Disney and Universal have filed a lawsuit against Midjourney, alleging that the San Francisco–based AI image generation startup is a "bottomless pit of plagiarism" that generates "endless unauthorized copies" of the studios' work. The complaint includes dozens of images that purportedly demonstrate how Midjourney can conjure images featuring the studios' intellectual property. One image depicts Yoda from Star Wars holding a light saber, which it says was made by inputting the prompt "Yoda with lightsaber, IMAX." Another shows that typing "The Boss Baby" as a prompt allegedly resulted in an image of an animated child in a tuxedo closely resembling the protagonist of Universal's The Boss Baby franchise. "This is an extremely significant development," says IP lawyer Chad Hummel, who sees the compilation of images in the complaint as compelling evidence that "the output is not sufficiently transformative."

Brian Wilson, the musical savant who scripted a defining Southern California soundtrack with a run of hit songs with the Beach Boys before being pulled down a rabbit hole of despair and depression when his highly anticipated masterwork was shelved unfinished, has died. Wilson's family announced his death Wednesday morning on Facebook. "We are at a loss for words right now," the post said. "Please respect our privacy at this time as our family is grieving. We realize we are sharing our grief with the world," said the statement, also shared on Instagram and the musician's website. The statement didn't reveal a cause of death. Wilson died more than a year after it was revealed he was diagnosed with dementia and placed under a conservatorship in May 2024.

In their lawsuit, the entertainment giants called Midjourney's popular AI-powered image generator a "bottomless pit of plagiarism" for its alleged reproductions of the studios' best-known characters. The suit, filed in federal court in Los Angeles, claims Midjourney pirated the libraries of the two Hollywood studios, making and distributing without permission "innumerable" copies of their marquee characters such as Darth Vader from Star Wars, Elsa from Frozen, and the Minions from Despicable Me. Midjourney did not immediately respond to a request for comment. Horacio Gutierrez, Disney's chief legal officer, said in a statement: "We are bullish on the promise of AI technology and optimistic about how it can be used responsibly as a tool to further human creativity, but piracy is piracy, and the fact that it's done by an AI company does not make it any less infringing." NBCUniversal's executive vice-president and general counsel, Kim Harris, said the company was suing to "protect the hard work of all the artists whose work entertains and inspires us and the significant investment we make in our content". Instead, the studios argue, Midjourney continued to release new versions of its AI image service that boast higher-quality infringing images.

Australia must "lean in hard" to the benefits of artificial intelligence or else risk ending up "on the end of somebody else's supply chain", according to the new industry and science minister, Tim Ayres, with the Labor government planning to further regulate the rapidly evolving technology. Ayres, a former official with the manufacturing union, acknowledged Australians remained sceptical about AI and stressed that employers and employees needed to have discussions about how automation could affect workplaces. The minister said Australia had "no alternative" but to embrace the new technology and seek to become a world leader in regulating and using AI. "It's the government's job to lean into the opportunity to outline that for businesses and for workers, but also to make sure that they are confident that we've got the capability to deal with the potential pitfalls," Ayres told Guardian Australia. "I think the Australian answer has got to be leaning in hard and focusing on strategy and regulation that is in the interest of Australians."

This is what Aïcha – a fictional Master's student in AI – and her friend Félix discover in Utop'IA an educational (French language) graphic novel developed in association with author and illustrator Herji as part of a project initiated by LEARN. "Exploring AI through an environmental lens brings its physical, tangible side into sharp focus," says Sonia Agrebi, an expert in digital sociology and a LEARN projects manager. "Utop'IA examines how AI can make both a positive and negative impact on the environment. As a society, we use AI without realizing the repercussions. Our aim isn't to moralize or point the finger of blame, but rather to challenge perceptions and explain concepts to raise awareness of the issues surrounding AI." Utop'IA is backed by solid scientific reasoning and evidence, since every detail was reviewed by a committee of EPFL experts in AI, sustainability and learning science. "AI is playing an increasingly important role in our everyday lives, but I find it alarming that so little is said about its environmental impact. Utop'IA offers digestible insights into this complex subject."

This book offers a hands-on introduction to building and understanding federated learning (FL) systems. FL enables multiple devices -- such as smartphones, sensors, or local computers -- to collaboratively train machine learning (ML) models, while keeping their data private and local. It is a powerful solution when data cannot or should not be centralized due to privacy, regulatory, or technical reasons. The book is designed for students, engineers, and researchers who want to learn how to design scalable, privacy preserving FL systems. Our main focus is on personalization: enabling each device to train its own model while still benefiting from collaboration with relevant devices. This is achieved by leveraging similarities between (the learning tasks associated with) devices that are encoded by the weighted edges (or links) of a federated learning network (FL network). The key idea is to represent real-world FL systems as networks of devices, where nodes correspond to device and edges represent communication links and data similarities between them. The training of personalized models for these devices can be naturally framed as a distributed optimization problem. This optimization problem is referred to as generalized total variation minimization (GTVMin) and ensures that devices with similar learning tasks learn similar model parameters. Our approach is both mathematically principled and practically motivated. While we introduce some advanced ideas from optimization theory and graph-based learning, we aim to keep the book accessible. Readers are guided through the core ideas step by step, with intuitive explanations.

Knowledge editing techniques for large language models (LLMs) can inject knowledge that is later reproducible verbatim, but they fall short on propagating that knowledge: models cannot answer questions that require reasoning with the injected knowledge. We present a hypernetwork-based approach for knowledge propagation, named PropMEND, where we meta-learn how to modify gradients of a language modeling loss to encourage injected information to propagate. Our approach extends the meta-objective of MEND [29] so that gradient updates on knowledge are transformed to enable answering multi-hop questions involving that knowledge. We show improved performance on the RippleEdit dataset, showing almost 2x accuracy on challenging multi-hop questions whose answers are not explicitly stated in the injected fact. We further introduce a new dataset, Controlled RippleEdit, to evaluate the generalization of our hypernetwork, testing knowledge propagation along relations and entities unseen during hypernetwork training. PropMEND still outperforms existing approaches in unseen entity-relation pairs, yet the performance gap decreases substantially, suggesting future work in propagating knowledge to a wide range of relations.

The extraction of information about traffic accidents from legal documents is crucial for quantifying insurance company costs. Extracting entities such as percentages of physical and/or psychological disability and the involved compensation amounts is a challenging process, even for experts, due to the subtle arguments and reasoning in the court decision. A two-step procedure is proposed: first, segmenting the document identifying the most relevant segments, and then extracting the entities. For text segmentation, two methodologies are compared: a classic method based on regular expressions and a second approach that divides the document into blocks of n-tokens, which are then vectorized using multilingual models for semantic searches (text-embedding-ada-002/MiniLM-L12-v2 ). Subsequently, large language models (LLaMA-2 7b, 70b, LLaMA-3 8b, and GPT-4 Turbo) are applied with prompting to the selected segments for entity extraction. For the LLaMA models, fine-tuning is performed using LoRA. LLaMA-2 7b, even with zero temperature, shows a significant number of hallucinations in extractions which are an important contention point for named entity extraction. This work shows that these hallucinations are substantially reduced after finetuning the model. The performance of the methodology based on segment vectorization and subsequent use of LLMs significantly surpasses the classic method which achieves an accuracy of 39.5%. Among open-source models, LLaMA-2 70B with finetuning achieves the highest accuracy 79.4%, surpassing its base version 61.7%. Notably, the base LLaMA-3 8B model already performs comparably to the finetuned LLaMA-2 70B model, achieving 76.6%, highlighting the rapid progress in model development. Meanwhile, GPT-4 Turbo achieves the highest accuracy at 86.1%.

Large language models (LLMs) use data to learn about the world in order to produce meaningful correlations and predictions. As such, the nature, scale, quality, and diversity of the datasets used to train these models, or to support their work at inference time, have a direct impact on their quality. The rapid development and adoption of LLMs of varying quality has brought into focus the scarcity of publicly available, high-quality training data and revealed an urgent need to ground the stewardship of these datasets in sustainable practices with clear provenance chains. To that end, this technical report introduces Institutional Books 1.0, a large collection of public domain books originally digitized through Harvard Library's participation in the Google Books project, beginning in 2006. Working with Harvard Library, we extracted, analyzed, and processed these volumes into an extensively-documented dataset of historic texts. This analysis covers the entirety of Harvard Library's collection scanned as part of that project, originally spanning 1,075,899 volumes written in over 250 different languages for a total of approximately 250 billion tokens. As part of this initial release, the OCR-extracted text (original and post-processed) as well as the metadata (bibliographic, source, and generated) of the 983,004 volumes, or 242B tokens, identified as being in the public domain have been made available. This report describes this project's goals and methods as well as the results of the analyses we performed, all in service of making this historical collection more accessible and easier for humans and machines alike to filter, read and use.

While existing benchmarks probe the reasoning abilities of large language models (LLMs) across diverse domains, they predominantly assess passive reasoning, providing models with all the information needed to reach a solution. By contrast, active reasoning-where an LLM must interact with external systems to acquire missing evidence or data-has received little systematic attention. To address this shortfall, we present AR-Bench, a novel benchmark designed explicitly to evaluate an LLM's active reasoning skills. AR-Bench comprises three task families-detective cases, situation puzzles, and guessing numbers-that together simulate real-world, agentic scenarios and measure performance across commonsense, logical, and symbolic reasoning challenges. Empirical evaluation on AR-Bench demonstrates that contemporary LLMs exhibit pronounced difficulties with active reasoning: they frequently fail to acquire or leverage the information needed to solve tasks. This gap highlights a stark divergence between their passive and active reasoning abilities. Moreover, ablation studies indicate that even advanced strategies, such as tree-based searching or post-training approaches, yield only modest gains and fall short of the levels required for real-world deployment. Collectively, these findings highlight the critical need to advance methodology for active reasoning, e.g., incorporating interactive learning, real-time feedback loops, and environment-aware objectives for training. The benchmark is publicly available at: https://github.com/tmlr-group/AR-Bench.