Africa pose, partial view, color diapers The Americas vs. Africa pose, color, partial view pet foods Asia vs.

Are drones, AI making it harder to fight armed groups in the Sahel? The brazen attack on the international airport and nearby military airbase in Niamey, Niger's capital, came overnight between January 28 and 29. Balls of orange fire flew across the sky as the Nigerien army attempted to respond while residents ducked for cover and whispered prayers, as shown in videos on social media. ISIL (ISIS) in Sahel Province, or ISSP - a Niger-based outfit earlier known as the ISIL affiliate in the Greater Sahara or ISGS - has since claimed responsibility and says it killed several soldiers, although the Nigerien army disputes this. Many of its fighters had breached military drone hangars using RPGs and mortars, and managed to damage several aircraft and one civilian aeroplane, according to videos from the group.

Three West African juntas have turned to Russia. The US has declared a stark policy shift towards three West African countries which are battling Islamist insurgents and whose military governments have broken defence ties with France and turned towards Russia. The state department announced that Nick Checker, head of its Bureau of African Affairs, would visit Mali's capital Bamako to convey the United States' respect for Mali's sovereignty and chart a new course in relations, moving past policy missteps. It adds that the US also looks forward to co-operating with Mali's allies, neighbouring Burkina Faso and Niger, on shared security and economic interests. Absent from the agenda is the longstanding American concern for democracy and human rights.

Americans pulling into a Starbucks drive thru might think they are being served by a friendly staff member. But at some locations, the voice listening to the order is actually an AI robot. Behind the counter inside the store, baristas can lean on a virtual personal assistant to recall recipes or manage schedules. In the back of the shop, a scanning tool has taken on the painstaking process of counting the inventory, relieving staff of one of retail's most tedious chores, in a bid to fix the out-of-stock gaps that have frustrated the firm. The new technology is part of the hundreds of millions of dollars the 55-year-old coffee giant has been investing as it tries to win back customers after several years of struggling sales.

Clockwise from top left: Shrimp Jesus, Nayib Bukele, Justin Bieber and Super Cat League. Clockwise from top left: Shrimp Jesus, Nayib Bukele, Justin Bieber and Super Cat League. In the algorithm-driven economy of 2025, one man's shrimp Jesus is another man's side hustle. AI slop - the low-quality, surreal content flooding social media platforms, designed to farm views - is a phenomenon, some would say the phenomenon of the 2024 and 2025 internet. Merriam-Webster's word of the year this year is "slop", referring exclusively to the internet variety.

Cartographic reasoning is the skill of interpreting geographic relationships by aligning legends, map scales, compass directions, map texts, and geometries across one or more map images. Although essential as a concrete cognitive capability and for critical tasks such as disaster response and urban planning, it remains largely unevaluated. Building on progress in chart and infographic understanding, recent large vision language model studies on map visual question-answering often treat maps as a special case of charts. In contrast, map VQA demands comprehension of layered symbology (e.g., symbols, geometries, and text labels) as well as spatial relations tied to orientation and distance that often span multiple maps and are not captured by chart-style evaluations. To address this gap, we introduce FRIEDA, a benchmark for testing complex open-ended cartographic reasoning in LVLMs. FRIEDA sources real map images from documents and reports in various domains and geographical areas. Following classifications in Geographic Information System (GIS) literature, FRIEDA targets all three categories of spatial relations: topological (border, equal, intersect, within), metric (distance), and directional (orientation). All questions require multi-step inference, and many require cross-map grounding and reasoning. We evaluate eleven state-of-the-art LVLMs under two settings: (1) the direct setting, where we provide the maps relevant to the question, and (2) the contextual setting, where the model may have to identify the maps relevant to the question before reasoning. Even the strongest models, Gemini-2.5-Pro and GPT-5-Think, achieve only 38.20% and 37.20% accuracy, respectively, far below human performance of 84.87%. These results reveal a persistent gap in multi-step cartographic reasoning, positioning FRIEDA as a rigorous benchmark to drive progress on spatial intelligence in LVLMs.

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.

For example, in the Pandemic Response Challenge experiment, the context consisted of data about the geographic region for which the predictions were made, e.g., historical data of COVID-19 cases and intervention policies; actions were future schedules of intervention policies for the region; and outcomes were predicted future cases of COVID-19 along with the stringency