Who is behind the most notorious "deepfake" app on the internet? Trying to answer that question these past few months, for a new Guardian podcast series, Black Box, has been like wandering through a hall of mirrors. The app, ClothOff, has hundreds of thousands of followers and has already been used in a least two cases to generate dozens of images of underage girls – pictures that have left the girls traumatised, their parents outraged and the police baffled at how to stop it. Producers Josh Kelly, Alex Atack and I have followed ClothOff's trail to nondescript addresses in central London that appear to be unoccupied. We have encountered sham businesses, distorted voices and photographs of fake employees.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A schoolboy has completed one of his largest portraits yet by using over 1,500 Rubik's Cubes to resemble the prime minister of the United Kingdom. Henil Soni is an 11-year-old from Harwich, Essex, England, who began his infatuation with the handheld puzzle when he was just five years old, according to SWNS, the British news service. Soni, who can now solve the well-known puzzle in mere seconds, is taking his talents to a larger scale by making portraits out of the colors on the cube.

This practice can lead to more rational decision-making that is not heavily influenced by specific opinions or positions [12, 22, 23]. As the Internet is a primary source of information for many people and the volume of online information is immense, effectively helping people consume and share information from diverse perspectives is necessary but challenging [57, 93]. Researchers have proposed various support methods for this, including the development and use of computer technology. In particular, artificial intelligence (AI)-based recommendation systems have been designed to support efficient information consumption by learning users' demographic characteristics or online activity patterns and providing tailored information based on their preferences [77]. Although computer technology plays an important role in enabling people to access and share online information, it should be noted that providing information solely based on individuals' preferences and tendencies can inadvertently contribute to the formation of echo chambers [77], a phenomenon where individuals are exposed primarily to the like-minded groups or information, leading to a reinforcement of shared narratives [28]. Research has shown that echo chambers can have many negative outcomes, including the creation and dissemination of biased information [77], increased susceptibility to fake news [8, 27], resistance towards accepting scientific evidence [63], and the adoption of unbalanced perspectives [36]. To prevent users from becoming polarized towards a specific political stance, many studies have proposed the use of computer-based tools designed to present information from diverse perspectives [31, 48, 53, 62].

Traditional deep learning often overlooks bytes, the basic units of the digital world, where all forms of information and operations are encoded and manipulated in binary format. Inspired by the success of next token prediction in natural language processing, we introduce bGPT, a model with next byte prediction to simulate the digital world. bGPT matches specialized models in performance across various modalities, including text, audio, and images, and offers new possibilities for predicting, simulating, and diagnosing algorithm or hardware behaviour. It has almost flawlessly replicated the process of converting symbolic music data, achieving a low error rate of 0.0011 bits per byte in converting ABC notation to MIDI format. In addition, bGPT demonstrates exceptional capabilities in simulating CPU behaviour, with an accuracy exceeding 99.99% in executing various operations. Leveraging next byte prediction, models like bGPT can directly learn from vast binary data, effectively simulating the intricate patterns of the digital world.

Large Language Models (LLMs) have shown impressive capabilities but still suffer from the issue of hallucinations. A significant type of this issue is the false premise hallucination, which we define as the phenomenon when LLMs generate hallucinated text when confronted with false premise questions. In this paper, we perform a comprehensive analysis of the false premise hallucination and elucidate its internal working mechanism: a small subset of attention heads (which we designate as false premise heads) disturb the knowledge extraction process, leading to the occurrence of false premise hallucination. Based on our analysis, we propose \textbf{FAITH} (\textbf{F}alse premise \textbf{A}ttention head constra\textbf{I}ining for mi\textbf{T}igating \textbf{H}allucinations), a novel and effective method to mitigate false premise hallucinations. It constrains the false premise attention heads during the model inference process. Impressively, extensive experiments demonstrate that constraining only approximately $1\%$ of the attention heads in the model yields a notable increase of nearly $20\%$ of model performance.

In today's digital age, characterized by rapid news consumption and increasing vulnerability to propaganda, fostering citizens' critical thinking is crucial for stable democracies. This paper introduces the design of ClarifAI, a novel automated propaganda detection tool designed to nudge readers towards more critical news consumption by activating the analytical mode of thinking, following Kahneman's dual-system theory of cognition. Using Large Language Models, ClarifAI detects propaganda in news articles and provides context-rich explanations, enhancing users' understanding and critical thinking. Our contribution is threefold: first, we propose the design of ClarifAI; second, in an online experiment, we demonstrate that this design effectively encourages news readers to engage in more critical reading; and third, we emphasize the value of explanations for fostering critical thinking. The study thus offers both a practical tool and useful design knowledge for mitigating propaganda in digital news.

With the rapid development of social media, the wide dissemination of fake news on social media is increasingly threatening both individuals and society. In the dynamic landscape of social media, fake news detection aims to develop a model trained on news reporting past events. The objective is to predict and identify fake news about future events, which often relate to subjects entirely different from those in the past. However, existing fake detection methods exhibit a lack of robustness and cannot generalize to unseen events. To address this, we introduce Future ADaptive Event-based Fake news Detection (FADE) framework. Specifically, we train a target predictor through an adaptive augmentation strategy and graph contrastive learning to make more robust overall predictions. Simultaneously, we independently train an event-only predictor to obtain biased predictions. Then we further mitigate event bias by obtaining the final prediction by subtracting the output of the event-only predictor from the output of the target predictor. Encouraging results from experiments designed to emulate real-world social media conditions validate the effectiveness of our method in comparison to existing state-of-the-art approaches.

Three progressive US outlets – the Intercept, Raw Story and AlterNet – filed suits in Manhattan federal court on Wednesday, demanding compensation from the tech companies. "It's important to democracy that a diverse array of news sites continue to thrive. OpenAI's violations, if not checked, will further decimate the news industry, and with it, the critical news reporters who affect positive change." The Intercept's suit lists both OpenAI and its most prominent investor Microsoft as defendants, while the joint suit filed by Raw Story and AlterNet only lists OpenAI. The complaints are otherwise nearly identical, and the law firm Loevy & Loevy is representing all three outlets in the suits.

Natalie Portman attends the Christian Dior Haute Couture Spring/Summer 2024 show as part of Paris Fashion Week on Jan. 22, 2024, in Paris. OUT OF A JOB: Natalie Portman has some mixed feelings about artificial intelligence. In her new interview with Vanity Fair for its annual Hollywood issue, the "Star Wars" star was asked if she felt the technology was a threat to her livelihood. THE WOKE ALPHABET: Resurfaced videos and comments from Google employees have come to light amid backlash to the company's Gemini artificial intelligence (AI) chatbot. ENDLESS POSSIBILITIES: The thought of AI replacing human intellect and creativity in the workforce can indeed be unsettling.

A new sci-fit movie has overtaken The Shawshank Redemption as the number one highest-rated film on IMDB - and it hasn't even been released. The Shawshank Redemption has held the number one best film spot on IMDB since 2008 when it knocked The Godfather out of its long-standing ranking of nearly 20 years. The 1994 flick has lost its crown Dune: Part Two, which has been deemed'visually thrilling' and'narratively epic' by critics days before its official release on March 1. The Warner Bros movie has a 9.4 rating on IMDB from viewers who went to an early screening of the Sci-Fi epic on Sunday, along with a 97 percent on Rotten Tomatoes. Dune: Part Two is the second park in the film adaptation of Frank Herbert's first book of the same name Dune: Part Two's sudden rise in popularity is in direct contrast to The Shawshank Redemption, which was a slow burn for viewers after it tanked at the box office.