The past decade has brought the world a lot of political and economic chaos, but in its defense, that same span of time has also given us the Paddington Bear movies. With those two London-set adventures, a mix of animation (Paddington) and live action (everyone else), director Paul King created a loopy world all his own, as cozy and visually pleasing as a dollhouse. The Paddington films were also refreshingly gentle, with moral messages that emerged not from preachy dialogue but from their ursine protagonist's unassuming goodness. And Ben Whishaw's voice performance as the unfailingly polite, naively bumbling bear is one of the all-time great matches between actor and animated character, up there with Tom Hanks' Woody in the Toy Story films: Whishaw quite simply is Paddington, and the completeness and believability of his characterization would have set the films apart even without their droll scripts and all-in supporting casts. The third film in the series, Paddington in Peru, ran a high risk of becoming a shark-jumping sequel, with King and his co-writers now replaced by first-time feature director Dougal Wilson and a new writing team consisting of Mark Burton, Jon Foster, and James Lamont.

Scarlett Johansson has warned of the "imminent dangers of AI" after a deepfake video of her and other prominent Jewish celebrities opposing recent antisemitic remarks from Kanye West went viral this week. The video contained AI-generated versions of more than a dozen celebrities, including Johansson, David Schwimmer, Jerry Seinfeld, Drake, Adam Sandler, Stephen Spielberg, and Mila Kunis. It opened with a deepfake likeness of Johansson in a T-shirt that was emblazoned with a hand and middle finger extended, a Star of David and the name Kanye. The video was set to "Hava Nagila", a Jewish folk song that is typically played at celebratory cultural events, and ended with the slogan: "Enough is enough. Other stars depicted included Sacha Baron Cohen, Jack Black, Natalie Portman, Adam Levine, Ben Stiller, and Lenny Kravitz. "It has been brought to my attention by family members and friends, that an AI-generated video featuring my likeness, in response to an antisemitic view, has been circulating online and gaining traction," Johansson said in a statement to People. "I am a Jewish woman who has no tolerance for antisemitism or hate speech of any kind.

In an age of breathless predictions and sky-high valuations, cognitive scientist Gary Marcus has emerged as one of the best-known skeptics of generative artificial intelligence (AI). In fact, he recently wrote a book about his concerns, Taming Silicon Valley, in which he made the case that "we are not on the best path right now, either technically or morally." Marcus--who has spent his career examining both natural and artificial intelligence--explained his reasoning in a recent conversation with Leah Hoffmann. You've written about neural networks in everything from your 1992 monograph on language acquisition to, most recently, your book Taming Silicon Valley. Your thoughts about how AI companies and policies fall short have been well covered in your U.S. Senate testimony and other outlets (including your own Substack).

This is another salvo in the ongoing war between the people that make stuff and the AI algorithms that mimic the stuff that people make. Additionally, the startup has been accused of passing off large segments of entire articles to its users without proper attribution. "Rather than create their own content, they're stealing ours to compete with us without our permission, without compensation, and undermining our very business that feeds their machines in the first place," said Danielle Coffey, CEO of the News Media Alliance, which organized the lawsuit on behalf of its members. The suit also says the company has engaged in trademark infringement, suggesting that the algorithm would send articles to users with proper attribution, using the publisher's name, but the article itself would be filled with hallucinated and incorrect information. One example given in the suit involves a piece that The Guardian published about Hamas's attack on the Nova music festival in Israel, only the AI conflated the terror attack with a 2020 shooting in Nova Scotia, Canada. Members of the News Media Alliance are suing the AI company Cohere, accusing it of stealing their journalism without permission to train its generative AI model.

Google's former chief executive has warned that artificial intelligence could be used by rogue states such as North Korea, Iran and Russia to "harm innocent people". Eric Schmidt, who held senior posts at Google from 2001 to 2017, told BBC Radio 4's Today programme that those countries and terrorists could adopt and misuse the technology to develop weapons to create "a bad biological attack from some evil person". The tech billionaire said: "The real fears that I have are not the ones that most people talk about AI – I talk about extreme risk. "Think about North Korea, or Iran, or even Russia, who have some evil goal. This technology is fast enough for them to adopt that they could misuse it and do real harm."

Stargate, the massive artificial intelligence (AI) infrastructure project recently unveiled by President Donald Trump, has begun production in Texas -- with data center construction in other states expected to be announced in the coming months. OpenAI, Softbank, Oracle and other partners' total investment of 500 million in the project will produce a large-scale network of campuses. Each campus will be designed in the roughly 1 gigawatt (GW) or greater range, a measurement of electricity that can power a minimum of 750,000 homes. During a recent press briefing on The Stargate Project attended by Fox News Digital, OpenAI announced that construction on the first site is underway in Abilene, Texas. Significant progress has been made in identifying additional locations.

Stemming from physics and later applied to other fields such as ecology, the theory of critical transitions suggests that some regime shifts are preceded by statistical early warning signals. Reddit's r/place experiment, a large-scale social game, provides a unique opportunity to test these signals consistently across thousands of subsystems undergoing critical transitions. In r/place, millions of users collaboratively created compositions, or pixel-art drawings, in which transitions occur when one composition rapidly replaces another. We develop a machine-learning-based early warning system that combines the predictive power of multiple system-specific time series via gradient-boosted decision trees with memory-retaining features. Our method significantly outperforms standard early warning indicators. Trained on the 2022 r/place data, our algorithm detects half of the transitions occurring within 20 minutes at a false positive rate of just 3.7%. Its performance remains robust when tested on the 2023 r/place event, demonstrating generalizability across different contexts. Using SHapley Additive exPlanations (SHAP) for interpreting the predictions, we investigate the underlying drivers of warnings, which could be relevant to other complex systems, especially online social systems. We reveal an interplay of patterns preceding transitions, such as critical slowing down or speeding up, a lack of innovation or coordination, turbulent histories, and a lack of image complexity. These findings show the potential of machine learning indicators in socio-ecological systems for predicting regime shifts and understanding their dynamics.

News recommender systems play a critical role in mitigating the information overload problem. In recent years, due to the successful applications of large language model technologies, researchers have utilized Discriminative Large Language Models (DLLMs) or Generative Large Language Models (GLLMs) to improve the performance of news recommender systems. Although several recent surveys review significant challenges for deep learning-based news recommender systems, such as fairness, privacy-preserving, and responsibility, there is a lack of a systematic survey on Large Language Model (LLM)-based news recommender systems. In order to review different core methodologies and explore potential issues systematically, we categorize DLLM-based and GLLM-based news recommender systems under the umbrella of LLM-based news recommender systems. In this survey, we first overview the development of deep learning-based news recommender systems. Then, we review LLM-based news recommender systems based on three aspects: news-oriented modeling, user-oriented modeling, and prediction-oriented modeling. Next, we examine the challenges from various perspectives, including datasets, benchmarking tools, and methodologies. Furthermore, we conduct extensive experiments to analyze how large language model technologies affect the performance of different news recommender systems. Finally, we comprehensively explore the future directions for LLM-based news recommendations in the era of LLMs.

The pervasiveness of large language models and generative AI in online media has amplified the need for effective automated fact-checking to assist fact-checkers in tackling the increasing volume and sophistication of misinformation. The complex nature of fact-checking demands that automated fact-checking systems provide explanations that enable fact-checkers to scrutinise their outputs. However, it is unclear how these explanations should align with the decision-making and reasoning processes of fact-checkers to be effectively integrated into their workflows. Through semi-structured interviews with fact-checking professionals, we bridge this gap by: (i) providing an account of how fact-checkers assess evidence, make decisions, and explain their processes; (ii) examining how fact-checkers use automated tools in practice; and (iii) identifying fact-checker explanation requirements for automated fact-checking tools. The findings show unmet explanation needs and identify important criteria for replicable fact-checking explanations that trace the model's reasoning path, reference specific evidence, and highlight uncertainty and information gaps.

Conversational agents are designed to understand dialogs and generate meaningful responses to communicate with humans. After the popularity of ChatGPT, with its surprising performance and powerful conversational ability, commercial Large Language Models (LLMs) for general NLP tasks such as GPT-4 [1], etc., sprung up and brought the generative AI as a solution to the public view. These LLMs work quite well in content generation tasks, but their deficiency in fact-and-knowledge-oriented tasks is wellestablished by now [13]. These models themselves cannot tell whether the text they generate is based on facts or made-up stories, and they cannot always follow the given data and rules strictly and sometimes even modify the data at will, also called hallucination. The reasoning that these LLMs appear to perform is also at a very shallow level.