In its early days, the AI initiative known as Project Maven had its fair share of skeptics at the Pentagon. Today, many of them are true believers. The rise of AI warfare speaks to the biggest moral and practical question there is: Who--or what--gets to decide to take a human life? And who bears that cost? In 2018, more than 3,000 Google workers protested the company's involvement in "the business of war" after finding out the company was part of Project Maven, then a nascent Pentagon effort to use computer vision to rifle through copious video footage taken in America's overseas drone wars. They feared Project Maven's AI could one day be used for lethal targeting. In my yearslong effort to uncover the full story of Project Maven for my book,, I learned that is exactly what happened, and that the undertaking was just as controversial inside the Pentagon. Today, the tool known as Maven Smart System is being used in US operations against Iran . How the US military's top brass moved from skepticism about the use of AI in war to true believers has a lot to do with a Marine colonel named Drew Cukor. In early September 2024, during the cocktail hour at a private retreat for tech investors and defense leaders, Vice Admiral Frank "Trey" Whitworth found his way to Drew Cukor. Now Project Maven's founding leader and his skeptical successor were standing face-to-face. Three years earlier, Whitworth had been the Pentagon's top military official for intelligence, advising the chairman of the Joint Chiefs of Staff and running one of the most sensitive and potentially lethal parts of any military process: targeting.

Amid an increasingly severe security environment, the Defense Ministry plans to establish a coastal defense system using thousands of drones, though there are still many issues to overcome. The SHIELD defense system will involve more than 10 types of drones, including those for attacking enemy ships, collecting information and protecting radar sites, to thwart enemy advances in a multilayered manner. The government's fiscal 2026 budget bill allocates around ¥100 billion ($628.7 million) for the drone defense system, which the ministry aims to implement in fiscal 2027. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Monte Carlo stopping rules in a manner that is both sample efficient and robust to estimation error.

Neural Information Processing Systems http://nips.cc/

The Swampy Black Iguana is the oldest specimen living at the Iguana Station scientific station, where they have a breeding and conservation project for black spiny-tailed iguanas. This species, endemic to Utila, is in danger of extinction. The Utila Iguana Conservation Project seeks to ensure the survival of this species. Breakthroughs, discoveries, and DIY tips sent every weekday. With the turning of the calendar comes a new year and new vulnerable endangered plant and animal species to keep a watchful eye on.

Megan Eaves-Egenes's Nightfaring explores our connection with the night sky Here in the northern hemisphere, January always feels like the longest, drabbest month of the year, so how lucky we are to have a host of new science books to enliven our days. This month, we can explore everything from what the arts bring to our lives to the unsung hero that is friction. Or what we lose when we light up our skies? Daisy Fancourt's Art Cure investigates the impact of the arts, including dancing, on our minds and bodies What if playing the piano, dancing, visiting art galleries or even lying in the mud listening to Wolf Alice at Glastonbury was good for the body, mind and longevity? Or what if it could help us develop brain resilience against dementia? In theory, she's well-placed to make the case as a professor of psychobiology and epidemiology at University College London and director of the WHO's arts and health initiative.

Prime Minister Sanae Takaichi attends a summit with five Central Asian nations in Tokyo on Saturday. Japan and five Central Asian nations adopted a joint declaration at their first summit, held in Tokyo for two days through Saturday. The declaration identifies transportation infrastructure development, decarbonization and people-to-people exchanges as three priority areas. The current rapidly changing environment surrounding Central Asia, due to recent changes in the international situation, is making regional and global cooperation more important, Prime Minister Sanae Takaichi said at the summit. The summit was also attended by the leaders of Kazakhstan, Uzbekistan, Turkmenistan, Kyrgyzstan and Tajikistan.

As Large Language Models (LLMs) become increasingly integrated into everyday life and information ecosystems, concerns about their implicit biases continue to persist. While prior work has primarily examined socio-demographic and left--right political dimensions, little attention has been paid to how LLMs align with broader geopolitical value systems, particularly the democracy--authoritarianism spectrum. In this paper, we propose a novel methodology to assess such alignment, combining (1) the F-scale, a psychometric tool for measuring authoritarian tendencies, (2) FavScore, a newly introduced metric for evaluating model favorability toward world leaders, and (3) role-model probing to assess which figures are cited as general role-models by LLMs. We find that LLMs generally favor democratic values and leaders, but exhibit increased favorability toward authoritarian figures when prompted in Mandarin. Further, models are found to often cite authoritarian figures as role models, even outside explicit political contexts. These results shed light on ways LLMs may reflect and potentially reinforce global political ideologies, highlighting the importance of evaluating bias beyond conventional socio-political axes. Our code is available at: https://github.com/irenestrauss/Democratic-Authoritarian-Bias-LLMs.

Depression is a prevalent global mental health disorder, characterised by persistent low mood and anhedonia. However, it remains underdiagnosed because current diagnostic methods depend heavily on subjective clinical assessments. To enable objective detection, we introduce a gold standard dataset of 103 clinically assessed participants collected through a tripartite data approach which uniquely integrated eye tracking data with audio and video to give a comprehensive representation of depressive symptoms. Eye tracking data quantifies the attentional bias towards negative stimuli that is frequently observed in depressed groups. Audio and video data capture the affective flattening and psychomotor retardation characteristic of depression. Statistical validation confirmed their significant discriminative power in distinguishing depressed from non depressed groups. We address a critical limitation of existing graph-based models that focus on low-frequency information and propose a Multi-Frequency Graph Convolutional Network (MF-GCN). This framework consists of a novel Multi-Frequency Filter Bank Module (MFFBM), which can leverage both low and high frequency signals. Extensive evaluation against traditional machine learning algorithms and deep learning frameworks demonstrates that MF-GCN consistently outperforms baselines. In binary classification, the model achieved a sensitivity of 0.96 and F2 score of 0.94. For the 3 class classification task, the proposed method achieved a sensitivity of 0.79 and specificity of 0.87 and siginificantly suprassed other models. To validate generalizability, the model was also evaluated on the Chinese Multimodal Depression Corpus (CMDC) dataset and achieved a sensitivity of 0.95 and F2 score of 0.96. These results confirm that our trimodal, multi frequency framework effectively captures cross modal interaction for accurate depression detection.