The boom-and-bust cycle has always been a feature of capitalism, and--capturing as it does the human traits of creativity, hope, greed,, anxiety, and panic--it always will be. Creativity gives rise to technological progress and transformative inventions, which provide a new driving force for the economy and a focal point for investors. Today, we are living through another speculative boom. This time the transformative invention is, of course, A.I., and last week's SpaceX I.P.O. While Elon Musk's creation is an impressive rocket-and-satellite company, the stunning $1.78-trillion valuation of the I.P.O. was largely based on its ambitions to build A.I. data centers in space, which remain largely untested .

The great German philosopher, who died in March, understood how much depended on a principled public sphere. Habermas emerged from the uncompromising Frankfurt School, but his work was considerably less fatalistic. You wake up and brace yourself for the barrage of toxic gibberish that constitutes the modern public sphere. Your e-mail is overrun with spam, scams, and smut. There are voice mails from no one about nothing. A glance at the news reveals that the President is continuing to spew lies and obscenities; that a trillionaire is peddling white-supremacist propaganda on a social-media platform he owns; that a chart-topping musical artist is praising Hitler, or apologizing for praising Hitler, or praising Hitler once again. Publications from the on down employ clickbait headlines that treat you like a starving rat in a Pavlovian experiment. A.I. systems simulate the experience of talking to an arrogant ten-year-old boy who knows far less than he thinks he does. When pressed, the chatbots admit that they cannot "naturally understand human morality, dignity, culture, or meaning." It all adds up to a continuous discursive tinnitus--a buzz of random, fake, stupid, sinister chatter that nobody wants and nobody can stop. The person who should have been best able to explain how we got here was the great German philosopher Jürgen Habermas, who illuminated how a feisty, principled public sphere is integral to democracy. But Habermas died in March, at the age of ninety-six, and, although he remained active until his final months, commenting on Ukraine, Gaza, and Eurobonds, he struggled to understand the turn history had taken. As a teen-ager in 1945, he had witnessed American soldiers enter his home town of Gummersbach, near Cologne, carrying messages of freedom and openness. Eight decades later, he watched American voters choose a leader who had advertised his fascistic bent in blood-and-soil rhetoric, fantasies of punitive violence, and a taste for bombastic architectural kitsch.

When Did White-Collar Work Start to Look So Bleak? In the nineteen-eighties, an office job promised security and fulfillment. For graduates starting careers today, the prospect is often tinged with dread. The workplace's sense of control can prove illusory--as it did in the era of yuppie-wrought corporate consolidation, and as it does now for graduates entering an economy destabilized by new uncertainties. This spring, across the nation's auditoriums and quadrangles, members of the class of 2026 took their seats to receive remarks from distinguished guests. The graduation speech is a thankless form: generalized, impersonal exhortation/congratulation is almost guaranteed to be forgettable, if all goes well. But this year, on at least a few American campuses, all did not go well. At the University of Arizona, Eric Schmidt, the former C.E.O. of Google, told the crowd that artificial intelligence "will touch every profession, every classroom, every hospital, every laboratory, every person, and every relationship you have," a sweeping promise that landed like a threat.

A new report warns that Miami, Kansas City, Philadelphia, Dallas, and Houston could be particularly hot places to play during the 2026 World Cup. Extreme heat will be one of the biggest challenges for players and fans during the 2026 FIFA World Cup . According to an analysis by the World Weather Attribution (WWA), around 25 percent of the 104 matches of the tournament could be played under temperatures that exceed the recommended thermal safety limits. The study points out that the probability of facing these conditions is almost double that recorded in the 1994 tournament held in the United States. The projections were developed using a statistical model designed to calculate the probability of each match being played in extremely hot conditions.

Recent years have witnessed significant advancements in industrial anomaly detection (IAD) thanks to existing anomaly detection datasets. However, the large performance gap between these benchmarks and real industrial practice reveals critical limitations in existing datasets. We argue that the mismatch between current datasets and real industrial scenarios becomes the primary barrier to practical IAD deployment. To this end, we propose ReinAD dataset, a comprehensive contrastive dataset towards Real-world industrial Anomaly Detection. Our dataset prioritizes three critical real-world requirements: 1) Contrast-based anomaly definition that is essential for industrial practice, 2) Fine-grained unaligned image pairs reflecting real inspections, and 3) Large-scale data from active production lines spanning multiple industrial categories. Based on our dataset, we introduce the ReinADNet. It takes both normal reference and test images as inputs, achieving anomaly detection through normal-anomaly comparison. To address the fine-grained and unaligned properties of real industrial scenes, our method integrates pyramidal similarity aggregation for comprehensive anomaly characterization and globallocal feature fusion for spatial misalignment tolerance. Our method outperforms all baselines on the ReinAD dataset (e.g., 64.5% v.s.

Chest X-rays (CXRs) are the most frequently performed imaging examinations in clinical settings. Recent advancements in Medical Large Multimodal Models (MLMMs) have enabled automated CXR interpretation, improving diagnostic accuracy and efficiency.

In the era of artificial intelligence (AI), pedestrian privacy is increasingly at risk. In research areas such as autonomous driving, computer vision, and surveillance, large datasets are often collected in public spaces, capturing pedestrians without consent or anonymization. These datasets are used to train systems that can identify, track, and analyze individuals, often without their knowledge. Although various technical methods and regional regulations have been proposed to address this issue, existing solutions are either insufficient to protect privacy or compromise data utility, thereby limiting their effectiveness for research. In this paper, we argue that more effort is needed to protect pedestrian privacy in the era of AI while maintaining data utility. We call on the AI and computer vision communities to take pedestrian privacy seriously and to rethink how pedestrian data are collected and anonymized. Collaboration with experts in law and ethics will also be essential for the responsible development of AI. Without stronger action, it will become increasingly difficult for individuals to protect their privacy, and public trust in AI may decline.

Fairness and action smoothness are two crucial considerations in many online optimization problems, but they have yet to be addressed simultaneously. In this paper, we study a new and challenging setting of fairness-regularized smoothed online convex optimization with switching costs. First, to highlight the fundamental challenges introduced by the long-term fairness regularizer evaluated based on the entire sequence of actions, we prove that even without switching costs, no online algorithms can possibly achieve a sublinear regret or finite competitive ratio compared to the offline optimal algorithm as the problem episode length T increases. Then, we propose FairOBD(Fairness-regularized Online Balanced Descent), which reconciles the tension between minimizing the hitting cost, switching cost, and fairness cost.

Aircraft manufacturing is the jewel in the crown of industry, in which generating high-fidelity airfoil geometries with controllable and editable representations remains a fundamental challenge. Existing deep learning methods, which typically rely on predefined parametric representations (e.g., Bézier curves) or discrete point sets, face an inherent trade-off between expressive power and resolution adaptability. To tackle this challenge, we introduce FuncGenFoil, a novel functionspace generative model that directly reconstructs airfoil geometries as function curves. Our method inherits the advantages of arbitrary-resolution sampling and smoothness from parametric functions, as well as the strong expressiveness of discrete point-based representations. Empirical evaluations demonstrate that FuncGenFoil improves upon state-of-the-art methods in airfoil generation, achieving a relative 74.4% reduction in label error and a 23.2% increase in diversity on the AF-200K dataset. Our results highlight the advantages of function-space modeling for aerodynamic shape optimization, offering a powerful and flexible framework for high-fidelity airfoil design.

While Large Language Models (LLMs) have gained remarkable success, they are consistently at risk of being stolen when deployed on untrusted edge devices. As a solution, TEE-based secure inference has been proposed to protect valuable model property. However, we identify a statistical vulnerability in existing protection methods, and furtherly compromise their security guarantees by proposed Model Stealing Attack with Prior. To eliminate this vulnerability, LoRO is presented in this paper, which leverages dense mask to completely obfuscate parameters. LoRO includes two innovations: (1) Low Rank Mask, which uses low-rank factors to generate dense masks efficiently. The computing complexity in TEE is hence reduced by an exponential amount to achieve inference speed up, while providing robust model confidentiality.