On Sunday, the world's governments made a series of commitments to transform global governance at the United Nations Summit of the Future in New York. The ambitiously named summit was described as a "once-in-a-generation opportunity" to "forge a new global consensus on what our future should look like". Indeed, we are at a critical time when change is urgently needed. The world faces "a moment of historic danger", with increasingly imminent risks – from nuclear war to a planetary emergency, from persistent poverty and widening inequality to the unhindered advancement of artificial intelligence – threatening humanity's very existence. These are global challenges that cannot be solved purely at the national level: The people of the world need – and deserve – better coordinated global action.

The Biden administration has proposed new rules that would in effect prohibit Chinese-made vehicles from US roads after a months-long investigation into software and digital connections that could be used to spy on Americans or sabotage the vehicles. The proposed rules come as Chinese automakers become more powerful in global markets, exporting a flood of high-tech vehicles and posing new challenges to western manufacturers, with governments fearing that installed sensors, cameras and software could be used for espionage or other data collection purposes. Chinese-made vehicles aren't yet widespread on US roads but are becoming more common in Europe, Asia and other markets. The new rules, described as a national security action coming out of the US chamber of commerce, focus on Vehicle Connectivity System (VCS) and software integrated into the Automated Driving System (ADS). "Malicious access to these systems could allow adversaries to access and collect our most sensitive data and remotely manipulate cars on American roads," the department said in a statement on Sunday.

The mayor of Australia's second biggest city's desperate attempt to get residents excited about dozens of potential new parks has been completely derailed by the use of creepy AI-generated concept images. Melbourne Lord Mayor Nick Reece took to social media on Sunday to share a series of AI-generated images of some of the parks he's promised to create if re-elected next month. Cr Reece has vowed to transform the CBD into the'Garden City' by opening 28 new parks if he returns to the top job. But the plan backfired after the AI images left residents more concerned than excited for the new greenery. The images showed a number of confusing errors, including two people laying on the ground metres away from young children playing, a man with two legs melded into one, and several extra arms, sparking a range of reactions from baffled Aussies.

Their proposed fleet of'un-crewed' killer aircraft, piloted by'artificial intelligence' and dubbed MQ-28 Ghost Bats, would number in the thousands for the US alone. 'Boeing's track record doesn't seem to indicate that it's necessarily the best one to implement this kind of thing,' as one former State Department official, Steven Feldstein, told DailyMail.com. Boeing's MQ-28 Ghost Bat is an unmanned drone piloted by'artificial intelligence' (AI). It is one of the several robotic fighter jets competing to become the Pentagon's killer AI drone fleet With roughly 53 cubic-feet of storage capacity within its nose for interchangeable payloads, Boeing's Ghost Bats could one day carry a variety of bombs and munitions including multiple tactical nuclear weapons. Currently, three prototypes of the Ghost Bat have been built and flight-tested in Australia for the Royal Australian Air Force (RAAF) with at least one of those delivered to United States for its own tests and integration trials.

As the planet's largest economies struggle to forge consensus on the future of artificial intelligence, Brazil's Luiz Inacio Lula da Silva wants to ensure the developing world isn't left out of the debate. The Brazilian leader has added AI to his list of priorities for his country's presidency of the Group of 20 nations this year, seizing on the position to try to shape regulatory discussions that are raging from Europe to Asia to the United Nations, where the technology is expected to be a major theme of this week's General Assembly. Already seeking reforms to global institutions like the U.N. Security Council, Lula wants to use November's G20 leaders summit to craft a governance framework that includes the interests of Global South nations and forces AI superpowers China and the U.S. to the table, according to two people familiar with his views.

The number of people injured in a Russian air attack on Kharkiv, Ukraine's second-biggest city, rose to 21, including an eight-year-old child. Ukrainian President Volodymyr Zelenskyy said Russia used glide bombs to target "an ordinary residential building". Regional Governor Oleh Syniehubov said people were sleeping when the attack happened and two of the injured were in critical condition. Kharkiv's city council said 18 buildings were damaged in the attack. In Ukraine's eastern Donetsk region, a Russian air attack struck homes in the city of Sloviansk, trapping one woman under the rubble and injuring two of her neighbours, regional prosecutors said.

Heterogeneous materials, crucial in various engineering applications, exhibit complex multiscale behavior, which challenges the effectiveness of traditional computational methods. In this work, we introduce the Micromechanics Transformer ({\em Micrometer}), an artificial intelligence (AI) framework for predicting the mechanical response of heterogeneous materials, bridging the gap between advanced data-driven methods and complex solid mechanics problems. Trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, Micrometer can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of microstructures, material properties under any loading conditions and We demonstrate the accuracy and computational efficiency of Micrometer through applications in computational homogenization and multiscale modeling, where Micrometer achieves 1\% error in predicting macroscale stress fields while reducing computational time by up to two orders of magnitude compared to conventional numerical solvers. We further showcase the adaptability of the proposed model through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in mechanical analysis of solid materials. Our work represents a significant step towards AI-driven innovation in computational solid mechanics, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

Target selection is crucial in pharmaceutical drug discovery, directly influencing clinical trial success. Despite its importance, drug development remains resource-intensive, often taking over a decade with significant financial costs. High failure rates highlight the need for better early-stage target selection. We present GATher, a graph attention network designed to predict therapeutic gene-disease links by integrating data from diverse biomedical sources into a graph with over 4.4 million edges. GATher incorporates GATv3, a novel graph attention convolution layer, and GATv3HeteroConv, which aggregates transformations for each edge type, enhancing its ability to manage complex interactions within this extensive dataset. Utilizing hard negative sampling and multi-task pre-training, GATher addresses topological imbalances and improves specificity. Trained on data up to 2018 and evaluated through 2024, our results show GATher predicts clinical trial outcomes with a ROC AUC of 0.69 for unmet efficacy failures and 0.79 for positive efficacy. Feature attribution methods, using Captum, highlight key nodes and relationships, enhancing model interpretability. By 2024, GATher improved precision in prioritizing the top 200 clinical trial targets to 14.1%, an absolute increase of over 3.5% compared to other methods. GATher outperforms existing models like GAT, GATv2, and HGT in predicting clinical trial outcomes, demonstrating its potential in enhancing target validation and predicting clinical efficacy and safety.

Emotional support is a crucial aspect of communication between community members and police dispatchers during incident reporting. However, there is a lack of understanding about how emotional support is delivered through text-based systems, especially in various non-emergency contexts. In this study, we analyzed two years of chat logs comprising 57,114 messages across 8,239 incidents from 130 higher education institutions. Our empirical findings revealed significant variations in emotional support provided by dispatchers, influenced by the type of incident, service time, and a noticeable decline in support over time across multiple organizations. To improve the consistency and quality of emotional support, we developed and implemented a fine-tuned Large Language Model (LLM), named dispatcherLLM. We evaluated dispatcherLLM by comparing its generated responses to those of human dispatchers and other off-the-shelf models using real chat messages. Additionally, we conducted a human evaluation to assess the perceived effectiveness of the support provided by dispatcherLLM. This study not only contributes new empirical understandings of emotional support in text-based dispatch systems but also demonstrates the significant potential of generative AI in improving service delivery.

In this paper, we present Safe Guard, an LLM-agent for the detection of hate speech in voice-based interactions in social VR (VRChat). Our system leverages Open AI GPT and audio feature extraction for real-time voice interactions. We contribute a system design and evaluation of the system that demonstrates the capability of our approach in detecting hate speech, and reducing false positives compared to currently available approaches. Our results indicate the potential of LLM-based agents in creating safer virtual environments and set the groundwork for further advancements in LLM-driven moderation approaches.