The regulation also imposes new safety expectations on'AI chatbot services.' Canada is joining Australia, Indonesia and Malaysia, in banning teenagers from using social media. The Safe Social Media Act introduced by Marc Miller, Minister of Canadian Identity and Culture, bans children under the age of 16 from having a social media account and introduces new regulatory expectations for social media services and AI platforms. Under the legislation, social media services are required to design their products to be safer for children. Platforms will also be expected to remove deepfakes and content that sexually victimizes a child or revictimizes a survivor.

The Canadian government has introduced a new digital safety bill that would ban social media for children under 16, with exemptions for platforms that meet certain safety standards. The bill also aims to make AI chatbots safer by setting up a digital regulator to establish safety standards, a government official said. "We have seen the very serious consequences that online harms can have. The safety of children cannot be an afterthought," the Minister of Canadian Identity and Culture, Marc Miller, said in a statement. Companies could face penalties of 3% of global revenue or up to C$10 million ($7.2 million), whichever is more, for failing to comply. "Social media platforms and AI chatbots are designed to capture attention.

Chest X-rays (CXRs) are the most frequently performed imaging examinations in clinical settings. Recent advancements in Medical Large Multimodal Models (MLMMs) have enabled automated CXR interpretation, improving diagnostic accuracy and efficiency.

In the era of artificial intelligence (AI), pedestrian privacy is increasingly at risk. In research areas such as autonomous driving, computer vision, and surveillance, large datasets are often collected in public spaces, capturing pedestrians without consent or anonymization. These datasets are used to train systems that can identify, track, and analyze individuals, often without their knowledge. Although various technical methods and regional regulations have been proposed to address this issue, existing solutions are either insufficient to protect privacy or compromise data utility, thereby limiting their effectiveness for research. In this paper, we argue that more effort is needed to protect pedestrian privacy in the era of AI while maintaining data utility. We call on the AI and computer vision communities to take pedestrian privacy seriously and to rethink how pedestrian data are collected and anonymized. Collaboration with experts in law and ethics will also be essential for the responsible development of AI. Without stronger action, it will become increasingly difficult for individuals to protect their privacy, and public trust in AI may decline.

Pope Leo XIV has described Barcelona's Sagrada Família as a masterpiece of stones, colours and light as he inaugurated its newest - and tallest - tower. The giant Tower of Jesus Christ, completed in February, has brought the church to a soaring height of 172.5m (566ft) - cementing it as the tallest church in the world. His visit to the iconic basilica also marks 100 years since the death of its architect, Antoni Gaudí. Among those attending the service were Spanish royals King Felipe VI and Queen Letizia, as well as Prime Minister Pedro Sánchez. The pope's week-long visit to Spain, which began on Saturday, is the first by a pope in some 15 years.

"Defenders cannot afford to take weeks to patch," one Cybersecurity and Infrastructure Security Agency official warned on Wednesday. With new generations of AI models fueling both rapid software vulnerability discovery and the potential for faster exploitation by malicious hackers, the United States Cybersecurity and Infrastructure Security Agency released a new directive on Wednesday that requires more rapid and efficient software patching by federal civilian agencies. The "binding operational directive" (BOD) lays out a rubric for how quickly bugs must be fixed based on four assessments of urgency, with a turnaround time in critical cases of just three days. Chris Butera, CISA's acting executive assistant director for cybersecurity, told reporters on Wednesday that the goal of the directive is to help agencies prioritize, so they can address the most problematic vulnerabilities first while taking more time to remediate bugs that pose a less-pressing risk. The directive comes as private companies and governments have been scrambling to assess the extent of the cybersecurity reckoning that AI vulnerability and exploit development capabilities could unleash.

Recent advances in operating system (OS) agents have enabled vision-language models (VLMs) to directly control a user's computer. Unlike conventional VLMs that passively output text, OS agents autonomously perform computer-based tasks in response to a single user prompt. OS agents do so by capturing, parsing, and analysing screenshots and executing low-level actions via application programming interfaces (APIs), such as mouse clicks and keyboard inputs. This direct interaction with the OS significantly raises the stakes, as failures or manipulations can have immediate and tangible consequences. In this work, we uncover a novel attack vector against these OS agents: Malicious Image Patches (MIPs), adversarially perturbed screen regions that, when captured by an OS agent, induce it to perform harmful actions by exploiting specific APIs. For instance, a MIP can be embedded in a desktop wallpaper or shared on social media to cause an OS agent to exfiltrate sensitive user data. We show that MIPs generalise across user prompts and screen configurations, and that they can hijack multiple OS agents even during the execution of benign instructions. These findings expose critical security vulnerabilities in OS agents that have to be carefully addressed before their widespread deployment.

Trump Risks Key Surveillance Authority Over'Unqualified' Spy-Chief Pick US lawmakers are alarmed that Bill Pulte, a housing official with no intelligence experience, is poised to take charge of one of the government's most powerful surveillance tools. A sweeping warrantless surveillance authority remains on track to expire Friday, with no clear path to a deal, after President Donald Trump refused this week to abandon his pick of housing official Bill Pulte to temporarily lead the US intelligence community--even tasking Pulte with gutting the Office of the Director of National Intelligence in a DOGE-style "downsizing" before a permanent director is named. In a Truth Social post after his second White House meeting in two days with House speaker Mike Johnson, Trump called Section 702 of the Foreign Intelligence Surveillance Act "very important to our military, and keeping the American people safe" and asked Congress for a short-term extension to give him time to find a permanent director of national intelligence. Section 702 lets the government collect the communications of foreign targets abroad without a warrant, sweeping in an unknown volume of Americans' messages that the FBI can later search. It faces a first-ever lapse in its legal authorization if Congress does not act by the end of Friday, June 12.

Combining math concepts with sports can help boost your fractions game. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Students also saw improvement in other math areas after participating in the workshop. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

The rapid growth of publicly available data has fueled deep learning advancements but also raises concerns about unauthorized data usage. Unlearnable Examples (UEs) have emerged as a data protection strategy that introduces imperceptible perturbations to prevent unauthorized learning. However, most existing UE methods produce perturbations strongly tied to specific training sets, leading to a significant drop in unlearnability when applied to unseen data or tasks. In this paper, we argue that for broad applicability, UEs should maintain their effectiveness across diverse application scenarios. To this end, we conduct the first comprehensive study on the transferability of UEs across diverse and practical yet demanding settings. Specifically, we identify key scenarios that pose significant challenges for existing UE methods, including varying styles, out-of-distribution classes, resolutions, and architectures.