompanied with the publication on his blog of an astonishing interview with the AI system. The news gave rise to a flood of comments and opinions with echoes and references to science fiction, although it was short-lived. Google officials categorically denied Lemoine's claims, and he was fired shortly afterwards for breaching his confidentiality agreement. But if this particular episode was found to be false and brought to an early end, it was in fact just another milestone in a long and still unresolved controversy: can a machine feel and know it exists, and will it ever do so, with or without our intervention or consent? It is so familiar to us because we have experienced it countless times through our imagination. Perhaps the Maschinenmensch in Metropolis (1927) was the earliest cinematic example for the general public, although its precursors can be traced back at least as far back as Frankenstein.

Training a machine-learning model on an intelligent edge device allows it to adapt to new data and make better predictions.

The Biden administration unveiled a set of far-reaching goals aimed at averting harms caused by the rise of artificial intelligence (AI) systems.