The Canadian province of British Columbia has filed a lawsuit against OpenAI over the company's failure to notify authorities about the Tumbler Ridge shooter's conversations with its chatbot. According to the Wall Street Journal and The New York Times, the lawsuit accuses the company of designing an unsafe product and of negligence for not alerting law enforcement about the shooter's chats. It claims the shooting could have been prevented if the company had heeded its human reviewers' advice to notify the Royal Canadian Mounted Police about the shooter's activities on its service. In February, the Canadian government demanded safety changes from OpenAI after reports came out that it didn't alert authorities after its employees flagged the account of the shooter in 2025, because it contained potential warnings of committing real-world violence. While the shooter's original account was banned, OpenAI eventually discovered that she made a second account.

Canada's British Columbia is suing OpenAI over the school killings and alleges CEO Sam Altman promised reforms after the attack but never followed through. Canada's British Columbia is suing OpenAI over the school killings and alleges CEO Sam Altman promised reforms after the attack but never followed through. Canadian province alleges deadly attack could have been prevented if company had warned police of shooter's ChatGPT use British Columbia has sued OpenAI in California, saying a mass shooting at a school in the province could have been prevented if the company had warned local law enforcement that the shooter had used ChatGPT to plan the massacre. The lawsuit filed in San Francisco federal court on Monday names OpenAI and its CEO, Sam Altman, as defendants. It is seeking damages to fund recovery efforts in the province after the February attack, as well as an order directing changes to the way the company handles ChatGPT conversations that could lead to violence.

OpenAI is facing another wave of lawsuits in the wake of the February mass shooting in Tumbler Ridge in Canada's province of British Columbia, which left eight people dead. On Wednesday, 30 new complaints were reportedly filed in a United States federal court in California, including teachers and students who were witnesses and survivors at the school where most of the shooting took place, joining seven initial lawsuits filed in April. The suits, brought by lawyer Jay Edelson, allege that the company knew about the intentions of the 18-year-old shooter who, in her interactions with OpenAI's chatbot ChatGPT, had described scenarios involving gun violence, but that the leadership did not report their concerns to law enforcement, echoing earlier lawsuits on the matter. In April, Altman penned a letter to the community apologising that the company did not alert law enforcement about the shooter, Jesse Van Rootselaar. "While I know words can never be enough, I believe an apology is necessary to recognize the harm and irreversible loss your community has suffered," Altman wrote in the letter.

The Canadian province of British Columbia is preparing to sue OpenAI, alleging the US company failed to alert police after its staff internally flagged violent ChatGPT conversations linked to the person responsible for February's Tumbler Ridge mass shooting . Attorney General Niki Sharma announced Tuesday that the province has hired legal teams in British Columbia and California to "explore all legal avenues to hold OpenAI and its decision-makers accountable for its documented failure to notify law enforcement regarding explicit, flagged threats made by the perpetrator on the company's ChatGPT platform." The move stems from the February 10 attack in the remote mountain community of Tumbler Ridge, where authorities say 18-year-old Jesse Van Rootselaar killed their mother and half-brother before going to the Tumbler Ridge Secondary School and opening fire. Five children between the ages of 11 and 13 and one educator were killed at the school. Twenty-seven other people were wounded before Van Rootselaar died from what police described as a self-inflicted gunshot wound.

In recent years, the Prairies have seen bigger swings in climate conditions -- very wet years followed by very dry ones. That makes an already unpredictable landscape even harder to forecast, with real consequences for flood preparedness and water quality. The challenge is the landscape itself. Much of the Canadian Prairies sit within the Prairie Pothole Region, a landscape dotted with millions of shallow wetlands and depressions. Water doesn't simply run downhill into a stream, it is stored first.

A new book from Rebecca Solnit, promising to bring us hope in these "difficult times", is among our pick of popular science titles out this month - along with a guide on how to talk to AI, and a look at modern warfare March, in the northern hemisphere anyway, is about venturing out for some much-needed vitamin D and dodging showers. Forget that - just head for a decent café where you can delve into the marvellous science books we've got waiting for you. This month you can explore how animals shaped our world, how to spot liars from their language, what forest trees can tell us - and flowers as revolutionaries. There is some stronger stuff too, if you are in the mood: try AI in the hands of the US military, or a deep cultural look at how our world has changed beyond recognition. Whatever your choice, it's all guaranteed to enrich the inner you.

As archives turn to artificial intelligence to manage growing volumes of digital records, privacy risks inherent in current AI data practices raise critical concerns about data sovereignty and ethical accountability. This paper explores how privacy-enhancing technologies (PETs) and Web3 architectures can support archives to preserve control over sensitive content while still being able to make it available for access by researchers. We present Clio-X, a decentralized, privacy-first Web3 digital solution designed to embed PETs into archival workflows and support AI-enabled reference and access. Drawing on a user evaluation of a medium-fidelity prototype, the study reveals both interest in the potential of the solution and significant barriers to adoption related to trust, system opacity, economic concerns, and governance. Using Rogers' Diffusion of Innovation theory, we analyze the sociotechnical dimensions of these barriers and propose a path forward centered on participatory design and decentralized governance through a Clio-X Decentralized Autonomous Organization. By integrating technical safeguards with community-based oversight, Clio-X offers a novel model to ethically deploy AI in cultural heritage contexts.

In 1948, the founder of information theory, Claude Shannon, proposed modelling language in terms of the probability of the next word in a sentence given the previous words. These types of probabilistic language models were largely derided, most famously by linguist Noam Chomsky: "The notion of'probability of a sentence' is an entirely useless one." In 2022, 74 years after Shannon's proposal, ChatGPT appeared, which caught the attention of the public, with some even suggesting it was a gateway to super-human intelligence. Going from Shannon's proposal to ChatGPT took so long because the amount of data and computing time used was unimaginable even a few years before. ChatGPT is a large language model (LLM) learned from a huge corpus of text from the internet.

In the search for happiness, having a good day every day is surely crucial. But when there are so many pursuits competing for our attention, sometimes it's difficult to know how much time to allocate for each one. Now, scientists in Canada claim to cracked the code for the perfect day – and surprisingly, it includes a short shift at work. According to the experts, the formula for the perfect day is six hours of family time, two hours spent with friends, 1.5 hour socialising, two hours exercising and one hour eating and drinking. Additionally, the perfect day should involve no more than six hours of work and less than 15 minutes commuting.

Background: Patient stratification in brain disorders remains a significant challenge, despite advances in machine learning and multimodal neuroimaging. Automated machine learning algorithms have been widely applied for identifying patient subtypes (biotypes), but results have been inconsistent across studies. These inconsistencies are often attributed to algorithmic limitations, yet an overlooked factor may be the statistical properties of the input data. This study investigates the contribution of data patterns on algorithm performance by leveraging synthetic brain morphometry data as an exemplar. Methods: Four widely used algorithms-SuStaIn, HYDRA, SmileGAN, and SurrealGAN were evaluated using multiple synthetic pseudo-patient datasets designed to include varying numbers and sizes of clusters and degrees of complexity of morphometric changes. Ground truth, representing predefined clusters, allowed for the evaluation of performance accuracy across algorithms and datasets. Results: SuStaIn failed to process datasets with more than 17 variables, highlighting computational inefficiencies. HYDRA was able to perform individual-level classification in multiple datasets with no clear pattern explaining failures. SmileGAN and SurrealGAN outperformed other algorithms in identifying variable-based disease patterns, but these patterns were not able to provide individual-level classification. Conclusions: Dataset characteristics significantly influence algorithm performance, often more than algorithmic design. The findings emphasize the need for rigorous validation using synthetic data before real-world application and highlight the limitations of current clustering approaches in capturing the heterogeneity of brain disorders. These insights extend beyond neuroimaging and have implications for machine learning applications in biomedical research.