A transfer order made by the US judicial panel on multidistrict litigation on Thursday said that centralisation will "allow a single judge to coordinate discovery, streamline pretrial proceedings, and eliminate inconsistent rulings". Cases brought in California by prominent authors including Ta-Nehisi Coates, Michael Chabon, Junot Díaz and the comedian Sarah Silverman will be transferred to New York and joined with cases brought by news outlets, including the New York Times, and other authors including John Grisham, George Saunders, Jonathan Franzen and Jodi Picoult. Most of the plaintiffs opposed consolidation, arguing that their cases were too different to be combined. OpenAI had proposed consolidating the cases in northern California. The judicial panel ultimately transferred the cases to the southern district of New York, stating that centralisation would "serve the convenience of the parties and witnesses" and "promote the just and efficient conduct of this litigation".

Alex Shieh is a student at Brown University. He is making waves and facing charges for asking the school's non-faculty employees what they do all day. A sophomore at Brown University is facing the school's wrath after he sent a DOGE-like email to non-faculty employees asking them what they do all day to try to figure out why the elite school's tuition has gotten so expensive. "The inspiration for this is the rising cost of tuition," Alex Shieh told Fox News Digital in an interview. "Next year, it's set to be 93,064 to go to Brown," Shieh said of the Ivy League university.

Yes, tariffs are a tool of power. You will protect our industries, our jobs, our economy,

Tricia Helfer, who played a humanoid robot Cylon on "Battlestar Galactica," says the show's look at the conflict between humans and AI still resonates today. "We did warn against AI while we were shooting it," Helfer told Fox News Digital at the Beverly Hills Film Festival this week. She continued, "It was 20 years ago, and I've recently re-watched it and went, 'Oh my gosh, it's even more relevant now.' So I think we just really need to be careful. It's a slippery slope between using it to our advantage and having it maybe be able to control us a little bit." "I think we're a little bit far off from the humanoid Cylons yet and humanoid robots, but I don't know, they're coming," Helfer added.

A robotics company has advanced from a backflipping robot to a side-flipping robot. Robots aren't just efficient machines anymore, they are now agile performers that can flip and jog. Take, for instance, Unitree, a Chinese robotics company that has been making headlines with its incredible G1 humanoid robot. You might have seen it dancing alongside humans or remembered its predecessor, the H1, which stunned us with a backflip using electric motors. But now, the G1 has taken things to a whole new level.

Novelists Kate Mosse and Tracy Chevalier as well as poet and former Royal Society of Literature chair Daljit Nagra will be among those in attendance outside the company's King's Cross office. Protesters will meet at Granary Square at 1.30pm and a letter to Meta from the Society of Authors (SoA) will be hand-delivered at 1.45pm. It will also be sent to Meta headquarters in the US. Earlier this year, a US court filing alleged that Meta CEO Mark Zuckerberg approved the company's use of a notorious "shadow library", LibGen, which contains more than 7.5 million books. Last month, the Atlantic republished a searchable database of the titles contained in LibGen, through which many authors discovered their works may have been used to train Meta's AI models.

Visual narrative generation transforms textual narratives into sequences of images illustrating the content of the text. However, generating visual narratives that are faithful to the input text and self-consistent across generated images remains an open challenge, due to the lack of knowledge constraints used for planning the stories. In this work, we propose a new benchmark, VinaBench, to address this challenge. Our benchmark annotates the underlying commonsense and discourse constraints in visual narrative samples, offering systematic scaffolds for learning the implicit strategies of visual storytelling. Based on the incorporated narrative constraints, we further propose novel metrics to closely evaluate the consistency of generated narrative images and the alignment of generations with the input textual narrative. Our results across three generative vision models demonstrate that learning with VinaBench's knowledge constraints effectively improves the faithfulness and cohesion of generated visual narratives.

Automated frame analysis of political communication is a popular task in computational social science that is used to study how authors select aspects of a topic to frame its reception. So far, such studies have been narrow, in that they use a fixed set of pre-defined frames and focus only on the text, ignoring the visual contexts in which those texts appear. Especially for framing in the news, this leaves out valuable information about editorial choices, which include not just the written article but also accompanying photographs. To overcome such limitations, we present a method for conducting multi-modal, multi-label framing analysis at scale using large (vision-)language models. Grounding our work in framing theory, we extract latent meaning embedded in images used to convey a certain point and contrast that to the text by comparing the respective frames used. We also identify highly partisan framing of topics with issue-specific frame analysis found in prior qualitative work. We demonstrate a method for doing scalable integrative framing analysis of both text and image in news, providing a more complete picture for understanding media bias.

We study how to use naturally available user feedback, such as clicks, to optimize large language model (LLM) pipelines for generating personalized sentences using prompts. Naive approaches, which estimate the policy gradient in the prompt space, suffer either from variance caused by the large action space of prompts or bias caused by inaccurate reward predictions. To circumvent these challenges, we propose a novel kernel-based off-policy gradient method, which estimates the policy gradient by leveraging similarity among generated sentences, substantially reducing variance while suppressing the bias. Empirical results on our newly established suite of benchmarks demonstrate the effectiveness of the proposed approach in generating personalized descriptions for movie recommendations, particularly when the number of candidate prompts is large.

I too rolled my eyes when A Minecraft Movie was announced. We're all tired of seeing Jack Black in video game movies -- he was fine in Super Mario Bros., but good god Borderlands was a disaster. And the Minecraft film's trailers did it no favors, another soulless movie produced on a virtual set about a game that's completely open-ended and plotless. But it turns out A Minecraft Movie is actually good. Honestly, I'm as surprised as you are.