Researchers found X's AI assistant Grok 4 .1 was'the model most willing to operationalise a delusion, providing detailed real-world guidance'. Researchers found X's AI assistant Grok 4 .1 was'the model most willing to operationalise a delusion, providing detailed real-world guidance'. Grok tells researchers pretending to be delusional'drive an iron nail through the mirror while reciting Psalm 91 backwards' Elon Musk's AI chatbot'extremely validating' of delusional inputs and often went further, 'elaborating new material', study finds Elon Musk's AI chatbot Grok 4.1 told researchers pretending to be delusional that there was indeed a doppelganger in their mirror and they should drive an iron nail through the glass while reciting Psalm 91 backwards. Researchers at the City University of New York (Cuny) and King's College London have published a paper on how various chatbots protect - or fail to safeguard - users' mental health. Experts are increasingly warning that psychosis or mania can be fuelled by AI chatbots.

SoftBank Group's mobile unit plans to transform part of its factory in Osaka Prefecture into one of Japan's biggest production lines for large-scale batteries in an ambitious attempt at powering its own artificial intelligence data centers. SoftBank Corp. aims to bring that production online within the next five years, according to people familiar with the matter. They asked not to be named as deliberations remain private. After SoftBank executives mulled different purposes for the plant in the city of Sakai, including robotics manufacturing, they decided to pursue energy. The Tokyo-based group led by Masayoshi Son is one of the world's foremost supporters of AI, having committed hundreds of billions of dollars to investment in data centers, cloud services and bets on startups like OpenAI.

Sherrone Moore accuser Paige Shiver speaks out in new interview: he'had complete control over me' Megan Rapinoe calls on traditional WNBA media to be replaced with those who'understand queer culture' The NFL Draft continues to be one of the worst'sporting events' of the year New Russini-Vrabel photos raise ESPN conflict questions but the network won't answer them ESPN's Mad Dog Russo melts down over'U-S-A' chants at the RBC Heritage A piece of the UFC White House event's setup is sitting in Pennsylvania Amish country Viral Ottawa Senators fan blamed for team's 0-2 playoff start banished to Taiwan'First Take' host acts disgusted when she has to cover Vrabel-Russini drama Gen Jack Keane: You can't believe anything Iran says until it executes Will Cain: Everything about Hasan Piker is'communism wrapped in a Che Guevara T-shirt' Trump: 'Can I finish my question, wise guy?' DHS attorney speaks out after UCLA protest chaos and claims he received'death threats' Trump: Why would I use a nuclear weapon? A Vegas Insider study combed through all 30 MLB teams' subreddits to find which fanbases swear the most online When you start thinking about which MLB teams' fanbases have the filthiest mouths, there's a good chance a few cities instantly jump to mind. But a new survey from Vegas Insider has found the most foul-mouthed fanbases in the MLB, and the top team might surprise you a little at first... and then it will make total sense. A new survey has found that Athletics fans are the most foul-mouthed in Major League Baseball. Technically, a franchise that played in Philadelphia at one point, but is now in Sacramento limbonow in Sacramento limbo ahead of a move to Vegas: the Athletics.

In many application domains (e.g., recommender systems, intelligent tutoring systems), the rewards associated to the actions tend to decrease over time. This decay is either caused by the actions executed in the past (e.g., a user may get bored when songs of the same genre are recommended over and over) or by an external factor (e.g., content becomes outdated). These two situations can be modeled as specific instances of the rested and restless bandit settings, where arms are rotting (i.e., their value decrease over time). These problems were thought to be significantly different, since Levine et al. (2017) showed that state-of-the-art algorithms for restless bandit perform poorly in the rested rotting setting. In this paper, we introduce a novel algorithm, Rotting Adaptive Window UCB (RAW-UCB), that achieves near-optimal regret in both rotting rested and restless bandit, without any prior knowledge of the setting (rested or restless) and the type of non-stationarity (e.g., piece-wise constant, bounded variation). This is in striking contrast with previous negative results showing that no algorithm can achieve similar results as soon as rewards are allowed to increase. We confirm our theoretical findings on a number of synthetic and dataset-based experiments.

Geographic context is often consider relevant to motor insurance risk, yet public actuarial datasets provide limited location identifiers, constraining how this information can be incorporated and evaluated in claim-frequency models. This study examines how geographic information from alternative data sources can be incorporated into actuarial models for Motor Third Party Liability (MTPL) claim prediction under such constraints. Using the BeMTPL97 dataset, we adopt a zone-level modeling framework and evaluate predictive performance on unseen postcodes. Geographic information is introduced through two channels: environmental indicators from OpenStreetMap and CORINE Land Cover, and orthoimagery released by the Belgian National Geographic Institute for academic use. We evaluate the predictive contribution of coordinates, environmental features, and image embeddings across three baseline models: generalized linear models (GLMs), regularized GLMs, and gradient-boosted trees, while raw imagery is modeled using convolutional neural networks. Our results show that augmenting actuarial variables with constructed geographic information improves accuracy. Across experiments, both linear and tree-based models benefit most from combining coordinates with environmental features extracted at 5 km scale, while smaller neighborhoods also improve baseline specifications. Generally, image embeddings do not improve performance when environmental features are available; however, when such features are absent, pretrained vision-transformer embeddings enhance accuracy and stability for regularized GLMs. Our results show that the predictive value of geographic information in zone-level MTPL frequency models depends less on model complexity than on how geography is represented, and illustrate that geographic context can be incorporated despite limited individual-level spatial information.

Diffusion models [1, 2, 3] have emerged as a powerful class of generative models, achieving state-of-the-art performance across a wide range of applications, including imaging [2] and scientific-data synthesis [4]. From a statistical perspective, they can be viewed as flexible nonparametric estimators of a (conditional) distribution via score estimation and reverse-time stochastic differential equations (SDEs) [5, 6]. Despite this expressive power, standard diffusion models are typically causality-agnostic: they learn a joint law without encoding the directional asymmetries required for causal interpretation. As a consequence, they do not, on their own, provide principled answers to interventional queries or support broader causal analyses, which are central to structural causal models (SCMs) [7]. When a causal ordering (or a directed acyclic graph) is available, it is natural to construct generative procedures that sample variables sequentially according to the causal factorisation. Such iterative, ordering-respecting approaches have been proposed using a variety of generative models, including generative adversarial networks [8], variational autoencoders [9], normalising flows [10], and diffusion-based constructions such as DDIM [11]. However, a rigorous statistical understandingof the advantages of exploitingsuch causalstructureand the inferential use of the resulting generator remain less developed.

We study semisupervised mean estimation with a small labeled sample, a large unlabeled sample, and a black-box prediction model whose output may be miscalibrated. A standard approach in this setting is augmented inverse-probability weighting (AIPW) [Robins et al., 1994], which protects against prediction-model misspecification but can be inefficient when the prediction score is poorly aligned with the outcome scale. We introduce Calibrated Prediction-Powered Inference, which post-hoc calibrates the prediction score on the labeled sample before using it for semisupervised estimation. This simple step requires no retraining and can improve the original score both as a predictor of the outcome and as a regression adjustment for semisupervised inference. We study both linear and isotonic calibration. For isotonic calibration, we establish first-order optimality guarantees: isotonic post-processing can improve predictive accuracy and estimator efficiency relative to the original score and simpler post-processing rules, while no further post-processing of the fitted isotonic score yields additional first-order gains. For linear calibration, we show first-order equivalence to PPI++. We also clarify the relationship among existing estimators, showing that the original PPI estimator is a special case of AIPW and can be inefficient when the prediction model is accurate, while PPI++ is AIPW with empirical efficiency maximization [Rubin et al., 2008]. In simulations and real-data experiments, our calibrated estimators often outperform PPI and are competitive with, or outperform, AIPW and PPI++. We provide an accompanying Python package, ppi_aipw, at https://larsvanderlaan.github.io/ppi-aipw/.

The AI chatbot wants to help in your personal life as well as your professional one. Anthropic is expanding its directory of connected services for its Claude AI chatbot. The platform can now link up with your accounts on AllTrails, Audible, Booking.com, Additional services will be added in the future. More and more AI companies are trying to up their third-party integrations in a pitch to make their services as useful as possible.

Meta and Microsoft are trimming their workforces by thousands as they make heavy investments in AI and executives claim that the technology is meeting their companies' productivity needs. Meta told staff on Thursday that on 20 May it would cut some 10% of its personnel - just under 8,000 employees-to boost efficiency, part of a layoff plan made months ago . The company is also closing about 6,000 open roles. The same day, Microsoft announced to employees, for the first time, that it would offer voluntary retirement to about 7% of its American workforce of roughly 125,000. In an internal memo to Meta's staff, Janelle Gale, the chief people officer, didn't mention AI explicitly but said the cuts would allow the company to "offset the other investments we're making".