Harmful nitrogen dioxide and ozone levels could affect fertility and pregnancies. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . Air quality is linked to a wide range of health issues including respiratory problems, cardiovascular complications, and cancer, and that list of concerns only continues to grow .

For decades, the senator has argued that concentrated wealth threatened American democracy. Now he's betting that frustration with Big Tech, billionaires, and unchecked AI is reaching a tipping point. It's hard to believe Bernie Sanders . Not because the longtime Vermont senator bears the hallmarks of a liar. Yes, he's a career politician, but the 84-year-old progressive torchbearer counts more viral memes than scandals to his name. Rather, it's hard to believe Bernie Sanders because, for decades, he's told Americans that this country can radically change, while championing ideas too far afield from the status quo to really have a chance. He wants to bring billionaires to heel, for one. And implement universal, government-run health care. If Sanders had his way, it wouldn't even exist. I believe it, and WIRED champions it. Sanders, though, is now hard at work adding one more big, improbable change to the pile: Since 2023, he's been advocating for firm and decisive regulation of the AI industry . In March of this year, Sanders and his frequent collaborator, Representative Alexandria Ocasio-Cortez, proposed legislation that would halt data center construction until a series of safeguards are implemented. In June, Sanders announced the American AI Sovereign Wealth Fund Act, which would essentially tax AI's richest companies and result in direct payments to American citizens. I wanted to talk to Sanders about those bills, and his perspective on AI more broadly. On a deeper level, though, I was curious about how Sanders sees the barriers to regulation--from tech oligarchs and deep-pocketed super PACs, to a federal administration happier to enrich itself via technology than actually govern it--and whether he thinks those seemingly intractable obstacles can be overcome. After a few months of haranguing, Sanders agreed to sit down, which is how I found myself in his modest DC campaign office watching the senator--thoughtful, genuine, vociferous as ever--grapple in real time with what he describes as "the most consequential, transformational technology in the history of humanity." Sanders and I spoke on Tuesday, June 23, as the New York Democratic primary was underway. I woke up the next day, our conversation echoing in my head, to find that a coalition of democratic socialists had swept their respective elections and sent party stalwarts into an existential tailspin. A few hours later, New Jersey representative Frank Pallone, the top Democrat on the House Energy and Commerce Committee, became the most mainstream member of the party to publicly support an AI data center moratorium .

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Visual autoregressive modeling has recently demonstrated potential in image tasks by enabling coarse-to-fine, next-level prediction. Most indoor 3D occupancy prediction methods, however, continue to rely on dense voxel grids and convolution-heavy backbones, which incur high computational costs when applying such coarse-tofine frameworks. In contrast, cost-efficient alternatives based on Gaussian representations--particularly in the context of multi-scale autoregression--remain underexplored. To bridge this gap, we propose DFGauss, a Dynamic Focused masking framework for multi-scale 3DGaussian representation. Unlike conventional approaches that refine voxel volumes or 2D projections, DFGauss directly operates in the 3DGaussian parameter space, progressively refining representations across resolutions under hierarchical supervision. Each finer-scale Gaussian is conditioned on its coarser-level counterpart, forming a scale-wise autoregressive process. To further enhance efficiency, we introduce an importance-guided refinement strategy that selectively propagates informative Gaussians across scales, enabling spatially adaptive detail modeling. Experiments on 3D occupancy benchmarks demonstrate that DFGauss achieves competitive performance, highlighting the promise of autoregressive modeling for scalable 3D occupancy prediction.

Randomized experiments are the preferred approach for evaluating the effects of interventions, but they are costly and often yield estimates with substantial uncertainty. On the other hand, in silico experiments leveraging foundation models offer a cost-effective alternative that can potentially attain higher statistical precision. However, the benefits of in silico experiments come with a significant risk: statistical inferences are not valid if the models fail to accurately predict experimental responses to interventions. In this paper, we propose a novel approach that integrates the predictions from multiple foundation models with experimental data while preserving valid statistical inference. Our estimator is consistent and asymptotically normal, with asymptotic variance no larger than the standard estimator based on experimental data alone. Importantly, these statistical properties hold even when model predictions are arbitrarily biased. Empirical results across several randomized experiments show that our estimator offers substantial precision gains, equivalent to a reduction of up to 20% in the sample size needed to match the same precision as the standard estimator based on experimental data alone.

Recent advances in retrieval-based in-context learning (ICL) train the retriever using a classification objective, which categorizes in-context examples (ICEs) into the most useful and the rest based on absolute scores. However, during inference, ICEs are retrieved by score ranking rather than classification -- The classification training objective deviates from this test scenario. Hence, in this paper, we propose a novel algorithm that trains a retrieval model by ranking formulation, where the preference rankings between ICEs are given by comparing the likelihood of the LLM generating the correct answer conditioned on each exemplar. By learning to rank, we motivate the retriever to automatically learn diverse rationales why specific examples are more useful for ICL decisions. This addresses the issue that classification models poorly capture broader utility. Experimental results demonstrate the top-1 performance of our proposal across 9 NLP tasks, with ablation studies and case studies further validating the effectiveness of our design.

The ancient relationship between the ancient arthropods and shorebirds drives a 9,000-mile journey. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . Few creatures wear the mantle of deep time as visibly as, better known as the Atlantic horseshoe crab .

Google's Debug project is seeking EPA approval to release millions of sterile male mosquitoes in New Jersey, California and Florida to reduce disease-spreading populations.

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The games start June 11 and end with a grand finale in New Jersey on July 19. There are 104 of them. Here's how to watch'em all. The FIFA Men's World Cup is almost here, and this one will be the biggest ever. The tournament is hosted by three countries: Mexico, Canada, and the US.