Amazon agrees to pay $2.5 billion to settle FTC allegations over Prime enrollment practices and difficult cancellation processes, with $1.5 billion for consumer refunds.

Apple could unveil Gemini-powered Siri in Feb. Plaintiffs claimed they saw ads based on conversations Google Assistant shouldn't have heard. Google has agreed to a $68 million settlement regarding claims that its voice assistant inappropriately spied on smartphone users. Plaintiffs claimed that the company's Google Assistant platform began listening to them after it misheard conversations that sounded like its wake words. The suit argued that private information that Google Assistant shouldn't have heard was then used to deliver those individuals targeted ads. The preliminary class action settlement was filed on Friday and now awaits approval from U.S. District Judge Beth Labson Freeman.

Google has agreed to pay $68m (£51m) to settle a lawsuit claiming it secretly listened to people's private conversations through their phones. Users accused Google Assistant - a virtual assistant present on many Android devices - of recording private conversations after it was inadvertently triggered on their devices. They claimed the recordings were then shared with advertisers in order to send them targeted advertising. The BBC has contacted Google for comment. But in a filing seeking to settle the case, it denied wrongdoing and said it was seeking to avoid litigation.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Times Investigation: Ex-Trump DOJ lawyers say'fraudulent' UC antisemitism probes led them to quit This is read by an automated voice. Please report any issues or inconsistencies here . Nine former DOJ attorneys investigating UC antisemitism told The Times they felt pressured to conclude that campuses had violated the civil rights of Jewish students and staff. The attorneys resigned during the course of their UC assignments, some concerned that they were being asked to violate ethical standards. UC says it is open to talks with the Trump administration to protect $17.5 billion in federal funding.

Megalithic monuments in the otherworldly Orkney Islands remain a fundamental part of the landscape. Sheep linger at the Stones of Stenness, the remnants of a ceremonial circle. The Stones of Stenness, a brood of lichen-encrusted megaliths in the far north of the British Isles, could be mistaken for a latter-day work of land art, one with ominous overtones. The stones stand between two lochs on the largest of the Orkney Islands, off the northeastern tip of mainland Scotland. Three colossal planks of sandstone, ranging in height from fifteen feet nine inches to eighteen feet eight inches, rise from the grass, along with a smaller stone that has the bent shape of a boomerang. In contrast to the rectilinear blocks at Stonehenge, the Stenness megaliths are thin slabs with angled upper edges, like upside-down guillotine blades. Remnants of a ceremonial circle, they are placed twenty or more feet apart, creating a chasm of negative space. The monoliths in "2001: A Space Odyssey" inevitably come to mind. Given that the stones were erected five thousand years ago by a culture that left no trace of its belief system, it is unwise to project modern aesthetics onto them. Still, they can be seen only with living eyes. During a recent visit to Orkney, I kept returning to Stenness, at all hours and in all weather. On drizzly days, with skies hanging low, the stones resemble ladders to nowhere. In bright sun, hidden colors emerge: streaks of blue against gray; white and green spatters of lichen; yellowish stains indicating the presence of limonite, an iron ore. Pockmarks and brittle edges show the abrading action of millennia of wind and rain. I watched as tourists approached the stones and hesitantly touched them, as if afraid. When I put my own hands on the rock, I felt no obvious emanations, though I did not feel nothing. One evening, I leaned on a fence as the sun went down, the horizon glowing orange against a cobalt sky.

The largest and most varied such publicly available dataset, at Airbus SPOT 6/7 satellites' high resolution of up to 1.5 m/pixel, empowered by European Space Agency's (ESA) Phi-Lab as part of the ESA-funded

Accurate, fine-grained poverty maps remain scarce across much of the Global South. While Demographic and Health Surveys (DHS) provide high-quality socioeconomic data, their spatial coverage is limited and reported coordinates are randomly displaced for privacy, further reducing their quality. We propose a graph-based approach leveraging low-dimensional AlphaEarth satellite embeddings to predict cluster-level wealth indices across Sub-Saharan Africa. By modeling spatial relations between surveyed and unlabeled locations, and by introducing a probabilistic "fuzzy label" loss to account for coordinate displacement, we improve the generalization of wealth predictions beyond existing surveys. Our experiments on 37 DHS datasets (2017-2023) show that incorporating graph structure slightly improves accuracy compared to "image-only" baselines, demonstrating the potential of compact EO embeddings for large-scale socioeconomic mapping.

Villages areas hold significant importance in the study of human-land relationships. However, with the advancement of urbanization, the gradual disappearance of spatial characteristics and the homogenization of landscapes have emerged as prominent issues. Existing studies primarily adopt a single-disciplinary perspective to analyze villages spatial morphology and its influencing factors, relying heavily on qualitative analysis methods. These efforts are often constrained by the lack of digital infrastructure and insufficient data. To address the current research limitations, this paper proposes a Hierarchical Graph Neural Network (HGNN) model that integrates multi-source data to conduct an in-depth analysis of villages spatial morphology. The framework includes two types of nodes-input nodes and communication nodes-and two types of edges-static input edges and dynamic communication edges. By combining Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT), the proposed model efficiently integrates multimodal features under a two-stage feature update mechanism. Additionally, based on existing principles for classifying villages spatial morphology, the paper introduces a relational pooling mechanism and implements a joint training strategy across 17 subtypes. Experimental results demonstrate that this method achieves significant performance improvements over existing approaches in multimodal fusion and classification tasks. Additionally, the proposed joint optimization of all sub-types lifts mean accuracy/F1 from 0.71/0.83 (independent models) to 0.82/0.90, driven by a 6% gain for parcel tasks. Our method provides scientific evidence for exploring villages spatial patterns and generative logic.

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? How much of Europe's oil still comes from Russia? Ukraine's Zelenskyy to meet European leaders in London over military aid Ukrainian President Volodymyr Zelenskyy is due to meet European leaders in the United Kingdom for talks on military aid to stave off future Russian aggression if a ceasefire stops the war now in its fourth year. Zelenskyy and British Prime Minister Keir Starmer are expected to be joined at the Foreign Office in London on Friday by NATO Secretary-General Mark Rutte, Danish Prime Minister Mette Frederiksen and Dutch Prime Minister Dick Schoof.

We address the long-horizon gap in large language model (LLM) agents by enabling them to sustain coherent strategies in adversarial, stochastic environments. Settlers of Catan provides a challenging benchmark: success depends on balancing short- and long-term goals amid randomness, trading, expansion, and blocking. Prompt-centric LLM agents (e.g., ReAct, Reflexion) must re-interpret large, evolving game states each turn, quickly saturating context windows and losing strategic consistency. We propose HexMachina, a continual learning multi-agent system that separates environment discovery (inducing an adapter layer without documentation) from strategy improvement (evolving a compiled player through code refinement and simulation). This design preserves executable artifacts, allowing the LLM to focus on high-level strategy rather than per-turn reasoning. In controlled Catanatron experiments, HexMachina learns from scratch and evolves players that outperform the strongest human-crafted baseline (AlphaBeta), achieving a 54% win rate and surpassing prompt-driven and no-discovery baselines. Ablations confirm that isolating pure strategy learning improves performance. Overall, artifact-centric continual learning transforms LLMs from brittle stepwise deciders into stable strategy designers, advancing long-horizon autonomy.