Data valuation is a class of techniques for quantitatively assessing the value of data for applications like pricing in data marketplaces.

The rise of space AI is reshaping government and industry through applications such as disaster detection, border surveillance, and climate monitoring, powered by massive data from commercial and governmental low Earth orbit (LEO) satellites. Federated satellite learning (FSL) enables joint model training without sharing raw data, but suffers from slow convergence due to intermittent connectivity and introduces critical trust challenges--where biased or falsified updates can arise across satellite constellations, including those injected through cyberattacks on inter-satellite or satellite-ground communication links. We propose OrbitChain, a blockchain-backed framework that empowers trustworthy multi-vendor collaboration in LEO networks. OrbitChain (i) offloads consensus to high-altitude platforms (HAPs) with greater computational capacity, (ii) ensures transparent, auditable provenance of model updates from different orbits owned by different vendors, and (iii) prevents manipulated or incomplete contributions from affecting global FSL model aggregation. Extensive simulations show that OrbitChain reduces computational and communication overhead while improving privacy, security, and global model accuracy. Its permissioned proof-of-authority ledger finalizes over 1000 blocks with sub-second latency (0.16,s, 0.26,s, 0.35,s for 1-of-5, 3-of-5, and 5-of-5 quorums). Moreover, OrbitChain reduces convergence time by up to 30 hours on real satellite datasets compared to single-vendor, demonstrating its effectiveness for real-time, multi-vendor learning. Our code is available at https://github.com/wsu-cyber-security-lab-ai/OrbitChain.git

Despite substantial investment in safety alignment, the vulnerability of large language models to sophisticated multi-turn adversarial attacks remains poorly characterized, and whether model scale or inference mode affects robustness is unknown. This study employed the TEMPEST multi-turn attack framework to evaluate ten frontier models from eight vendors across 1,000 harmful behaviors, generating over 97,000 API queries across adversarial conversations with automated evaluation by independent safety classifiers. Results demonstrated a spectrum of vulnerability: six models achieved 96% to 100% attack success rate (ASR), while four showed meaningful resistance, with ASR ranging from 42% to 78%; enabling extended reasoning on identical architecture reduced ASR from 97% to 42%. These findings indicate that safety alignment quality varies substantially across vendors, that model scale does not predict adversarial robustness, and that thinking mode provides a deployable safety enhancement. Collectively, this work establishes that current alignment techniques remain fundamentally vulnerable to adaptive multi-turn attacks regardless of model scale, while identifying deliberative inference as a promising defense direction.

When you purchase through links in our articles, we may earn a small commission. AI is causing a DRAM apocalypse and it's affecting the whole PC market this holiday season. This year, Black Friday tech shoppers should heed one important message: Don't wait, buy now. Because certain components are skyrocketing in price--and it's expected to get even worse. DRAM prices, for example, have doubled in little more than a month. AI hyperscalers have snapped up whatever they can buy.

We argue that such explanations are natural for humans and are used commonly in domains such as health care and criminology.

We argue that such explanations are natural for humans and are used commonly in domains such as health care and criminology.

The rapid expansion of IoT devices has outpaced current identification methods, creating significant risks for security, privacy, and network accountability. These challenges are heightened in open-world environments, where traffic metadata is often incomplete, noisy, or intentionally obfuscated. We introduce a semantic inference pipeline that reframes device identification as a language modeling task over heterogeneous network metadata. To construct reliable supervision, we generate high-fidelity vendor labels for the IoT Inspector dataset, the largest real-world IoT traffic corpus, using an ensemble of large language models guided by mutual-information and entropy-based stability scores. We then instruction-tune a quantized LLaMA3.18B model with curriculum learning to support generalization under sparsity and long-tail vendor distributions. Our model achieves 98.25% top-1 accuracy and 90.73% macro accuracy across 2,015 vendors while maintaining resilience to missing fields, protocol drift, and adversarial manipulation. Evaluation on an independent IoT testbed, coupled with explanation quality and adversarial stress tests, demonstrates that instruction-tuned LLMs provide a scalable and interpretable foundation for real-world device identification at scale.

Apple promises to put an AI interpreter in everyone's ears. It couldn't even help me order tamales. Earlier this week, I stopped for breakfast in Sunset Park, Brooklyn, a largely Hispanic neighborhood where street vendors sell tamales and rice pudding out of orange Gatorade coolers. I speak some Spanish, but I wanted to test out Apple's new "Live Translation" feature, which has been advertised as a sort of interpreter in your ears. I popped in my AirPods, pulled up the Translate app, and approached.

Data valuation is a class of techniques for quantitatively assessing the value of data for applications like pricing in data marketplaces.

A new report warns that the number of US investors in powerful commercial spyware rose sharply in 2024 and names new countries linked to the dangerous technology. The United States has emerged as the largest investor in commercial spyware --a global industry that has enabled the covert surveillance of journalists, human rights defenders, politicians, diplomats, and others, posing grave threats to human rights and national security . In 2024, 20 new US-based spyware investors were identified, bringing the total number of American backers of this technology to 31. This growth has largely outpaced other major investing countries such as Israel, Italy, and the United Kingdom, according to a new report published today by the Atlantic Council. The study surveyed 561 entities across 46 countries between 1992 and 2024, identifying 34 new investors.