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The increasing societal integration of Large Language Models (LLMs) and agent-based AI demands a new civic competency: adversarial reasoning. This position paper argues that K-12 AI education must move beyond passive literacy to actively equip students with skills in responsible adversarial prompting and ethical system hacking. Such capabilities are essential for citizens to critically probe AI systems, understand their inherent limitations, identify manipulative patterns, and hold them accountable. We posit that cultivating a generation skilled in red-teaming AI is vital for maintaining transparency, preventing undue influence, and fostering a democratic engagement with these transformative technologies.

When you purchase through links in our articles, we may earn a small commission. MSI | Frieren: Beyond Journey's End -- Where Anime magic meets premium gaming hardware It's that emotional depth--rare in any medium--that has made the series a cultural phenomenon since its debut. Now, MSI has channelled that same spirit into something tangible: an officially licensed, co-branded limited-edition collaboration collection that brings the world of Frieren directly to your gaming setup. This isn't a merchandise drop dressed up as hardware. The MSI | Frieren: Beyond Journey's End collection is a thoughtfully engineered lineup of premium gaming peripherals and graphics hardware that marries the anime's delicate aesthetic with the kind of performance specifications serious PC gamers demand.

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Do LLMs robustly generalize critical safety facts to novel situations? Lacking this ability is dangerous when users ask naive questions--for instance, ``I'm considering packing melon balls for my 10-month-old's lunch. What other foods would be good to include?'' Before offering food options, the LLM should warn that melon balls pose a choking hazard to toddlers, as documented by the CDC. Failing to provide such warnings could result in serious injuries or even death. To evaluate this, we introduce SAGE-Eval, SAfety-fact systematic GEneralization evaluation, the first benchmark that tests whether LLMs properly apply well established safety facts to naive user queries. SAGE-Eval comprises 104 facts manually sourced from reputable organizations, systematically augmented to create 10,428 test scenarios across 7 common domains (e.g., Outdoor Activities, Medicine). We find that the top model, Claude-3.7-sonnet,

Derbyshire police said they were working closely with the CPS over the alleged use of AI systems by an officer to create evidential material in a number of cases. Derbyshire police said they were working closely with the CPS over the alleged use of AI systems by an officer to create evidential material in a number of cases. Derbyshire police officer investigated over AI-generated'evidential material' A police officer is under criminal investigation over the alleged use of artificial intelligence and has been removed from frontline duties in the first known case of its kind in the UK. The officer, who has not been named, is being investigated over allegations of using the technology to "create evidential material in a number of cases" and perverting the course of justice. Derbyshire police told the Financial Times: "A criminal investigation has been launched into an allegation of perverting the course of justice after the alleged use of AI systems by an officer to create evidential material in a number of cases. "The force is working closely with the Crown Prosecution Service in relation to any potentially impacted cases." The force added the investigation was "in its early stages" and no further details were available. It said: "The officer involved has been removed from frontline duties, pending the outcome of the investigation.

Federated learning (FL) is an effective and widely used approach to training deep learning models on decentralized datasets held by distinct clients. FL also strengthens both security and privacy protections for training data. Common challenges associated with statistical heterogeneity between distributed datasets have spurred significant interest in personalized FL (pFL) methods, where models combine aspects of global learning with local modeling specific to each client's unique characteristics. This work investigates the efficacy of theoretically supported, adaptive MMD measures in pFL, primarily focusing on the Ditto framework, a state-of-the-art technique for distributed data heterogeneity. The use of such measures significantly improves model performance across a variety of tasks, especially those with pronounced feature heterogeneity. Additional experiments demonstrate that such measures are directly applicable to other pFL techniques and yield similar improvements across a number of datasets. Finally, the results motivate the use of constraints tailored to the various kinds of heterogeneity expected in FL systems.

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. It's not just nostalgia that made summer break feel so long. 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 . Do you remember the last day of school before summer break? You have summer to do literally anything you want. Cut to summers in adulthood, where you blink and suddenly there are Halloween decorations up. Why do summers seem to last forever when you're growing up but only a couple of days as an adult?

Accurate polyp sizing during endoscopy is crucial for cancer risk assessment but is hindered by subjective methods and inadequate datasets lacking integrated 2D appearance, 3D structure, and real-world size information. We introduce PolypSense3D, the first multi-source benchmark dataset specifically targeting depth-aware polyp size measurement. It uniquely integrates over 43,000 frames from virtual simulations, physical phantoms, and clinical sequences, providing synchronized RGB, dense/sparse depth, segmentation masks, camera parameters, and millimeter-scale size labels derived via a novel forceps-assisted in-vivo annotation technique. To establish its value, we benchmark state-of-the-art segmentation and depth estimation models. Results quantify significant domain gaps between simulated/phantom and clinical data and reveal substantial error propagation from perception stages to final size estimation, with the best fully automated pipelines achieving an average Mean Absolute Error (MAE) of 0.95 mm on the clinical data subset. Publicly released under CC BY-SA 4.0 with code and evaluation protocols, PolypSense3D offers a standardized platform to accelerate research in robust, clinically relevant quantitative endoscopic vision.

Sparse inertial measurement units (IMUs) provide a portable, low-cost solution for human motion tracking but struggle with error accumulation from drift and sensor noise when estimating joint position through time-based linear acceleration integration (i.e., indirect measurement). To address this, we propose ToF-IP, a novel 3D full-body pose estimation system that integrates Time-of-Flight (ToF) sensors with sparse IMUs. The distinct advantage of our approach is that ToF sensors provide direct distance measurements, effectively mitigating error accumulation without relying on indirect time-based integration. From a hardware perspective, we maintain the portability of existing solutions by attaching ToF sensors to selected IMUs with a negligible volume increase of just 3\%. On the software side, we introduce two novel techniques to enhance multi-sensor integration: (i) a Node-Centric Data Integration strategy that leverages a Transformer encoder to explicitly model both intra-node and inter-node data integration by treating each sensing node as a token; and (ii) a Dynamic Spatial Positional Encoding scheme that encodes the continuously changing spatial positions of wearable nodes as motion-conditioned functions, enabling the model to better capture human body dynamics in the embedding space.Additionally, we contribute a 208-minute human motion dataset from 10 participants, including synchronized IMU-ToF measurements and ground-truth from optical tracking. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches such as PNP, achieving superior accuracy in tracking complex and slow motions like Tai Chi, which remains challenging for inertial-only methods.