Things to Do in L.A. Canadian snowbirds are still unhappy with Trump. This is read by an automated voice. Please report any issues or inconsistencies here . Palm Springs relies heavily on Canadian tourists, who are declining to travel to the U.S. or shortening their stays because of Trump. The number of Canadian visitors to California plummeted more than 18% in 2025 compared with the year prior.

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In what's turning out to be a great quarter for corporate earnings growth, company executives and investors alike are focused on something else entirely: the threat from artificial intelligence. Mentions of AI disruption on management calls almost doubled compared to the previous quarter, an analysis of transcripts shows. While the technology hasn't yet noticeably reduced earnings estimates, investors aren't waiting around and instead are selling any company perceived to be at risk. Last week, commercial real estate company CBRE Group published better-than-expected earnings. In a call with analysts following the results, its chief executive officer said it's possible AI will reduce demand for office space in the long term. The comments sparked a 20% selloff in the stock over two days.

As the housing market stalls, Zillow's CEO sees AI as "an ingredient rather than a threat" that can both help the company protect its turf and reinvent how people search for homes. This will not be a banner year for the real estate app Zillow. "We describe the home market as bouncing along the bottom," CEO Jeremy Wacksman said in our conversation this week. Last year was dismal for the real estate market, and he expects things to improve only marginally in 2026. "The way to think about it is that there were 4.1 million existing homes sold last year--a normal market is 5.5 to 6 million," Wacksman says.

The share declines were sparked by AI firms such as Anthropic, the company behind the chatbot Claude, releasing new tools. The share declines were sparked by AI firms such as Anthropic, the company behind the chatbot Claude, releasing new tools. But, after second day of Wall Street falls, analysts say sell-off'may overstate AI's immediate risk to complex deal-making' Shares in commercial property services companies have tumbled, in the latest sell-off driven by fears over disruption from artificial intelligence. After steep declines on Wall Street, European stocks in the sector were hit on Thursday. The estate agent Savills' shares fell 7.5% in London, while the serviced office provider International Workplace Group, which owns the Regus brand, lost 9%.

Things to Do in L.A. Tap to enable a layout that focuses on the article. With a Super Bowl ad, California governor's race'is now kicked into gear' San José Mayor Matt Mahan, a moderate Democrat, has broken with Gov. Gavin Newsom on crime and other issues and is pitching himself as a pragmatist. This is read by an automated voice. Please report any issues or inconsistencies here . Backers of Matt Mahan, San José's mayor, spend $1.4 million in Super Bowl ad campaign funded by Silicon Valley tech executives to boost his gubernatorial bid.

One chilly day in November 2025, community worker Mike Prado drove through Abilene, Tex., handing out blankets, socks, and jackets to unhoused individuals across the city. People sat on curbs, alleyway after alleyway, their meager belongings soaked by the previous night's hard rain. Prado has worked in this community for a decade, and was once homeless in Abilene himself. Prado has witnessed difficult years--but the current situation was the worst he'd ever seen, he told TIME. One man with a walker approached Prado outside of the Hope Haven offices--an Abilene nonprofit where Prado works, which operates a shelter and helps people with vouchers find housing--and accepted a jacket from him.

Machine learning predictions are increasingly used to supplement incomplete or costly-to-measure outcomes in fields such as biomedical research, environmental science, and social science. However, treating predictions as ground truth introduces bias while ignoring them wastes valuable information. Prediction-Powered Inference (PPI) offers a principled framework that leverages predictions from large unlabeled datasets to improve statistical efficiency while maintaining valid inference through explicit bias correction using a smaller labeled subset. Despite its potential, the growing PPI variants and the subtle distinctions between them have made it challenging for practitioners to determine when and how to apply these methods responsibly. This paper demystifies PPI by synthesizing its theoretical foundations, methodological extensions, connections to existing statistics literature, and diagnostic tools into a unified practical workflow. Using the Mosaiks housing price data, we show that PPI variants produce tighter confidence intervals than complete-case analysis, but that double-dipping, i.e. reusing training data for inference, leads to anti-conservative confidence intervals and coverages. Under missing-not-at-random mechanisms, all methods, including classical inference using only labeled data, yield biased estimates. We provide a decision flowchart linking assumption violations to appropriate PPI variants, a summary table of selective methods, and practical diagnostic strategies for evaluating core assumptions. By framing PPI as a general recipe rather than a single estimator, this work bridges methodological innovation and applied practice, helping researchers responsibly integrate predictions into valid inference.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Stage 9, also known as the Seinfeld Stage, where the show was produced along Republic Avenue at Radford Studio Center in 2023 in Studio City. Owner Hackman Capital Partners is ceding the 55-acre property to Goldman Sachs. This is read by an automated voice. Please report any issues or inconsistencies here .

Short-term forecasting of residential and commercial building energy consumption is widely used in power systems and continues to grow in importance. Data-driven short-term load forecasting (STLF), although promising, has suffered from a lack of open, large-scale datasets with high building diversity. This has hindered exploring the pretrain-then-fine-tune paradigm for STLF. To help address this, we present BuildingsBench, which consists of: 1) Buildings-900K, a large-scale dataset of 900K simulated buildings representing the U.S. building stock; and 2) an evaluation platform with over 1,900 real residential and commercial buildings from 7 open datasets. BuildingsBench benchmarks two under-explored tasks: zero-shot STLF, where a pretrained model is evaluated on unseen buildings without fine-tuning, and transfer learning, where a pretrained model is fine-tuned on a target building.