NVIDIA CEO and co-founder Jensen Huang commends President Donald Trump's A.I. agenda and outlines what the country's job future will look like on'Special Report.' FIRST ON FOX: The federal government is stepping into the future and embracing artificial intelligence, specifically ChatGPT, across its agencies, which proponents say will streamline productivity while solidifying President Donald Trump's pledge to keep the U.S. in the driver's seat of the cutting-edge technology, Fox News Digital exclusively learned. The U.S. General Services Administration announced Wednesday that OpenAI's ChatGPT Enterprise is now available to all federal agencies to incorporate into their workflow at a 1 per agency cost, the GSA told Fox Digital. The deal with OpenAI, the tech company behind ChatGPT, is part of GSA's OneGov Strategy that aims to modernize "how the federal government purchases goods and services" under the Trump administration. "The use of this tool has been deployed and tested with responsible policy makers, with responsible legal folks," GSA Federal Acquisition Service Commissioner Josh Gruenbaum told Fox News Digital of integrating AI into the federal government.

Russia hit pipelines in Ukraine, sparking bright flames and plumes of smoke seen from Romania. Romania was forced to scramble F-16 jets after Russia carried out a strike just half a mile from the NATO nation's territory. The country's Ministry of National Defense (MApN) confirmed in a post on X that Russia carried out a drone attack near its border. "On the night of August 5-6, the Russian forces launched a massive drone attack on the civilian infrastructure in the Ismail area, Ukraine, in the vicinity of the border with Romania," Romania's defense ministry wrote in a post on X. "The radar systems of the MApN detected air targets in Ukrainian space, close to Tulcea County. At 1:10a.m., the population in the north of the county was warned via RO-Alert," the ministry added.

The people who study nuclear war for a living are certain that artificial intelligence will soon power the deadly weapons. None of them are quite sure what, exactly, that means. In the middle of July, Nobel laureates gathered at the University of Chicago to listen to nuclear war experts talk about the end of the world. In closed sessions over two days, scientists, former government officials, and retired military personnel enlightened the laureates about the most devastating weapons ever created. The goal was to educate some of the most respected people in the world about one of the most horrifying weapons ever made and, at the end of it, have the laureates make policy recommendations to world leaders about how to avoid nuclear war.

Authorities in the United States have charged two Chinese citizens with shipping tens of millions of dollars' worth of advanced Nvidia chips to China in breach of export controls. Chuan Geng and Shiwei Yang are alleged to have "knowingly and willfully" exported the graphic processing units (GPUs) used to power artificial intelligence without authorisation from October 2022 to July 2025, the US Department of Justice said on Tuesday. Export records indicate that Geng and Yang, both 28, organised at least 21 shipments through their El Monte, California-based company ALX Solutions Inc to companies in Singapore and Malaysia, the Justice Department said. The exports included a December 2024 shipment of Nvidia H100 GPUs – described as the most powerful chip on the market – that was "falsely labelled" and had not obtained the necessary licence from the US Department of Commerce, the Justice Department said. According to prosecutors, ALX Solutions received payments from firms in Hong Kong and China, including a 1m sum from a China-based company in January 2024, rather than the companies that accepted the shipments.

When Ming* found a hidden camera in her bedroom, she prayed for a reasonable explanation, wondering whether her boyfriend had placed it there to record memories of their "happy life" together. But hope quickly turned to horror. Ming's boyfriend had been secretly taking sexually exploitative photos of not just Ming and her female friends, but also of other women in other locations, then using AI technology to generate pornographic images of them. After Ming confronted him, he "begged for mercy" but became angry when she refused to forgive him, Ming reportedly told Chinese news outlet Jimu News. Ming is just one of many women in China who have been covertly photographed or filmed – both in private and public spaces, including toilets – by voyeurs who have then circulated or sold the images online without consent.

TASS also reported that a 30-year-old man was killed and a 51-year-old woman was injured in a Ukrainian drone attack on a car near the Russian-occupied village of Nyzhnia Duvanka in the Svatovsky district on Monday. Ukraine's military intelligence claimed that Ukrainian forces killed 334 Russian troops and wounded more than 550, in a failed attack on Ukraine's Sumy region. Al Jazeera was not able to verify the report. Ukraine's presidential chief of staff, Andriy Yermak, said on Telegram that Kyiv has found components from India in Russian drones used for attacks on Ukraine. Al Jazeera could not independently verify the information.

U.S. national security customers have begun to utilize large language models, including enterprise versions of ``off-the-shelf'' models (e.g., ChatGPT) familiar to the public. This uptake will likely accelerate. However, recent studies suggest that off-the-shelf large language models frequently suggest escalatory actions when prompted with geopolitical or strategic scenarios. We demonstrate two simple, non-technical interventions to control these tendencies. Introducing these interventions into the experimental wargame design of a recent study, we substantially reduce escalation throughout the game. Calls to restrict the use of large language models in national security applications are thus premature. The U.S. government is already, and will continue, employing large language models for scenario planning and suggesting courses of action. Rather than warning against such applications, this study acknowledges the imminent adoption of large language models, and provides actionable measures to align them with national security goals, including escalation management.

Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but their ability to forecast future events remains understudied. A year ago, large language models struggle to come close to the accuracy of a human crowd. I evaluate state-of-the-art LLMs on 464 forecasting questions from Metaculus, comparing their performance against top forecasters. Frontier models achieve Brier scores that ostensibly surpass the human crowd but still significantly underperform a group of experts.

Large language models (LLMs) possess strong multilingual capabilities, and combining Reinforcement Learning from Human Feedback (RLHF) with translation tasks has shown great potential. However, we observe that this paradigm performs unexpectedly poorly when applied to colloquial subtitle translation tasks. In this work, we investigate this issue and find that the offline reward model (RM) gradually diverges from the online LLM due to distributional shift, ultimately leading to undesirable training outcomes. To address this, we propose RIVAL, an adversarial training framework that formulates the process as a min-max game between the RM and the LLM. RIVAL iteratively updates the both models, with the RM trained to distinguish strong from weak translations (qualitative preference reward), and the LLM trained to enhance its translation for closing this gap. To stabilize training and improve generalizability, we also incorporate quantitative preference reward (e.g., BLEU) into the RM, enabling reference-free quality modeling aligned with human evaluation. Through extensive experiments, we demonstrate that the proposed adversarial training framework significantly improves upon translation baselines.

Agentic systems powered by large language models (LLMs) are becoming progressively more complex and capable. Their increasing agency and expanding deployment settings attract growing attention to effective governance policies, monitoring, and control protocols. Based on the emerging landscape of the agentic market, we analyze potential liability issues arising from the delegated use of LLM agents and their extended systems through a principal-agent perspective. Our analysis complements existing risk-based studies on artificial agency and covers the spectrum of important aspects of the principal-agent relationship and their potential consequences at deployment. Furthermore, we motivate method developments for technical governance along the directions of interpretability and behavior evaluations, reward and conflict management, and the mitigation of misalignment and misconduct through principled engineering of detection and fail-safe mechanisms. By illustrating the outstanding issues in AI liability for LLM-based agentic systems, we aim to inform the system design, auditing, and tracing to enhance transparency and liability attribution.