In 1989, Sir Tim revolutionized the online world. Today, in the era of misinformation, addictive algorithms, and extractive monopolies, he thinks he can do it again. Berners-Lee is building tools that aim to resist the Big Tech platforms, give users control over their own data, and prevent A.I. from hollowing out the open web. Tim Berners-Lee may have the smallest fame-to-impact ratio of anyone living. Strangers hardly ever recognize his face; on "Jeopardy!," Berners-Lee invented the World Wide Web, in 1989, but people informed of this often respond with a joke: Wasn't that Al Gore? Still, his creation keeps growing, absorbing our reality in the process. If you're reading this online, Berners-Lee wrote the hypertext markup language (HTML) that your browser is interpreting. He's the necessary condition behind everything from Amazon to Wikipedia, and if A.I. brings about what Sam Altman recently called "the gentle singularity"--or else buries us in slop--that, too, will be an outgrowth of his global collective consciousness. Somehow, the man responsible for all of this is a mild-mannered British Unitarian who loves model trains and folk music, and recently celebrated his seventieth birthday with a picnic on a Welsh mountain. An emeritus professor at Oxford and M.I.T., he divides his time between the U.K., Canada, and Concord, Massachusetts, where he and his wife, Rosemary Leith, live in a stout greige house older than the Republic. On the summer morning when I visited, geese honked and cicadas whined. Leith, an investor and a nonprofit director who co-founded a dot-com-era women's portal called Flametree, greeted me at the door. "We're basically guardians of the house," she said, showing me its antique features. I almost missed Berners-Lee in the converted-barn kitchen, standing, expectantly, in a blue plaid shirt. He shook my hand, then glanced at Leith. Minutes later, he and I were gliding across a pond behind the house. Berners-Lee is bronzed and wiry, with sharp cheekbones and faraway blue eyes, the right one underscored by an X-shaped wrinkle. A twitchier figure emerged when he spoke.

Legal experts analyze the challenging defense strategy for Tyler Robinson, who allegedly shot Charlie Kirk at Utah Valley University, as prosecutors prepare evidence for trial.

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Survival analysis is a cornerstone of clinical research by modeling time-to-event outcomes such as metastasis, disease relapse, or patient death. Unlike standard tabular data, survival data often come with incomplete event information due to dropout, or loss to follow-up. This poses unique challenges for synthetic data generation, where it is crucial for clinical research to faithfully reproduce both the event-time distribution and the censoring mechanism. In this paper, we propose SurvDiff, an end-to-end diffusion model specifically designed for generating synthetic data in survival analysis. SurvDiff is tailored to capture the data-generating mechanism by jointly generating mixed-type covariates, event times, and right-censoring, guided by a survival-tailored loss function. The loss encodes the time-to-event structure and directly optimizes for downstream survival tasks, which ensures that SurvDiff (i) reproduces realistic event-time distributions and (ii) preserves the censoring mechanism. Across multiple datasets, we show that \survdiff consistently outperforms state-of-the-art generative baselines in both distributional fidelity and downstream evaluation metrics across multiple medical datasets. To the best of our knowledge, SurvDiff is the first diffusion model explicitly designed for generating synthetic survival data.

Deep neural networks achieve state-of-the-art performance in estimating heterogeneous treatment effects, but their opacity limits trust and adoption in sensitive domains such as medicine, economics, and public policy. Building on well-established and high-performing causal neural architectures, we propose causalKANs, a framework that transforms neural estimators of conditional average treatment effects (CATEs) into Kolmogorov--Arnold Networks (KANs). By incorporating pruning and symbolic simplification, causalKANs yields interpretable closed-form formulas while preserving predictive accuracy. Experiments on benchmark datasets demonstrate that causalKANs perform on par with neural baselines in CATE error metrics, and that even simple KAN variants achieve competitive performance, offering a favorable accuracy--interpretability trade-off. By combining reliability with analytic accessibility, causalKANs provide auditable estimators supported by closed-form expressions and interpretable plots, enabling trustworthy individualized decision-making in high-stakes settings. We release the code for reproducibility at https://github.com/aalmodovares/causalkans .

ArabJobs is a publicly available corpus of Arabic job advertisements collected from Egypt, Jordan, Saudi Arabia, and the United Arab Emirates. Comprising over 8,500 postings and more than 550,000 words, the dataset captures linguistic, regional, and socio-economic variation in the Arab labour market. We present analyses of gender representation and occupational structure, and highlight dialectal variation across ads, which offers opportunities for future research. We also demonstrate applications such as salary estimation and job category normalisation using large language models, alongside benchmark tasks for gender bias detection and profession classification. The findings show the utility of ArabJobs for fairness-aware Arabic NLP and labour market research. The dataset is publicly available on GitHub: https://github.com/drelhaj/ArabJobs.

Analogical reasoning is an essential aspect of human cognition. In this paper, we summarize key theory about the processes underlying analogical reasoning from the cognitive science literature and relate it to current research in natural language processing. While these processes can be easily linked to concepts in NLP, they are generally not viewed through a cognitive lens. Furthermore, we show how these notions are relevant for several major challenges in NLP research, not directly related to analogy solving. This may guide researchers to better optimize relational understanding in text, as opposed to relying heavily on entity-level similarity.