Last year, filmmaker Paul Schrader--the director of Blue Collar, American Gigolo, and First Reformed, and writer of Martin Scorsese's Taxi Driver--issued what seemed like the last word on artificial intelligence in Hollywood filmmaking. A few days after the release of Denis Villeneuve's sci-fi blockbuster Dune: Part Two, Schrader asked his Facebook followers: "Will Dune 3 be made by AI? And, if it is, how will we know?" Schrader is well regarded not only as a director, but one of cinema's top-shelf curmudgeons, quick with a wry burn or baiting shit-post. But his Dune tweet seemed like more than another provocation. It spoke to a mounting feeling among many filmgoers, myself included: that Hollywood had stooped to producing sleek, antiseptic images so devoid of personality that they might as well have been made not by a living, breathing, thinking, feeling artist, but by a computer.

Lady Gaga probably wasn't thinking that a coup would unfold in her greenhouse. Then again, she was cohosting a party there with Sean Parker, the billionaire founder of Napster and first president of Facebook. It was February 2024, and the singer had invited guests to her 22.5 million oceanside estate in Malibu to mark the launch of a skin-care nonprofit. One of the organization's trustees was her boyfriend, whose day job was running the Parker Foundation. In the candlelit space, beside floor-to-ceiling windows that looked out over the Pacific, Parker's people mingled with Gaga's, nibbling focaccia and branzino alla brace to music from a string quartet (Grammy-winning, of course).

MSNBC's "Morning Joe" reacted to the networks upcoming name change, "My Source News Opinion World," or MS NOW, on Monday. But don't shed a tear (not that you would, anyway), it's turning into MS NOW. Or, as the New York Times put it, "Goodbye, MSNBC. The far-left network lost its tie to the newsy term "NBC" and looks more like some feminist retread site. Or, as MSNBC President Rebecca Kutler put it, "While our name will be changing, who we are and what we do will not." So, maybe my viewership assessment is correct. Sure, the ship might have made a career of hitting icebergs, but it's got a new name. The fallout from the change was swift. The Times even took a swipe with the follow-up headline: "MSNBC's Rebrand Invites Bemusement and Ridicule." The name switch reflects marketing nonsense as part of the corporate split. It also eliminates the long-standing comparison to MSDNC. The rationalization for the new name is: "My Source for News, Opinion, and the World." CNBC is going to keep its name, according to the Wall Street Journal, but the initials mean something else – "Consumer News and Business Channel," another marketing nuance. The new company will include, "NBCUniversal's cable television networks, including USA Network, CNBC, MSNBC, Oxygen, E!, SYFY and Golf Channel" along with a few other properties, including the formerly useful Rotten Tomatoes movie site. Nobody sane wants MSNBC/MS NOW connected in any way to NBC. It's been a corporate embarrassment for years. They're OK with it looking like the rational folks at CNBC are still connected, but the lunacy of MSNBC gets rebranded. It removes the stain for NBC. The more things change, the more they remain the same. This is the same network where they repeatedly compare President Donald Trump to monsters like Hitler and Stalin. Hosts regularly throw around charges of dictatorship like we are living in 1930s Germany – although somehow they are allowed to say it. Host Tiffany Cross recently claimed the government was grabbing people and "transporting them to concentration camps." And the face of the franchise, MSNBC host Rachel Maddow, told viewers, "We have a consolidating dictatorship in our country." Remember, "Morning Joe" host Joe Scarborough made the most-embarrassing quote of the entire failed Joe Biden presidency: "I've said it for years now, he's cogent.

V oice Assistants (V As) such as Amazon Alexa or Google Assistant rely on wake-word detection to respond to people's commands, which could potentially be vulnerable to audio adversarial examples. In this work, we target our attack on the wake-word detection system, jamming the model with some inconspicuous background music to deactivate the V As while our audio adversary is present. We implemented an emulated wake-word detection system of Amazon Alexa based on recent publications.

We collect a dataset of over one million unique images and associated news articles from left-and right-leaning news sources, and develop a method to predict the image's political leaning. This problem is particularly challenging because of the enormous intra-class visual and semantic diversity of our data. We propose a two-stage method to tackle this problem. In the first stage, the model is forced to learn relevant visual concepts that, when joined with document embeddings computed from articles paired with the images, enable the model to predict bias. In the second stage, we remove the requirement of the text domain and train a visual classifier from the features of the former model. We show this two-stage approach facilitates learning and outperforms several strong baselines.

Recent advances in large language models (LLMs) and multi-agent systems have demonstrated remarkable capabilities in complex problem-solving tasks such as deep research, vibe coding, and mathematical reasoning. However, most existing multi-agent systems are built upon manual prompt/workflow engineering with sophisticated agent frameworks, making them computationally inefficient, less capable, and can not benefit from data-centric learning. In this work, we introduce Chain-of-Agents (CoA), a novel paradigm of LLM reasoning that enables native end-to-end complex problem-solving in the same way as a multi-agent system (i.e., multi-turn problem solving with multiple tools and multiple agents) within one model. In chain-of-agents problem-solving, the model dynamically activates different tool agents and role-playing agents to simulate multi-agent collaboration in an end-to-end fashion. To elicit end-to-end chain-of-agents problem-solving abilities in LLMs, we introduce a multi-agent distillation framework to distill state-of-the-art multi-agent systems into chain-of-agents trajectories for agentic supervised fine-tuning. We then use agentic reinforcement learning on verifiable agentic tasks to further improve the models' capabilities on chain-of-agents problem solving. We call the resulting models Agent Foundation Models (AFMs). Our empirical studies demonstrate that AFM establishes new state-of-the-art performance across diverse benchmarks in both web agent and code agent settings. We make the entire research, including the model weights, code for training and evaluation, and the training data, fully open-sourced, which offers a solid starting point for future research on agent models and agentic RL.

The global spread of misinformation and concerns about content trustworthiness have driven the development of automated fact-checking systems. Since false information often exploits social media dynamics such as "likes" and user networks to amplify its reach, effective solutions must go beyond content analysis to incorporate these factors. Moreover, simply labelling content as false can be ineffective or even reinforce biases such as automation and confirmation bias. This paper proposes an explainable framework that combines content, social media, and graph-based features to enhance fact-checking. It integrates a misinformation classifier with explainability techniques to deliver complete and interpretable insights supporting classification decisions. Experiments demonstrate that multimodal information improves performance over single modalities, with evaluations conducted on datasets in English, Spanish, and Portuguese. Additionally, the framework's explanations were assessed for interpretability, trustworthiness, and robustness with a novel protocol, showing that it effectively generates human-understandable justifications for its predictions. The code and experiments are available at https://github.com/MeLLL-UFF/mu2X/ .

Audio DNNs have demonstrated impressive performance on various machine listening tasks; however, most of their representations are computationally costly and uninterpretable, leaving room for optimization. Here, we propose a novel approach centered on spectrotemporal modulation (STM) features, a signal processing method that mimics the neurophysiological representation in the human auditory cortex. The classification performance of our STM-based model, without any pretraining, is comparable to that of pretrained audio DNNs across diverse naturalistic speech, music, and environmental sounds, which are essential categories for both human cognition and machine perception. These results show that STM is an efficient and interpretable feature representation for audio classification, advancing the development of machine listening and unlocking exciting new possibilities for basic understanding of speech and auditory sciences, as well as developing audio BCI and cognitive computing.