Plus: A judge has blocked the Pentagon's blacklisting of Anthropic. A startup claims it's found a drug to make your blood young I knew I'd officially become a "longevity influencer" when a company called Generation Lab offered me the chance to write about--and even receive--their new rejuvenation treatment. A company fact sheet says that it "blocks the systemic spread of aging in the bloodstream, reawakens the body's own repair mechanism and restores health and youth to multiple tissues." The approach is based on research by Generation Lab's irascible scientific founder, Irina Conboy. She found that joining the circulatory systems of old and young mice improved the old animals' ability to heal from injury. Conboy now says she has found a combination of two existing drugs that can produce youthful effects without the need for any bodily fluid exchange.

Kimi Onoda, minister for intellectual property strategy, speaks to reporters Tuesday about the government's adoption of guiding principles on intellectual rights protection. The government on Tuesday adopted guiding principles on intellectual property protection for generative artificial intelligence operators, calling on such businesses to disclose an outline of data and methods used to train their AI tools. The Principles Code, while not legally binding, is designed to boost transparency related to AI and promote both the protection of intellectual property rights and innovation amid the rapidly spreading use of the cutting-edge technology. Businesses that accept all or part of the code will notify the government and disclose the learning processes, types of learning data and methods of collecting such data for their generative AI models on their websites. The code covers not only domestic businesses but also overseas operators that provide AI systems and services in Japan. There are growing concerns that texts, images and other materials are being used by generative AI for learning without permission, potentially resulting in intellectual property rights violations.

A government advisory panel is planning to discuss how best to use AI in education amid growing concerns the technology could be reducing the ability of students to think for themselves. A Japanese government advisory panel is set to start considering ways to appropriately and effectively utilize artificial intelligence in school education amid the continuing evolution of the new cutting-edge technology. At a general meeting of the Central Council for Education on Friday, education minister Yohei Matsumoto asked the panel for discussions on the government's next five-year basic education promotion program from fiscal 2028. The council plans to compile its recommendations in fiscal 2027, which begins next April. There are growing concerns that excessive reliance on AI may reduce opportunities for students to think and make decisions on their own.

The games industry's obsession with visual fidelity is costing them money and talent Big developers are discovering that the technical progress of photorealism doesn't always translate into the creative spark that gives gamers joy - and keeps them playing Don't get Pushing Buttons delivered to your inbox? The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. L ast week, Asha Sharma, the new head of Xbox, laid out her vision for the future of the console and interestingly, the two titles she picked out as potential areas of growth were Minecraft and Candy Crush Saga - games that don't exactly scream next-generation entertainment. Neither relies on hyper-realistic visuals, which is the north star that games consoles and gaming PCs have been aiming at for the past 40 years.

Would you choose £50,000 over the chance of £1m? You have the choice of instantly receiving £50,000 or flipping a coin for a 50/50 chance of £1m. The vast majority decide on taking the £50k, according to a survey of thousands of people by YouGov. Women voted 82% in favour of the guaranteed cash. The poll has sparked a debate about why Brits appear more risk-averse than people in the US.

Travel, said users should'scroll past these summaries and look at guest reviews'. Travel, said users should'scroll past these summaries and look at guest reviews'. AI-generated overview found to gloss over allegations of sexual harassment and describes hotel being sued over hygiene as'spotless' A hotel being sued for mass food poisonings was described as "spotless" and a resort where guests complained of sexual harassment by staff was praised for "friendly" service by an AI intended to summarise millions of Tripadvisor reviews. The overviews of customer feedback downplayed serious complaints, ranging from the stench of mould to a lack of mains water, according to an investigation by the consumer campaign organisation Which? The AI-generated reviews appear on the travel website's hotel webpages to help holidaymakers decide where to book.

Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols. Retraining DNN models to address these distribution shifts is often impractical due to the high cost of acquiring and annotating new medical datasets. To address this, we introduce VarDeepPCA, a novel lightweight variational DNN framework designed to restore/refine degraded segmentation maps by leveraging intrinsic geometric priors. Unlike existing approaches that require target-domain data or extensive pre-training, our VarDeepPCA explicitly learns a distribution of valid anatomical geometries using only small in-distribution (ID) datasets. Theoretically, our novel variational learning framework leverages a reinterpretation of the softmax mapping to implicitly perform exact distribution modeling, thereby enabling computationally efficient, sampling-free learning and inference. This also enables VarDeepPCA to provide uncertainty estimates associated with its restored segmentation maps. We empirically validate our framework across 4 distinct clinical applications, using 14 publicly available datasets, involving segmentation of the myocardium, neuroretinal rim, prostate, and fetal head. Comparisons against 15 existing methods demonstrate that VarDeepPCA consistently restores segmentation maps produced by the existing methods on OOD data to (i) significantly improve anatomical plausibility of geometries and clinical utility of the segmentations, and (ii) significantly reduce errors, without needing any more training data than that used by existing methods.

Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures that actively mediate human opportunity. As algorithms increasingly determine access to economic and social opportunities, it has become widely recognized that these systems are deeply embedded with the structural inequalities and prejudices of their environments. The field of algorithmic fairness emerged in response to the growing recognition that models optimized for predictive accuracy can systematically disadvantage marginalized groups. Early mitigation strategies, however, rested on fragile simplifications that limited their effectiveness in complex sociotechnical environments. This thesis identifies and addresses two fundamental limitations of contemporary fairness paradigms: the reliance on deterministic point estimates for auditing and the treatment of individuals as isolated entities devoid of structural context. First, the diagnosis of algorithmic unfairness has traditionally depended on scalar metrics that fail to capture the nuances of real-world deployment. This deterministic approach ignores the high statistical variance inherent in small, intersectional groups, often leading to false alarms or missed detections of bias. Furthermore, standard auditing struggles with the opacity of black-box models, frequently conflating unjustifiable bias with the influence of legitimate features.

Abstract--As black-box models become foundational to mod-solution that can be applied across a wide range of scientific ern research, ensuring their stability is paramount for the realiza-and industrial domains. The inherent diversity of inputs--ranging from structured Gaussian distributions to Notwithstanding its widespread application, the framework complex data with unknown structures--poses a significantexhibits certain shortcomings when dealing with complex challenge: how to stabilize black-box outputs while effectivelydatasets. First, standard resampling schemes often fail to leveraging available prior information. This paper introduces aaccount for the underlying data structures; as a result, the task-oriented randomization methodology that adaptively tailorsdrawn samples cannot reflect the true data distribution, thereby its strategy to the underlying generative mechanisms of the input data, specifically addressing unstructured complexities. Second, effective sampling requires prior comprehensive suite of stability guarantees is proposed. Beyondknowledge of the distribution, which is often unattainable establishing rigorous theoretical foundations for stability, thein practical environments.

AI-assisted interviews promise to reduce respondent burden in surveys by allowing respondents to describe experiences naturally while an AI system noisily maps those accounts into structured survey variables. That mapping is a measurement process that is fallible, versioned, adaptive, and potentially behaves differently across subgroups. This paper proposes Adaptive Matrix Validation (AMV), a design in which each respondent completes an AI-assisted interview, which is then mapped into tabular data by the AI. Respondents are also asked a small, randomized set of structured questions, which are used for statistical adjustment. The estimator first calibrates the mapped values using validation answers from other respondents, then corrects the remaining error with the validation answers observed for the target respondent. The paper develops estimators for item means, subgroup estimates, and regression coefficients when outcomes, predictors, or both are mapped from interviews. It also gives planning formulas the number of validation questions required and the sample size. A design-calibration simulation, an American Time Use Survey emulation, and a CHAMPS verbal-autopsy narrative study show when sparse validation can improve precision and when it cannot