When Pope Leo XIV presented his first encyclical on artificial intelligence at the Vatican on Monday, he invited Christopher Olah, cofounder of Anthropic, to speak. The move signaled an unprecedented alliance between the Catholic church and Silicon Valley. But to understand how this partnership came about, we need to go back to Anthropic's founding. Anthropic launched in 2021 after a group of OpenAI researchers, including Dario and Daniela Amodei, left to form a rival lab. They did so with a clear conviction: Artificial intelligence models were becoming too powerful to be developed exclusively according to the logic of competition and speed.

In this piece, we reflect on AIES 2025, and outline the conversations and presentations from a discussion session on LLMs in the context of clinical usage and human rights. This is a crosspost from the latest issue of AI Matters, published by the ACM SIAGI. This year's conference on artificial intelligence, ethics and society (AIES) took place in the north of Madrid within the 180m-high tower block that forms the vertical campus of IE University. The event kicked off with a welcome from the chairs and organising committee members, with this opening session also featuring the conference best paper awards. Topics covered during the three-day event included mitigating bias, integrating AI into the workplace, evaluating LLMs in clinical settings, power dynamics in AI ecosystems, and dataset creation.

The 10th International Workshop on Health Intelligence (W3PHIAI-26) celebrated a decade of bringing AI and health research together, building on a lineage that began with the AAAI-W3PHI workshops focused on population health (2014-2016), the AAAI-HIAI workshops focused on personalized health (2013-2016), and the subsequent joint W3PHIAI workshops held annually from 2017 through 2025. Over this decade, the series has produced hundreds of talks and high-impact publications that have collectively received thousands of citations, shaping the research agenda in both population health intelligence and personalized healthcare AI. This year's special theme, "Foundation Models and AI Agents," reflected the field's rapidly evolving frontier: the emergence of autonomous and semi-autonomous AI systems reshaping clinical workflows, patient management, health system operations, and public health surveillance. Day 1 of the workshop focused on medical imaging and the translation of AI for clinical ...

Time-varying dependence is often modeled with dynamic correlations or Gaussian graphical models, but multivariate systems can change through tail behavior, asymmetry, or conditional structure even when correlations are nearly stable. We introduce Dynamic Vine Copulas (DVC), a temporal vine-copula framework for estimating and diagnosing sequence-wide non-Gaussian dependence. DVC fixes a chosen vine factorization for comparability; the framework applies to C-, D-, and R-vines, and our experiments use fixed-root-order C-vines. Pair-copula states evolve through smooth parameter trajectories or temporally regularized family-switching paths. The main diagnostic is a held-out comparison between a full vine and its matched 1-truncated version, which separates flexible first-tree pairwise dependence from evidence contributed by higher-tree conditional terms. At the population level, under a correct fixed vine and the simplifying assumption, this contrast equals the higher-tree component of a vine total-correlation decomposition; in finite samples, it is a predictive diagnostic. In controlled benchmarks, DVC detects Student-t degrees-of-freedom changes, Clayton-to-Gumbel switches, and recurrent conditional-interaction episodes missed or conflated by Gaussian dynamic baselines. The higher-tree score remains near zero in pairwise-only regimes and rises during conditional-interaction regimes. On Allen Visual Behavior Neuropixels data, DVC identifies a reproducible time-indexed higher-tree signal that is positive across held-out splits and vanishes under a decorrelated null, indicating simultaneous cross-area dependence. DVC therefore provides a flexible temporal copula model and an interpretable test of whether temporal dependence changes are pairwise or conditional.

When you purchase through links in our articles, we may earn a small commission. Try Adobe Acrobat Studio for free today! Communication is a central part of any business or creative endeavour. Whether its sharing information between colleagues or highlighting the advantages of new products and services to customers, getting the messaging right is an essential part of success. Traditionally, this could involve hours of painstaking work, preparing documents and then replicating their data into slides for presentations.

We thank all reviewers for their valuable feedback and thoughtfull suggestions. To the best of our knowledge, there is no official implementation for the paper by Gu et al. (no link to the code However, in Section 5.1 we compare the lower bound on the objective we use with the one of Gu et These works do not report significant improvements in BLEU scores against the autoregressive baselines. Stern et al.(2019) focus on parallel decoding (with the final result matching the vanilla Transformer). NMT models for high-resource language pairs), we will add them should the paper get accepted. Note that we consider not only natural language output, but also Image-to-Latex, where output is LaTex formulas.

Stimulus-drivenbrain-computer interfaces (BCIs), such astheP300 speller,rely onusing asequence ofsensory stimuli toelicit specific neural responses ascontrol signals, while a user attends to relevant target stimuli that occur within the sequence. In current BCIs, the stimulus presentation schedule is typically generated in a pseudo-random fashion. Given the non-stationarity of brain electrical signals, a better strategy could be to adapt the stimulus presentation schedule in real-time by selecting the optimal stimuli that will maximize the signal-to-noise ratios of the elicited neural responses and provide the most information about the user's intent based on the uncertainties of the data being measured. However, the high-dimensional stimulus space limits the development of algorithms with tractable solutions for optimized stimulus selection to allow for real-time decision-making within the stringent time requirements of BCI processing.