This week has been an eventful one for America's public health agency. Two former leaders of the US Centers for Disease Control and Prevention explained why they suddenly departed in a Senate hearing. They also described how CDC employees are being instructed to turn their backs on scientific evidence. They painted a picture of a health agency in turmoil--and at risk of harming the people it is meant to serve. And, just hours afterwards, a panel of CDC advisers voted to stop recommending the MMRV vaccine for children under four. This article first appeared in The Checkup, MIT Technology Review's weekly biotech newsletter.

A drone crashed into a hotel in the southern Israeli city of Eilat on Thursday, causing a fire but no casualties, authorities said. Yemen's Houthi group, who have been firing drones and missiles in solidarity with Palestinians in Gaza, claimed responsibility for the attack. Palestinians turn to the sea to flee Israel's bombardment Trump says US wants Afghanistan's Bagram Air Base back from Taliban What did Jimmy Kimmel say about Charlie Kirk's killing?

Tours are being offered in Tokyo using a shoulder-mounted robot equipped with a camera, speaker and microphone. The robot twitches its wing-like arms, which it can fold together as if in prayer when visiting a temple. In the business district of Tokyo's Nihonbashi, groups of tourists are on guided tours of the area. But rather than being accompanied by a person, they're being shown around by a shoulder-mounted robot. If all goes well, these bilingual human-controlled robots, conversant in English and Japanese, could become a common sight in the capital. Created by robotics company OryLab, OriHime is a white robot with green eyes, weighing 4 kilograms.

Kim Jong Un declares AI military drone development a'top priority' North Korea's Supreme Leader Kim Jong Un has said the use of artificial intelligence is a "top priority" in modernising his country's increasingly sophisticated weapons technology and building up drone capabilities, state media reports. During a visit to the Unmanned Aeronautical Technology Complex in the capital Pyongyang on Thursday, Kim presided over performance tests of multipurpose drones and unmanned surveillance vehicles, North Korea's Korean Central News Agency (KCNA) said on Friday. Kim also called for "expanding and strengthening the serial production capacity of drones". The visit to the aeronautical complex comes just a week after Kim oversaw another test of a new solid-fuel rocket engine designed for intercontinental ballistic missiles, which he hailed as a "significant" expansion of Pyongyang's nuclear capabilities. North Korea's military power includes nuclear-armed ballistic and cruise missiles, an increasing stockpile of nuclear weapons and a nascent spy satellite programme, according to the United States Defense Intelligence Agency (DIA).

Robert Cherone, 45, was killed at Palermo's Pizza West Milwaukee facility after being crushed by a robotic machine, prompting an OSHA investigation.

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? Ukrainian drones hit a key oil-processing and petrochemical complex in Russia's Bashkortostan region, as well as an oil refinery in the Volgograd region, as Ukraine escalates its campaign against Russia's extensive oil and gas sector. Russian military units claim to have breached Ukraine's western village of Yampol and secured new positions near five residential areas in the same area, according to Russia's state TASS news agency.

As autonomous technologies increasingly shape maritime operations, understanding why an AI system makes a decision becomes as crucial as what it decides. In complex and dynamic maritime environments, trust in AI depends not only on performance but also on transparency and interpretability. This paper highlights the importance of Explainable AI (xAI) as a foundation for effective human-machine teaming in the maritime domain, where informed oversight and shared understanding are essential. To support the user-centered integration of xAI, we propose a domain-specific survey designed to capture maritime professionals' perceptions of trust, usability, and explainability. Our aim is to foster awareness and guide the development of user-centric xAI systems tailored to the needs of seafarers and maritime teams.

Context: Dynamic production environments make it challenging to maintain reliable machine learning (ML) systems. Runtime issues, such as changes in data patterns or operating contexts, that degrade model performance are a common occurrence in production settings. Monitoring enables early detection and mitigation of these runtime issues, helping maintain users' trust and prevent unwanted consequences for organizations. Aim: This study aims to provide a comprehensive overview of the ML monitoring literature. Method: We conducted a multivocal literature review (MLR) following the well established guidelines by Garousi to investigate various aspects of ML monitoring approaches in 136 papers. Results: We analyzed selected studies based on four key areas: (1) the motivations, goals, and context; (2) the monitored aspects, specific techniques, metrics, and tools; (3) the contributions and benefits; and (4) the current limitations. We also discuss several insights found in the studies, their implications, and recommendations for future research and practice. Conclusion: Our MLR identifies and summarizes ML monitoring practices and gaps, emphasizing similarities and disconnects between formal and gray literature. Our study is valuable for both academics and practitioners, as it helps select appropriate solutions, highlights limitations in current approaches, and provides future directions for research and tool development.

Recent advances in multi-modal large language models (MLLMs) have enabled increasingly sophisticated autonomous visualization agents capable of translating user intentions into data visualizations. However, measuring progress and comparing different agents remains challenging, particularly in scientific visualization (SciVis), due to the absence of comprehensive, large-scale benchmarks for evaluating real-world capabilities. This position paper examines the various types of evaluation required for SciVis agents, outlines the associated challenges, provides a simple proof-of-concept evaluation example, and discusses how evaluation benchmarks can facilitate agent self-improvement. We advocate for a broader collaboration to develop a SciVis agentic evaluation benchmark that would not only assess existing capabilities but also drive innovation and stimulate future development in the field.

We investigate a WIMP dark matter (DM) candidate in the form of a singlino-dominated lightest supersymmetric particle (LSP) within the $Z_3$-symmetric Next-to-Minimal Supersymmetric Standard Model. This framework gives rise to regions of parameter space where DM is obtained via co-annihilation with nearby higgsino-like electroweakinos and DM direct detection~signals are suppressed, the so-called ``blind spots". On the other hand, collider signatures remain promising due to enhanced radiative decay modes of higgsinos into the singlino-dominated LSP and a photon, rather than into leptons or hadrons. This motivates searches for radiatively decaying neutralinos, however, these signals face substantial background challenges, as the decay products are typically soft due to the small mass-splits ($Δm$) between the LSP and the higgsino-like coannihilation partners. We apply a data-driven Machine Learning (ML) analysis that improves sensitivity to these subtle signals, offering a powerful complement to traditional search strategies to discover a new physics scenario. Using an LHC integrated luminosity of $100~\mathrm{fb}^{-1}$ at $14~\mathrm{TeV}$, the method achieves a $5σ$ discovery reach for higgsino masses up to $225~\mathrm{GeV}$ with $Δm\!\lesssim\!12~\mathrm{GeV}$, and a $2σ$ exclusion up to $285~\mathrm{GeV}$ with $Δm\!\lesssim\!20~\mathrm{GeV}$. These results highlight the power of collider searches to probe DM candidates that remain hidden from current direct detection experiments, and provide a motivation for a search by the LHC collaborations using ML methods.