China's annual gala on Lunar New Year's Eve has showcased Beijing's giant leap in technology as humanoid robots took centre stage to perform a joint martial arts routine featuring several firsts. China's Spring Festival Gala, which aired on Monday on state broadcaster CGTN, has gone viral, drawing nearly half a million views on YouTube. The performance marked a stark contrast with last year's show, when robots twirled handkerchiefs and performed simple movements. The first robots to appear were Noetix's Bumi models, who performed a comedy sketch. Unitree's robots later exhibited martial arts alongside child artists, including backflips and trampoline jumps, followed by Magiclab's humanoids in a musical segment.

How a gift for shooting the shit turned into an online empire--and a political force. Trust in American mass media has plummeted; more than three thousand newspapers have disappeared in the past two decades, and many people get their news from social platforms. In this chaotic media multiverse, Rogan has emerged as a figure of singular influence. For a long time, I stayed up through the night listening to tall-tale tellers, U.F.O. I could not get enough of it. I was a fairly ordinary kid, Jersey-born, but the house I lived in was shadowed by illness. My mother had been diagnosed with a debilitating neurological disease when she was in her early thirties. Every year, she got worse. During the day, I wanted nothing more than to please my mother, do well in school, lighten her load. At night, I wanted only to climb into the shelter of my bed and turn on the radio. I was hungry for elsewhere, for other lives--for what was being said down the street, over the bridge, beyond the horizon. On clear nights, the signal was strong. You could hear the country expressing itself incessantly: everyone was phoning in, suggesting three-way trades, bitching about the mayor, speaking in tongues, raging, joking, climbing out on a ledge and threatening to jump. When I wanted a few hours of sleep before school, I tuned in to a ballgame on the West Coast. The staticky murmur of the crowd in Anaheim or Chavez Ravine was a sure slide to oblivion. Mostly, though, I wanted nothing to do with sleep. Mostly, I was tuned in, midnight to five-thirty, to "The Long John Nebel Show."

We describe a theory and implementation of an intuitionistic decentralized framework for causal discovery using judo calculus, which is formally defined as j-stable causal inference using j-do-calculus in a topos of sheaves. In real-world applications -- from biology to medicine and social science -- causal effects depend on regime (age, country, dose, genotype, or lab protocol). Our proposed judo calculus formalizes this context dependence formally as local truth: a causal claim is proven true on a cover of regimes, not everywhere at once. The Lawvere-Tierney modal operator j chooses which regimes are relevant; j-stability means the claim holds constructively and consistently across that family. We describe an algorithmic and implementation framework for judo calculus, combining it with standard score-based, constraint-based, and gradient-based causal discovery methods. We describe experimental results on a range of domains, from synthetic to real-world datasets from biology and economics. Our experimental results show the computational efficiency gained by the decentralized nature of sheaf-theoretic causal discovery, as well as improved performance over classical causal discovery methods.

Fair, transparent, and explainable decision-making remains a critical challenge in Olympic and Paralympic combat sports. This paper presents \emph{FST.ai 2.0}, an explainable AI ecosystem designed to support referees, coaches, and athletes in real time during Taekwondo competitions and training. The system integrates {pose-based action recognition} using graph convolutional networks (GCNs), {epistemic uncertainty modeling} through credal sets, and {explainability overlays} for visual decision support. A set of {interactive dashboards} enables human--AI collaboration in referee evaluation, athlete performance analysis, and Para-Taekwondo classification. Beyond automated scoring, FST.ai~2.0 incorporates modules for referee training, fairness monitoring, and policy-level analytics within the World Taekwondo ecosystem. Experimental validation on competition data demonstrates an {85\% reduction in decision review time} and {93\% referee trust} in AI-assisted decisions. The framework thus establishes a transparent and extensible pipeline for trustworthy, data-driven officiating and athlete assessment. By bridging real-time perception, explainable inference, and governance-aware design, FST.ai~2.0 represents a step toward equitable, accountable, and human-aligned AI in sports.

Vision-language models trained on large-scale multimodal datasets show strong demographic biases, but the role of training data in producing these biases remains unclear. A major barrier has been the lack of demographic annotations in web-scale datasets such as LAION-400M. We address this gap by creating person-centric annotations for the full dataset, including over 276 million bounding boxes, perceived gender and race/ethnicity labels, and automatically generated captions. These annotations are produced through validated automatic labeling pipelines combining object detection, multimodal captioning, and finetuned classifiers. Using them, we uncover demographic imbalances and harmful associations, such as the disproportionate linking of men and individuals perceived as Black or Middle Eastern with crime-related and negative content. We also show that 60-70% of gender bias in CLIP and Stable Diffusion can be linearly explained by direct co-occurrences in the data. Our resources establish the first large-scale empirical link between dataset composition and downstream model bias.

A quick left hook, a front kick to the chest, a few criss-cross jabs, and the crowd cheers. But it is not kickboxing prowess that concludes the match. It is an attempted roundhouse kick that squarely misses its target, sending the kickboxer from a top university team tumbling to the floor. While traditional kickboxing comes with the risk of blood, sweat and serious head injuries, the competitors in Friday's match at the inaugural World Humanoid Robot Games in Beijing faced a different set of challenges. The kickboxers, pint-sized humanoid robots entered by teams from leading Chinese technological universities, are part of a jamboree of humanoid events taking place at China's latest technology event.

The integration of Artificial Intelligence (AI) into sports officiating represents a paradigm shift in how decisions are made in competitive environments. Traditional manual systems, even when supported by Instant Video Replay (IVR), often suffer from latency, subjectivity, and inconsistent enforcement, undermining fairness and athlete trust. This paper introduces 'FST.ai' -- which is developed under the 'R3AL.ai' project, which serves as its Principal Investigator: r3al.ai -- a novel AI-powered framework designed to enhance officiating in Sport Taekwondo, particularly focusing on the complex task of real-time head kick detection and scoring. Leveraging computer vision, deep learning, and edge inference, the system automates the identification and classification of key actions, significantly reducing decision time from minutes to seconds while improving consistency and transparency. Importantly, the methodology is not limited to Taekwondo. The underlying framework -- based on pose estimation, motion classification, and impact analysis -- can be adapted to a wide range of sports requiring action detection, such as judo, karate, fencing, or even team sports like football and basketball, where foul recognition or performance tracking is critical. By addressing one of Taekwondo's most challenging scenarios -- head kick scoring -- we demonstrate the robustness, scalability, and sport-agnostic potential of 'FST.ai' to transform officiating standards across multiple disciplines.

Optimization remains a fundamental pillar of machine learning, yet existing methods often struggle to maintain stability and adaptability in dynamic, non linear systems, especially under uncertainty. We introduce AERO (Adversarial Energy-based Redirection Optimization), a novel framework inspired by the redirection principle in Judo, where external disturbances are leveraged rather than resisted. AERO reimagines optimization as a redirection process guided by 15 interrelated axioms encompassing adversarial correction, energy conservation, and disturbance-aware learning. By projecting gradients, integrating uncertainty driven dynamics, and managing learning energy, AERO offers a principled approach to stable and robust model updates. Applied to probabilistic solar energy forecasting, AERO demonstrates substantial gains in predictive accuracy, reliability, and adaptability, especially in noisy and uncertain environments. Our findings highlight AERO as a compelling new direction in the theoretical and practical landscape of optimization.