This week we welcomed a record-breaking baby to the world. Thaddeus Daniel Pierce, who arrived over the weekend, developed from an embryo that was frozen in storage for 30 and a half years. You could call him the world's oldest baby. His parents, Lindsey and Tim Pierce, were themselves only young children when that embryo was created, all the way back in 1994. Linda Archerd, who donated the embryo, described the experience as "surreal." Stories like this also highlight how reproductive technologies are shaping families.

Chicago Mayor Brandon Johnson addressed his controversial support for a 1% tax on groceries after a state tax is set to expire during a press conference. The Chicago Tribune warned on Thursday that Mayor Brandon Johnson's progressive policy proposals may scare businesses away from the already struggling city. As officials anticipate a 1.2 billion deficit, Johnson spoke to reporters on Tuesday about his plans to fix the local economy, particularly how the "billionaires and ultra-rich" can have "more skin in the game." "Everything has to be on the table. Everything has to be on the table," Johnson said of his plans.

President Donald Trump's administration has dramatically expanded its work with Palantir, elevating the company cofounded by Trump ally Peter Thiel as the government's go-to software developer. Following massive contract terminations for consulting giants and government contractors like Accenture, Booz Allen, and Deloitte, Palantir has emerged ahead. Now the data analytics firm is partnering with those companies--offering them a lifeline while consolidating its own power. Palantir has become one of the few winners in the Trump administration's cost-cutting efforts, receiving more than 113 million in federal spending since the beginning of the year, according to The New York Times. Palantir's US government revenue has grown by more than 370 million compared to this time last year, according to the company's most recent quarterly earnings report.

The government made a commitment to banning ninja swords in its 2024 election manifesto, as part of the Labour Party's pledge to "take back our streets" through a knife crime action plan. Home Secretary Yvette Cooper later set out plans to crackdown on crime at the party's conference in 2024, when she said the government would attempt to halve knife offences in a decade. New data from the government revealed that the number of reported robberies involving a knife dropped by 6%, from 16,068 to 15,028, between June 2024 and June 2025. Ministers also announced an extra 5m would be made available for police and local authorities to try new, targeted actions on knife crime. The new money will be used for knife crime hotspots across the country, where targeted intervention tactics will be trialled, including facial recognition technology and the use of drones to support police officers on the streets.

Ukrainian President Volodymyr Zelenskyy on Thursday urged his allies to bring about "regime change" in Russia, hours after a Russian drone and missile attack on Kyiv killed 16 people, including a 6-year-old boy. The overnight strikes reduced part of a nine-story apartment block in Kyiv's western suburbs to rubble and wounded at least 150 people in the capital, authorities said. The Russian army, meanwhile, claimed to have captured Chasiv Yar, a strategically important hillside town in eastern Ukraine where the two sides have been fiercely fighting for months.

Artificial Intelligence (AI) systems are transforming critical sectors such as healthcare, finance, and transportation, enhancing operational efficiency and decision-making processes. However, their deployment in high-stakes domains has exposed vulnerabilities that can result in significant societal harm. To systematically study and mitigate these risk, initiatives like the AI Incident Database (AIID) have emerged, cataloging over 3,000 real-world AI failure reports. Currently, associating a new report with the appropriate AI Incident relies on manual expert intervention, limiting scalability and delaying the identification of emerging failure patterns. To address this limitation, we propose a retrieval-based framework that automates the association of new reports with existing AI Incidents through semantic similarity modeling. We formalize the task as a ranking problem, where each report-comprising a title and a full textual description-is compared to previously documented AI Incidents based on embedding cosine similarity. Benchmarking traditional lexical methods, cross-encoder architectures, and transformer-based sentence embedding models, we find that the latter consistently achieve superior performance. Our analysis further shows that combining titles and descriptions yields substantial improvements in ranking accuracy compared to using titles alone. Moreover, retrieval performance remains stable across variations in description length, highlighting the robustness of the framework. Finally, we find that retrieval performance consistently improves as the training set expands. Our approach provides a scalable and efficient solution for supporting the maintenance of the AIID.

--Federated Learning (FL) enables collaborative model training across distributed medical institutions while preserving patient privacy, but remains vulnerable to Byzantine attacks and statistical heterogeneity. T o address convergence challenges under data heterogeneity, we develop FedBN-Prox (FedBN-P), combining Federated Batch Normalization with proximal regularization for optimal accuracy-convergence trade-offs. Extensive evaluation across MNIST, CIF AR-10, and Alzheimer's MRI datasets under various Byzantine attack scenarios demonstrates significant improvements over state-of-the-art defenses, achieving up to +1.6 percentage points over FLGuard under non-IID conditions while maintaining robust performance against diverse attack patterns through our adaptive learning approach. In recent years, Federated Learning (FL) has emerged as a powerful paradigm for training deep neural networks across geographically distributed hospitals while preserving patient privacy under stringent regulations such as HIP AA and GDPR [1]-[4]. Recent advances in federated learning for healthcare have shown significant promise in addressing privacy-sensitive medical data challenges through innovative approaches such as secure multi-party computation [5] and blockchain-enhanced frameworks [6] while enabling secure collaborative learning across medical institutions. As illustrated in Figure 1, this collaborative framework allows medical institutions to exchange model updates rather than raw MRI scans, enabling multi-institutional collaboration--for instance, a small rural hospital with just a handful of Alzheimer's MRI scans can still contribute to, and benefit from, a model jointly trained with top-tier research centers. However, real-world FL deployments must cope with two intertwined challenges. First, Byzantine updates --malicious or low-quality gradient submissions--can severely skew the global model and compromise clinical reliability [7], [8], arising from hospitals with insufficient labeled data, poor-quality imaging equipment, or adversarial behavior.

As artificial intelligence systems increasingly inform high-stakes decisions across sectors, transparency has become foundational to responsible and trustworthy AI implementation. Leveraging our role as a leading institute in advancing AI research and enabling industry adoption, we present key insights and lessons learned from practical interpretability applications across diverse domains. This paper offers actionable strategies and implementation guidance tailored to organizations at varying stages of AI maturity, emphasizing the integration of interpretability as a core design principle rather than a retrospective add-on.

Large language models (LLMs) increasingly serve as educational tools, yet evaluating their teaching capabilities remains challenging due to the resource-intensive, context-dependent, and methodologically complex nature of teacher-student interactions. We introduce EducationQ, a multi-agent dialogue framework that efficiently assesses teaching capabilities through simulated dynamic educational scenarios, featuring specialized agents for teaching, learning, and evaluation. Testing 14 LLMs across major AI Organizations (OpenAI, Meta, Google, Anthropic, and others) on 1,498 questions spanning 13 disciplines and 10 difficulty levels reveals that teaching effectiveness does not correlate linearly with model scale or general reasoning capabilities - with some smaller open-source models outperforming larger commercial counterparts in teaching contexts. This finding highlights a critical gap in current evaluations that prioritize knowledge recall over interactive pedagogy. Our mixed-methods evaluation, combining quantitative metrics with qualitative analysis and expert case studies, identifies distinct pedagogical strengths employed by top-performing models (e.g., sophisticated questioning strategies, adaptive feedback mechanisms). Human expert evaluations show 78% agreement with our automated qualitative analysis of effective teaching behaviors, validating our methodology. EducationQ demonstrates that LLMs-as-teachers require specialized optimization beyond simple scaling, suggesting next-generation educational AI prioritize targeted enhancement of specific pedagogical effectiveness.

Variation in language use, shaped by speakers' sociocultural background and specific context of use, offers a rich lens into cultural perspectives, values, and opinions. However, the computational study of these Sociocultural Linguistic Phenomena (SLP) has often been limited to bespoke analyses of specific groups or topics, hindering the pace of scientific discovery. To address this, we introduce Splits!, a 9.7 million-post dataset from Reddit designed for systematic and flexible research. The dataset contains posts from over 53,000 users across 6 demographic groups, organized into 89 discussion topics to enable comparative analysis. We validate Splits! via self-identification and by successfully replicating several known SLPs from existing literature. We complement this dataset with a framework that leverages efficient retrieval methods to rapidly validate potential SLPs (PSLPs) by automatically evaluating whether a given hypothesis is supported by our data. Crucially, to distinguish between novel and obvious insights, the framework incorporates a human-validated measure of a hypothesis's ``unexpectedness.'' We demonstrate that the two-stage process reduces the number of statistically significant findings requiring manual inspection by a factor of 1.5-1.8x, streamlining the discovery of promising phenomena for further investigation.