The final language of the annual bill that funds the US military is in. It removes provisions that would have helped ensure service members' ability to fix their own equipment. US lawmakers have removed provisions in the National Defense Authorization Act for 2026 that would have ensured military members' right to repair their own equipment. The final language of the NDAA was shared by the House Armed Services Committee on Sunday, after weeks of delays pushed the annual funding bill to the end of the year. Among a host of other language changes made as part of reconciling different versions of the legislation drafted by the Senate and the House of Representatives, two provisions focused on the right to repair--Section 836 of the Senate bill and Section 863 of the House bill--have both been removed.

From "cameo" to "io," OpenAI keeps trying to call its new and upcoming releases by names that resemble existing trademarks. In September, OpenAI launched a way for users to generate a digital likeness of themselves they could use to create personalized deepfake videos . This is one of the core features in Sora, OpenAI's app for sharing AI videos inside a TikTok-style feed. The self-deepfaking feature was called "cameo," and with that standout feature, Sora quickly rose to the top of Apple's iOS download charts. This feature name led to a trademark lawsuit with Cameo, the app where fans can pay celebrities to record personalized videos.

Study shows leading U.S. universities collaborated with Chinese state-backed AI labs linked to surveillance technology targeting Uyghur Muslims in Xinjiang.

Cybercriminals using malware and AI tools have stolen over $318 million in cargo shipments nationwide, targeting freight during transit with sophisticated online fraud schemes.

An image made with a drone shows air handling units on the roof of a CloudHQ data center in Ashburn, Virginia. An image made with a drone shows air handling units on the roof of a CloudHQ data center in Ashburn, Virginia. Mon 8 Dec 2025 07.00 ESTLast modified on Mon 8 Dec 2025 08.41 EST A coalition of more than 230 environmental groups has demanded a national moratorium on new datacenters in the US, the latest salvo in a growing backlash to a booming artificial intelligence industry that has been blamed for escalating electricity bills and worsening the climate crisis. The green groups, including Greenpeace, Friends of the Earth, Food & Water Watch and dozens of local organizations, have urged members of Congress to halt the proliferation of energy-hungry datacenters, accusing them of causing planet-heating emissions, sucking up vast amounts of water and for exacerbating electricity bill increases that have hit Americans this year. The push comes amid a growing revolt against moves by companies such as Meta, Google and Open AI to plow hundreds of billions of dollars into new datacenters, primarily to meet the huge computing demands of AI.

China conducts systematic intellectual property theft campaign against America using cyber-espionage and commercial access, threatening U.S. technological leadership.

Generative search engines based on large language models (LLMs) are replacing traditional search, fundamentally changing how information providers are compensated. To sustain this ecosystem, we need fair mechanisms to attribute and compensate content providers based on their contributions to generated answers. We introduce MaxShapley, an efficient algorithm for fair attribution in generative search pipelines that use retrieval-augmented generation (RAG). MaxShapley is a special case of the celebrated Shapley value; it leverages a decomposable max-sum utility function to compute attributions with linear computation in the number of documents, as opposed to the exponential cost of Shapley values. We evaluate MaxShapley on three multi-hop QA datasets (HotPotQA, MuSiQUE, MS MARCO); MaxShapley achieves comparable attribution quality to exact Shapley computation, while consuming a fraction of its tokens--for instance, it gives up to an 8x reduction in resource consumption over prior state-of-the-art methods at the same attribution accuracy.

Retrieving case law is a time-consuming task predominantly carried out by querying databases. We provide a comparison of two models in three different settings for Czech Constitutional Court decisions: (i) a large general-purpose embedder (OpenAI), (ii) a domain-specific BERT-trained from scratch on ~30,000 decisions using sliding windows and attention pooling. We propose a noise-aware evaluation including IDF-weighted keyword overlap as graded relevance, binarization via two thresholds (0.20 balanced, 0.28 strict), significance via paired bootstrap, and an nDCG diagnosis supported with qualitative analysis. Despite modest absolute nDCG (expected under noisy labels), the general OpenAI embedder decisively outperforms the domain pre-trained BERT in both settings at @10/@20/@100 across both thresholds; differences are statistically significant. Diagnostics attribute low absolutes to label drift and strong ideals rather than lack of utility. Additionally, our framework is robust enough to be used for evaluation under a noisy gold dataset, which is typical when handling data with heterogeneous labels stemming from legacy judicial databases.

As AI-generated video platforms rapidly advance, ethical challenges such as copyright infringement emerge. This study examines how users make sense of AI-generated videos on OpenAI's Sora by conducting a qualitative content analysis of user comments. Through a thematic analysis, we identified four dynamics that characterize how users negotiate authenticity, authorship, and platform governance on Sora. First, users acted as critical evaluators of realism, assessing micro-details such as lighting, shadows, fluid motion, and physics to judge whether AI-generated scenes could plausibly exist. Second, users increasingly shifted from passive viewers to active creators, expressing curiosity about prompts, techniques, and creative processes. Text prompts were perceived as intellectual property, generating concerns about plagiarism and remixing norms. Third, users reported blurred boundaries between real and synthetic media, worried about misinformation, and even questioned the authenticity of other commenters, suspecting bot-generated engagement. Fourth, users contested platform governance: some perceived moderation as inconsistent or opaque, while others shared tactics for evading prompt censorship through misspellings, alternative phrasing, emojis, or other languages. Despite this, many users also enforced ethical norms by discouraging the misuse of real people's images or disrespectful content. Together, these patterns highlighted how AI-mediated platforms complicate notions of reality, creativity, and rule-making in emerging digital ecosystems. Based on the findings, we discuss governance challenges in Sora and how user negotiations inform future platform governance.

Safeguard models help large language models (LLMs) detect and block harmful content, but most evaluations remain English-centric and overlook linguistic and cultural diversity. Existing multilingual safety benchmarks often rely on machine-translated English data, which fails to capture nuances in low-resource languages. Southeast Asian (SEA) languages are underrepresented despite the region's linguistic diversity and unique safety concerns, from culturally sensitive political speech to region-specific misinformation. Addressing these gaps requires benchmarks that are natively authored to reflect local norms and harm scenarios. We introduce SEA-SafeguardBench, the first human-verified safety benchmark for SEA, covering eight languages, 21,640 samples, across three subsets: general, in-the-wild, and content generation. The experimental results from our benchmark demonstrate that even state-of-the-art LLMs and guardrails are challenged by SEA cultural and harm scenarios and underperform when compared to English texts.