While artificial intelligence (AI) is advancing rapidly and mastering increasingly complex problems with astonishing performance, the safety assurance of such systems is a major concern. Particularly in the context of safety-critical, real-world cyber-physical systems, AI promises to achieve a new level of autonomy but is hampered by a lack of safety assurance. While data-driven control takes up recent developments in AI to improve control systems, control theory in general could be leveraged to improve AI safety. Therefore, this article outlines a new perspective on AI safety based on an interdisciplinary interpretation of the underlying data-generation process and the respective abstraction by AI systems in a system theory-inspired and system analysis-driven manner. In this context, the new perspective, also referred to as data control, aims to stimulate AI engineering to take advantage of existing safety analysis and assurance in an interdisciplinary way to drive the paradigm of data control. Following a top-down approach, a generic foundation for safety analysis and assurance is outlined at an abstract level that can be refined for specific AI systems and applications and is prepared for future innovation.

Federated learning with secure aggregation enables private and collaborative learning from decentralised data without leaking sensitive client information. However, secure aggregation also complicates the detection of malicious client behaviour and the evaluation of individual client contributions to the learning. To address these challenges, QI (Pejo et al.) and FedGT (Xhemrishi et al.) were proposed for contribution evaluation (CE) and misbehaviour detection (MD), respectively. QI, however, lacks adequate MD accuracy due to its reliance on the random selection of clients in each training round, while FedGT lacks the CE ability. In this work, we combine the strengths of QI and FedGT to achieve both robust MD and accurate CE. Our experiments demonstrate superior performance compared to using either method independently.

Relative localization is a crucial capability for multi-robot systems operating in GPS-denied environments. Existing approaches for multi-robot relative localization often depend on costly or short-range sensors like cameras and LiDARs. Consequently, these approaches face challenges such as high computational overhead (e.g., map merging) and difficulties in disjoint environments. To address this limitation, this paper introduces MGPRL, a novel distributed framework for multi-robot relative localization using convex-hull of multiple Wi-Fi access points (AP). To accomplish this, we employ co-regionalized multi-output Gaussian Processes for efficient Radio Signal Strength Indicator (RSSI) field prediction and perform uncertainty-aware multi-AP localization, which is further coupled with weighted convex hull-based alignment for robust relative pose estimation. Each robot predicts the RSSI field of the environment by an online scan of APs in its environment, which are utilized for position estimation of multiple APs. To perform relative localization, each robot aligns the convex hull of its predicted AP locations with that of the neighbor robots. This approach is well-suited for devices with limited computational resources and operates solely on widely available Wi-Fi RSSI measurements without necessitating any dedicated pre-calibration or offline fingerprinting. We rigorously evaluate the performance of the proposed MGPRL in ROS simulations and demonstrate it with real-world experiments, comparing it against multiple state-of-the-art approaches. The results showcase that MGPRL outperforms existing methods in terms of localization accuracy and computational efficiency. Finally, we open source MGPRL as a ROS package https://github.com/herolab-uga/MGPRL.

Embedded into information systems, artificial intelligence (AI) faces security threats that exploit AI-specific vulnerabilities. This paper provides an accessible overview of adversarial attacks unique to predictive and generative AI systems. We identify eleven major attack types and explicitly link attack techniques to their impacts -- including information leakage, system compromise, and resource exhaustion -- mapped to the confidentiality, integrity, and availability (CIA) security triad. We aim to equip researchers, developers, security practitioners, and policymakers, even those without specialized AI security expertise, with foundational knowledge to recognize AI-specific risks and implement effective defenses, thereby enhancing the overall security posture of AI systems.

Large language models (LLMs) are increasingly used across research and industry applications, yet their inference efficiency remains a significant challenge. As the computational power of modern GPU architectures continuously improves, their memory bandwidth and capacity have not scaled proportionally, creating a critical bottleneck during inference. To address this, we investigate ternary language models (TriLMs) that employ quantization-aware training to significantly reduce memory requirements. We first analyze the scalability of TriLMs by conducting a scaling law analysis, revealing that TriLMs benefit more from increasing training data than from scaling model parameters. Based on this observation, we introduce Spectra-1.1, an open suite of TriLMs trained on up to 1.2 trillion tokens, demonstrating sustained performance gains at scale. Furthermore, to improve inference efficiency, we propose novel 2-bit and 1.6-bit packing schemes for ternary weights, which demonstrate accelerated inference across various CPU architectures. Also, building on the 2-bit packing, we develop a GPU kernel called TriRun that accelerates end-to-end model inference by up to 5 times compared to floating-point baselines. To encourage further exploration and development of TriLMs, we will release the Spectra-1.1 suite and TriRun inference kernels. Overall, our work lays the foundation for building and deploying efficient LLMs, providing a valuable resource for the research community.

Anomaly detection in connected autonomous vehicles (CAVs) is crucial for maintaining safe and reliable transportation networks, as CAVs can be susceptible to sensor malfunctions, cyber-attacks, and unexpected environmental disruptions. This study explores an anomaly detection approach by simulating vehicle behavior, generating a dataset that represents typical and atypical vehicular interactions. The dataset includes time-series data of position, speed, and acceleration for multiple connected autonomous vehicles. We utilized machine learning models to effectively identify abnormal driving patterns. First, we applied a stacked Long Short-Term Memory (LSTM) model to capture temporal dependencies and sequence-based anomalies. The stacked LSTM model processed the sequential data to learn standard driving behaviors. Additionally, we deployed a Random Forest model to support anomaly detection by offering ensemble-based predictions, which enhanced model interpretability and performance. The Random Forest model achieved an R2 of 0.9830, MAE of 5.746, and a 95th percentile anomaly threshold of 14.18, while the stacked LSTM model attained an R2 of 0.9998, MAE of 82.425, and a 95th percentile anomaly threshold of 265.63. These results demonstrate the models' effectiveness in accurately predicting vehicle trajectories and detecting anomalies in autonomous driving scenarios.

Judge Vince Chhabria sided with Meta but appeared to do so regretfully, stating that Meta's use of the writers' work to train its bots isn't necessarily legal but that the plaintiffs "made the wrong arguments."

When Jim O'Neill was nominated to be the second in command at the US Department of Health and Human Services, longevity enthusiasts were excited. As Robert F. Kennedy Jr.'s new right-hand man, O'Neill is expected to wield authority at health agencies that fund biomedical research and oversee the regulation of new drugs. And while O'Neill doesn't subscribe to Kennedy's most contentious beliefs--and supports existing vaccine schedules--he may still steer the agencies in controversial new directions. O'Neill is well-known in the increasingly well-funded and tight-knit longevity community. In speaking with more than 20 people who work in the longevity field and are familiar with O'Neill, it's clear that they share a genuine optimism about his leadership.

Florida Attorney General James Uthmeier joins'Fox & Friends First' to discuss a federal judge moving to halt the state's social media ban for children and weigh in on the fight to protect women's sports. If you or someone you know is having thoughts of suicide, please contact the Suicide & Crisis Lifeline at 988 or 1-800-273-TALK (8255). When my 16-year-old son Mason was going through a painful breakup, he did what many kids of his generation do: He turned to TikTok. Mason used the social media site to search for positive affirmations and inspirational quotes. Instead, TikTok's algorithm sent him the most horrific content urging suicide and self-harm.

Russia launched its biggest aerial attack on Ukraine since the beginning of its full-scale invasion overnight on Sunday, firing a total of 537 aerial weapons, including 477 drones and decoys and 60 missiles, according to the Ukrainian air force. Ukrainian forces intercepted 475 of the weapons, but the military said F-16 pilot Lieutenant Colonel Maksym Ustimenko was killed "while repelling" the "massive enemy air attack". At least four others were also killed in the air raids, in Kherson, Kharkiv, Dnipropetrovsk and Kostiantynivka regions, the Associated Press news agency reported, citing local officials. The aerial attacks were also far-reaching, targeting regions as far away as Lviv, in the far west, where a drone attack caused a large fire at an industrial facility in the city of Drohobych, and cut electricity to parts of the area. Poland said it scrambled aircraft, together with other NATO countries, to ensure the safety of Polish airspace during the attack.