Figure Skaters at the 2026 Winter Olympics Are Pushing the Limits of What's Possible For years, quad axel jumps seemed impossible. Then Ilia Malinin landed one in 2022. As he heads to the Milano Cortina Games everyone wants to know what's next. In 2021, famed Russian figure skating coach Alexei Mishin said that no figure skater would ever be able to successfully perform a quad axel in his lifetime. The following year, two-time Olympic gold medalist Yuzuru Hanyu was training to master the jump, but when he attempted it at the 2022 Winter Games in Beijing, he fell short of finishing the four-and-a-half revolutions in the air. Mishin's pronouncement, it seemed, had been validated.

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Partially inspired by features of computation in visual cortex, deep neural networks compute hierarchical representations of their inputs. While these networks have been highly successful in machine learning, it is still unclear to what extent they can aid our understanding of cortical function. Several groups have developed metrics that provide a quantitative comparison between representations computed by networks and representations measured in cortex. At the same time, neuroscience is well into an unprecedented phase of large-scale data collection, as evidenced by projects such as the Allen Brain Observatory. Despite the magnitude of these efforts, in a given experiment only a fraction of units are recorded, limiting the information available about the cortical representation.

As deepfake video technology improves, the scale of online fraud will grow even further, experts say. As deepfake video technology improves, the scale of online fraud will grow even further, experts say. AI content for scams can be targeted at individuals and'produced by pretty much anybody', researchers say Deepfake fraud has gone "industrial", an analysis published by AI experts has said. Tools to create tailored, even personalised, scams - leveraging, for example, deepfake videos of Swedish journalists or the president of Cyprus - are no longer niche, but inexpensive and easy to deploy at scale, said the analysis from the AI Incident Database . These examples are part of a trend in which scammers are using widely available AI tools to perpetuate increasingly targeted heists.

Although the popular MNIST dataset \citep{mnist} is derived from the NIST database \citep{nist-sd19}, precise processing steps of this derivation have been lost to time. We propose a reconstruction that is accurate enough to serve as a replacement for the MNIST dataset, with insignificant changes in accuracy. We trace each MNIST digit to its NIST source and its rich metadata such as writer identifier, partition identifier, etc. We also reconstruct the complete MNIST test set with 60,000 samples instead of the usual 10,000. Since the balance 50,000 were never distributed, they enable us to investigate the impact of twenty-five years of MNIST experiments on the reported testing performances. Our results unambiguously confirm the trends observed by \citet{recht2018cifar,recht2019imagenet}: although the misclassification rates are slightly off, classifier ordering and model selection remain broadly reliable. We attribute this phenomenon to the pairing benefits of comparing classifiers on the same digits.

Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' How the US left Ukraine exposed to Russia's winter war Nighttime shelling by Ukrainian forces inflicted "serious damage" on the Russian city of Belgorod, the region's Governor Vyacheslav Gladkov said. "The enemy has shelled the civilian city of Belgorod. Everyone knows we have no military targets. There has been serious damage. I have been out to look around," Gladkov said on the Telegram messaging app.

Officially named'Essential,' the torch was designed by Carlo Ratti and weighs only 2.5 pounds. Breakthroughs, discoveries, and DIY tips sent six days a week. Every Olympic Games has a torch. Every torch has a designer. For the 2026 Milano Cortina Olympic Games and Paralympic Games, that designer is MIT engineer and architect Carlo Ratti .

We consider a causal inference problem frequently encountered in online advertising systems, where a publisher (e.g., Instagram, TikTok) interacts repeatedly with human users and advertisers by sporadically displaying to each user an advertisement selected through an auction. Each treatment corresponds to a parameter value of the advertising mechanism (e.g., auction reserve-price), and we want to estimate through experiments the corresponding long-term treatment effect (e.g., annual advertising revenue). In our setting, the treatment affects not only the instantaneous revenue from showing an ad, but also changes each user's interaction-trajectory, and each advertiser's bidding policy -- as the latter is constrained by a finite budget. In particular, each a treatment may even affect the size of the population, since users interact longer with a tolerable advertising mechanism. We drop the classical i.i.d. assumption and model the experiment measurements (e.g., advertising revenue) as a stopped random walk, and use a budget-splitting experimental design, the Anscombe Theorem, a Wald-like equation, and a Central Limit Theorem to construct confidence intervals for the long-term treatment effect.

Linear attention methods offer Transformers $O(N)$ complexity but typically underperform standard softmax attention. We identify two fundamental limitations affecting these approaches: the restriction to convex combinations that only permits additive information blending, and uniform accumulated weight bias that dilutes attention in long contexts. We propose Zero-Sum Linear Attention (ZeroS), which addresses these limitations by removing the constant zero-order term $1/t$ and reweighting the remaining zero-sum softmax residuals. This modification creates mathematically stable weights, enabling both positive and negative values and allowing a single attention layer to perform contrastive operations. While maintaining $O(N)$ complexity, ZeroS theoretically expands the set of representable functions compared to convex combinations. Empirically, it matches or exceeds standard softmax attention across various sequence modeling benchmarks.