Power outage in San Francisco knocks out electricity to one-third of Pacific Gas and Electric Co. customers, with fire at substation on 8th and Mission streets contributing to blackouts.

B rendan Foody is 22 years old and runs a company worth billions. This August, I met the young CEO in a glass conference room overlooking the San Francisco Bay. While his peers are searching for their first jobs, Foody is pursuing a " master plan," as he calls it, to upend the global labor market. His start-up, Mercor, offers an AI-powered hiring platform: Bots weed through résumés, and even conduct interviews. In the next five years, Foody told me, AI could automate 50 percent of the tasks that people do today.

Quantifying predictive uncertainty is essential for safe and trustworthy real-world AI deployment. Yet, fully nonparametric estimation of conditional distributions remains challenging for multivariate targets. We propose Tomographic Quantile Forests (TQF), a nonparametric, uncertainty-aware, tree-based regression model for multivariate targets. TQF learns conditional quantiles of directional projections $\mathbf{n}^{\top}\mathbf{y}$ as functions of the input $\mathbf{x}$ and the unit direction $\mathbf{n}$. At inference, it aggregates quantiles across many directions and reconstructs the multivariate conditional distribution by minimizing the sliced Wasserstein distance via an efficient alternating scheme with convex subproblems. Unlike classical directional-quantile approaches that typically produce only convex quantile regions and require training separate models for different directions, TQF covers all directions with a single model without imposing convexity restrictions. We evaluate TQF on synthetic and real-world datasets, and release the source code on GitHub.

Spatial boundaries, such as ecological transitions or climatic regime interfaces, capture steep environmental gradients, and shifts in their structure can signal emerging environmental changes. Quantifying uncertainty in spatial boundary locations and formally testing for temporal shifts remains challenging, especially when boundaries are derived from noisy, gridded environmental data. We present a unified framework that combines heteroskedastic Gaussian process (GP) regression with a scaled Maximum Absolute Difference (MAD) Global Envelope Test (GET) to estimate spatial boundary curves and assess whether they evolve over time. The heteroskedastic GP provides a flexible probabilistic reconstruction of boundary lines, capturing spatially varying mean structure and location specific variability, while the test offers a rigorous hypothesis testing tool for detecting departures from expected boundary behaviors. Simulation studies show that the proposed method achieves the correct size under the null and high power for detecting local boundary shifts. Applying our framework to the Sahel Sahara transition zone, using annual Koppen Trewartha climate classifications from 1960 to 1989, we find no statistically significant decade scale changes in the arid and semi arid or semi arid and non arid interfaces. However, the method successfully identifies localized boundary shifts during the extreme drought years of 1983 and 1984, consistent with climate studies documenting regional anomalies in these interfaces during that period.

Things to Do in L.A. Tap to enable a layout that focuses on the article. A magnitude 4.0 earthquake was reported Sunday at 3:30 p.m. in the Bay Area. It was followed in the same area by two smaller quakes. This is read by an automated voice. Please report any issues or inconsistencies here .

Things to Do in L.A. Tap to enable a layout that focuses on the article. Rare, deep-sea encounter: California scientists observe'extraordinary' seven-arm octopus On November 6, 2025, MBARI Senior Scientist Steven Haddock and researchers in MBARI's Biodiversity and Biooptics Team observed a seven-arm octopus (Haliphron atlanticus) during an expedition in Monterey Bay with MBARI's remotely operated vehicle at a depth of approximately 700 meters. This is read by an automated voice. Please report any issues or inconsistencies here . California scientists captured rare footage of a seven-arm octopus eating a jellyfish.

Defense analyst Seth Jones warns in 'The American Edge' that the U.S. could exhaust long-range missiles within a week of Taiwan conflict with China.

Plus: A CDC panel voted to recommend delaying the hepatitis B vaccine for babies. If you're a longtime reader, you probably know that our newsroom selects 10 breakthroughs every year that we think will define the future . This group exercise is mostly fun and always engrossing, with plenty of lively discussion along the way, but at times it can also be quite difficult. The 2026 list will come out on January 12--so stay tuned. In the meantime, we wanted to share some of the technologies from this year's reject pile, as a window into our decision-making process. These four technologies won't be on our 2026 list of breakthroughs, but all were closely considered, and we think they're worth knowing about.

Jon Kotwicki jokes that converting an aluminum plane in Alaska is the "worst idea that a person could possibly have." This 108-foot-long former cargo plane now has a king size bed, washer dryer, and heated floors, but the build was by no means easy. Breakthroughs, discoveries, and DIY tips sent every weekday. When flight instructor and former commercial airline pilot Jon Kotwicki happened upon a DC-6 air freighter for sale in 2022, he knew it was the perfect plane to transform into an overnight rental. However, once he made the purchase, "my first thought," says Kotwicki, "was, 'My God, what have I done?'" Built in 1956, the 117-foot-wide, 108-foot-long cargo plane had spent its days carrying freight and fuel to remote villages in Alaska before retiring from flight.

Perch 2.0 is a supervised bioacoustics foundation model pretrained on 14,597 species, including birds, mammals, amphibians, and insects, and has state-of-the-art performance on multiple benchmarks. Given that Perch 2.0 includes almost no marine mammal audio or classes in the training data, we evaluate Perch 2.0 performance on marine mammal and underwater audio tasks through few-shot transfer learning. We perform linear probing with the embeddings generated from this foundation model and compare performance to other pretrained bioacoustics models. In particular, we compare Perch 2.0 with previous multispecies whale, Perch 1.0, SurfPerch, AVES-bio, BirdAVES, and Birdnet V2.3 models, which have open-source tools for transfer-learning and agile modeling. We show that the embeddings from the Perch 2.0 model have consistently high performance for few-shot transfer learning, generally outperforming alternative embedding models on the majority of tasks, and thus is recommended when developing new linear classifiers for marine mammal classification with few labeled examples.