Apple could unveil Gemini-powered Siri in Feb. Still needs a holster, though. Between the new Macs, iPhones and rumored AI gadgets, Apple surprised us with an upgraded, second-generation AirTag. It has Apple's latest Ultra Wideband chip -- the same one used in the iPhone 17 lineup and the Apple Watch Ultra 3. It also uses Precision Finding to reach items up to 50 percent further away than the previous AirTag model. This feature will now work with any Apple Watch Series 9, Ultra 2 or newer.

LG TVs add'delete' option for Copilot The blackout knocked out traffic lights, causing the robo-taxis to get stuck at intersections. Waymo has resumed its robo-taxi service in San Francisco after a power outage stranded vehicles around the city, reported. The blackout, caused by a Pacific Gas & Electric (PG&E) substation fire, caused traffic light disruptions that affected Waymo's automated driving systems. Yesterday's power outage was a widespread event that caused gridlock across San Francisco, with non-functioning traffic signals and transit disruptions, a Waymo spokesperson told CNBC in a statement. While the failure of the utility infrastructure was significant, we are committed to ensuring our technology adjusts to traffic flow during such events.

Things to Do in L.A. Tap to enable a layout that focuses on the article. This is read by an automated voice. Please report any issues or inconsistencies here . After Saturday's blackout, roughly 110,000 San Francisco residents have power again. About 21,000 are still in the dark as extensive repairs continue after a substation fire.

LG TVs add'delete' option for Copilot A San Francisco power outage left Waymo's self-driving cars stranded at intersections Waymo halted its autonomous ride-hailing services in the city in response. Several of Waymo's autonomous vehicles were seen stuck in the middle of San Francisco streets following a significant power outage that took out the city's traffic lights. Waymo responded to the power outage by suspending its ride-hailing services in the city, but images and videos on social media showed the self-driving taxis stopped at intersections with hazard lights on. We have temporarily suspended our ride-hailing services in the San Francisco Bay Area due to the widespread power outage, Suzanne Philion, a spokesperson for Waymo, told Engadget in an email. Our teams are working diligently and in close coordination with city officials, and we are hopeful to bring our services back online soon.

Natural disasters such as hurricanes, wildfires, and winter storms have induced large-scale power outages in the U.S., resulting in tremendous economic and societal impacts. Accurately predicting power outage recovery and impact is key to resilience of power grid. Recent advances in machine learning offer viable frameworks for estimating power outage duration from geospatial and weather data. However, three major challenges are inherent to the task in a real world setting: spatial dependency of the data, spatial heterogeneity of the impact, and moderate event data. We propose a novel approach to estimate the duration of severe weather-induced power outages through Graph Attention Networks (GAT). Our network uses a simple structure from unsupervised pre-training, followed by semi-supervised learning. We use field data from four major hurricanes affecting $501$ counties in eight Southeastern U.S. states. The model exhibits an excellent performance ($>93\%$ accuracy) and outperforms the existing methods XGBoost, Random Forest, GCN and simple GAT by $2\% - 15\%$ in both the overall performance and class-wise accuracy.

The multifaceted nature of disaster impact shows that densely populated areas contribute more to aggregate burden, while sparsely populated but heavily affected regions suffer disproportionately at the individual level. This study introduces a framework for quantifying the societal impacts of power outages by translating customer weighted outage exposure into deprivation measures, integrating welfare metrics with three recovery indicators, average outage days per customer, restoration duration, and relative restoration rate, computed from sequential EAGLE I observations and linked to Zip Code Tabulation Area demographics. Applied to four United States hurricanes, Beryl 2024 Texas, Helene 2024 Florida, Milton 2024 Florida, and Ida 2021 Louisiana, this standardized pipeline provides the first cross event, fine scale evaluation of outage impacts and their drivers. Results demonstrate regressive patterns with greater burdens in lower income areas, mechanistic analysis shows deprivation increases with longer restoration durations and decreases with faster restoration rates, explainable modeling identifies restoration duration as the dominant driver, and clustering reveals distinct recovery typologies not captured by conventional reliability metrics. This framework delivers a transferable method for assessing outage impacts and equity, comparative cross event evidence linking restoration dynamics to social outcomes, and actionable spatial analyses that support equity informed restoration planning and resilience investment.

At the 1933 World's Fair in Chicago, an exhibit called The Home of Tomorrow laid out a vision of a futuristic living space. It included outlandish predictions including personal helicopter pads, but it also foreshadowed central air conditioning, automatic dishwashers, and other innovations that have become integral parts of modern life. As we envision the home of the future, it's easy to get caught up in sci-fi-inspired predictions of fully autonomous homes serviced by robotic butlers and disembodied virtual assistants that remove the humanity from our living spaces. Here at Popular Science, we reject that vision. For the inaugural Home of the Future Awards, we have selected 25 products that augment life at home by making it more efficient, affordable, accessible, and--ultimately--more enjoyable. We test, preview, and evaluate hundreds of products per year and these products deserve a chance to cohabitate with you and the people who matter to you. A flat top grill is one of the most versatile ways to cook just about anything from pancakes to burgers, but temperature control is key.

President Trump's tariff standoff with China has caused chaos, confusion, and major delays for companies of all shapes and sizes. As everyone waits to see what happens next, some businesses that depend on international trade are already feeling major impacts, saying that they might not meet their production deadlines. And one of those deadlines is pretty important: Christmas. Today on the show, we're joined by WIRED's senior business editor Louise Matsakis to talk through the latest on tariffs. Mentioned in this episode: Donald Trump Is Already Ruining Christmas by Zeyi Yang OpenAI Adds Shopping to ChatGPT in a Challenge to Google by Reece Rogers The Agonizing Task of Turning Europe's Power Back On by Natasha Bernal Write to us at uncannyvalley@wired.com.

Extreme hazard events such as wildfires and hurricanes increasingly threaten power systems, causing widespread outages and disrupting critical services. Recently, predict-then-optimize approaches have gained traction in grid operations, where system functionality forecasts are first generated and then used as inputs for downstream decision-making. However, this two-stage method often results in a misalignment between prediction and optimization objectives, leading to suboptimal resource allocation. To address this, we propose predict-all-then-optimize-globally (PATOG), a framework that integrates outage prediction with globally optimized interventions. At its core, our global-decision-focused (GDF) neural ODE model captures outage dynamics while optimizing resilience strategies in a decision-aware manner. Unlike conventional methods, our approach ensures spatially and temporally coherent decision-making, improving both predictive accuracy and operational efficiency. Experiments on synthetic and real-world datasets demonstrate significant improvements in outage prediction consistency and grid resilience.

With the global climate change, extreme weather events like hurricanes, winter storms, and tornadoes have increasingly led to widespread electric power outages across the United States [14]. For instance, during March 2018, the northeastern U.S. was battered by three consecutive winter storms within a span of 14 days. This series of events caused power outages that left over 2.75 million customers without electricity in the New England region, resulting in economic losses of approximately $4 billion, including $2.9 billion in insured damages [8]. Such severe weather-related incidents often leave millions without power for extended periods, resulting in significant economic disruption [19] and, tragically, sometimes even loss of life [25]. Given the considerable impact of extreme weather on power systems since the early 2000s, regulatory bodies in the U.S. have called on the energy sector to enhance the resilience of power grids through various hardening measures [1]. Consequently, accurately assessing the resilience of power grids is crucial not only for estimating potential damage from extreme weather but also for informing short-term disaster response strategies, long-term resilience planning, and shaping energy policy.