The COVID-19 pandemic prompted a surge in computational models to simulate disease dynamics and guide interventions. Agent-based models (ABMs) are well-suited to capture population and environmental heterogeneity, but their rapid deployment raised questions about utility for health policy. We systematically reviewed 536 COVID-19 ABM studies published from January 2020 to December 2023, retrieved from Web of Science, PubMed, and Wiley on January 30, 2024. Studies were included if they used ABMs to simulate COVID-19 transmission, where reviews were excluded. Studies were assessed against nine criteria of model usefulness, including transparency and re-use, interdisciplinary collaboration and stakeholder engagement, and evaluation practices. Publications peaked in late 2021 and were concentrated in a few countries. Most models explored behavioral or policy interventions (n = 294, 54.85%) rather than real-time forecasting (n = 9, 1.68%). While most described model assumptions (n = 491, 91.60%), fewer disclosed limitations (n = 349, 65.11%), shared code (n = 219, 40.86%), or built on existing models (n = 195, 36.38%). Standardized reporting protocols (n = 36, 6.72%) and stakeholder engagement were rare (13.62%, n = 73). Only 2.24% (n = 12) described a comprehensive validation framework, though uncertainty was often quantified (n = 407, 75.93%). Limitations of this review include underrepresentation of non-English studies, subjective data extraction, variability in study quality, and limited generalizability. Overall, COVID-19 ABMs advanced quickly, but lacked transparency, accessibility, and participatory engagement. Stronger standards are needed for ABMs to serve as reliable decision-support tools in future public health crises.

Generating regulatorily compliant Suspicious Activity Report (SAR) remains a high-cost, low-scalability bottleneck in Anti-Money Laundering (AML) workflows. While large language models (LLMs) offer promising fluency, they suffer from factual hallucination, limited crime typology alignment, and poor explainability -- posing unacceptable risks in compliance-critical domains. This paper introduces Co-Investigator AI, an agentic framework optimized to produce Suspicious Activity Reports (SARs) significantly faster and with greater accuracy than traditional methods. Drawing inspiration from recent advances in autonomous agent architectures, such as the AI Co-Scientist, our approach integrates specialized agents for planning, crime type detection, external intelligence gathering, and compliance validation. The system features dynamic memory management, an AI-Privacy Guard layer for sensitive data handling, and a real-time validation agent employing the Agent-as-a-Judge paradigm to ensure continuous narrative quality assurance. Human investigators remain firmly in the loop, empowered to review and refine drafts in a collaborative workflow that blends AI efficiency with domain expertise. We demonstrate the versatility of Co-Investigator AI across a range of complex financial crime scenarios, highlighting its ability to streamline SAR drafting, align narratives with regulatory expectations, and enable compliance teams to focus on higher-order analytical work. This approach marks the beginning of a new era in compliance reporting -- bringing the transformative benefits of AI agents to the core of regulatory processes and paving the way for scalable, reliable, and transparent SAR generation.

A House Oversight Committee hearing produced a flood of bizarre claims about cloud seeding, chemtrails, and solar geoengineering. Proven, human-driven changes to the weather were dismissed. "What this whole debate comes down to is who controls the skies," Republican representative Marjorie Taylor Greene of Georgia told the audience at a House Oversight Committee hearing on Tuesday. "Do we believe in God and that he has dominion over his perfect creation of planet Earth? Do we believe that he has given us everything we need to survive as a civilization since the beginning of time? Or do you believe in man's claim of authority over the weather, based on scientists that have only been alive for decades and weren't here to witness the climate changes since the beginning of time?"

The state banquet is the spectacular showstopper of a state visit, a glittering feast with speeches, royal toasts, trumpet fanfares and fancy food and wine. It's diplomacy served up with fine dining. A cut-glass shock-and-awe approach to hospitality designed to make a visiting leader like President Trump feel special. The setting in St George's Hall inside Windsor Castle is a remarkable sight, a mix of medieval banquet and Harry Potter film. Elaborately uniformed staff around the hall are as drilled as the soldiers who have been on parade during the day.

Silicon Valley startup Tensor plans to sell the world's first personal robocar, allowing consumers to own self-driving cars by 2026.

Fired CDC Director Says RFK Jr. Pressured Her to Blindly Approve Vaccine Changes Susan Monarez told a Senate committee that Robert F. Kennedy Jr. demanded she dismiss career officials without cause--and accept vaccine recommendations regardless of whether science backed them up. Susan Monarez testifies before the Senate on June 25, 2025. At a Senate hearing on Wednesday, former director of the US Centers for Disease Control and Prevention Susan Monarez said she was fired from her role for not rubber-stamping vaccine recommendations from her boss, Health and Human Services secretary Robert F. Kennedy Jr., regardless of whether they were backed by scientific evidence. Just two months after Monarez was sworn in to the job, HHS announced on August 27 that she was no longer the director of the CDC. She had been the acting director since January and was the first CDC director to receive Senate confirmation after a law took effect this year requiring the president's nominee to receive Senate approval.

Steven Pinker's new book shows how he's become a contradictory figure Steven Pinker's new book When Everyone Knows That Everyone Knows makes a compelling case for common knowledge. Steven Pinker argues that "cancel culture" is a form of censorship Steven Pinker's new book perfectly encapsulates what a contradictory figure he has become. Much of it is a clear, fascinating explanation of a major psychological phenomenon . But then he starts telling you what he thinks about current affairs. Pinker is a psychologist at Harvard University who has written a string of popular science books. Some, like Words and Rules, are rooted in his own research and are a good read.

The federal government played a critical role in creating computational science. From the early Cold War-era funding of nuclear weapons simulation, federal agencies such as the Department of Energy (DOE), the National Science Foundation (NSF), the National Aeronautics and Space Administration (NASA), and the Defense Advanced Research Projects Agency (DARPA) have provided the long-term, high-risk investment necessary to build computing infrastructure and advance algorithmic research. The vision for computational modeling as a peer to theory and experiment began to emerge in the latter half of the 20th century. As recounted in my ACM Turing Award lecture,9 strategic investments in scientific computing established a vast ecosystem of mathematical software, numerical libraries, and performance benchmarks. This effort, driven by DOE and NSF investments, paved the way for a new model of discovery in which complex physical phenomena could be simulated with increasing realism.

When you purchase through links in our articles, we may earn a small commission. The large language model is putting in new security features after high-profile cases of teen suicides, and may require that adults verify with scanned identification in some countries. Cases of "AI psychosis" are apparently on the rise, and multiple people have committed suicide after conversing with the ChatGPT large language model. Representatives of ChatGPT maker OpenAI are testifying before the US congress in response, and the company is announcing new methods of detecting users' age. According to the CEO, that may include ID verification.

Artificial intelligence can predict people's health problems over a decade into the future, say scientists. The technology has learned to spot patterns in people's medical records to calculate their risk of more than 1,000 diseases. The researchers say it is like a weather forecast that anticipates a 70% chance of rain - but for human health. Their vision is to use the AI model to spot high-risk patients to prevent disease and to help hospitals understand demand in their area, years ahead of time. The model - called Delphi-2M - uses similar technology to well-known AI chatbots like ChatGPT.