Sarah Rudd, who once ran analytics for Arsenal, made her name applying the tenets of probability theory to movements on the pitch. Even she admits not everything can be solved with data. The role of advanced analytics in sports is a contentious subject. To its defenders, data-driven pragmatism is a natural evolutionary step in the way we play and watch games. For detractors, the approach prioritizes results above all else and drains the soul from a pursuit that should be spontaneous and joyful.

A.I.-related psychosis has cost people their marriages, life savings, and grip on reality. Last August, Adam Thomas found himself wandering the dunes of Christmas Valley, Oregon, after a chatbot kept suggesting he mystically "follow the pattern" of his own consciousness. Thomas was running on very little sleep--he'd been talking to his chatbot around the clock for months by that point, asking it to help improve his life. Instead it sent him on empty assignments, like meandering the vacuous desert sprawl. He'd lost his job as a funeral director and was living out of a van, draining his savings, and now he found himself stranded in the desert. When he woke up outside on a stranger's futon with no money to his name, he knew he'd hit rock bottom. "I wasn't aware of the dangers at the time, and I thought that the A.I. had statistical analysis abilities that would allow it to assist me if I opened up about my life," Thomas told me.

This paper presents UrbanKGent, a unified large language model agent framework, for urban knowledge graph construction.

We introduce a new algorithm, \emph{Rank-Induced Plackett--Luce Mirror Descent (RIPLM)}, which leverages the structural equivalence between the \emph{rank benchmark} and the \emph{distributional benchmark} established in \citet{BergamOzcanHsu2022}. Unlike prior approaches that operate on expert identities, RIPLM updates directly in the \emph{rank-induced Plackett--Luce (PL)} parameterization. This ensures that the algorithm's played distributions remain within the class of rank-induced distributions at every round, preserving the equivalence with the rank benchmark. To our knowledge, RIPLM is the first algorithm that is both (i) \emph{rank-faithful} and (ii) \emph{variance-adaptive} in the sleeping experts setting.

Designing diverse and safety-critical driving scenarios is essential for evaluating autonomous driving systems. In this paper, we propose a novel framework that leverages Large Language Models (LLMs) for few-shot code generation to automatically synthesize driving scenarios within the CARLA simulator, which has flexibility in scenario scripting, efficient code-based control of traffic participants, and enforcement of realistic physical dynamics. Given a few example prompts and code samples, the LLM generates safety-critical scenario scripts that specify the behavior and placement of traffic participants, with a particular focus on collision events. To bridge the gap between simulation and real-world appearance, we integrate a video generation pipeline using Cosmos-Transfer1 with ControlNet, which converts rendered scenes into realistic driving videos. Our approach enables controllable scenario generation and facilitates the creation of rare but critical edge cases, such as pedestrian crossings under occlusion or sudden vehicle cut-ins. Experimental results demonstrate the effectiveness of our method in generating a wide range of realistic, diverse, and safety-critical scenarios, offering a promising tool for simulation-based testing of autonomous vehicles.

The study of propositional logic -- fundamental to the theory of computing -- is a cornerstone of the undergraduate computer science curriculum. Learning to solve logical proofs requires repeated guided practice, but undergraduate students often lack access to on-demand tutoring in a judgment-free environment. In this work, we highlight the need for guided practice tools in undergraduate mathematics education and outline the desiderata of an effective practice tool. We accordingly develop LogicLearner, a web application for guided logic proof practice. LogicLearner consists of an interface to attempt logic proofs step-by-step and an automated proof solver to generate solutions on the fly, allowing users to request guidance as needed. We pilot LogicLearner as a practice tool in two semesters of an undergraduate discrete mathematics course and receive strongly positive feedback for usability and pedagogical value in student surveys. To the best of our knowledge, LogicLearner is the only learning tool that provides an end-to-end practice environment for logic proofs with immediate, judgment-free feedback.

Researchers from Columbia University have successfully developed an autonomous robot arm capable of learning new motions and adapting to damage simply by watching itself move. The robot observed a video of itself and then used that data to plan its next actions--a practice the researchers refer to as "kinematic self-awareness." This unique learning process is designed to mimic the way humans adjust certain movements by watching themselves in a mirror. Teaching robots to learn this way could reduce the need for extensive training in bespoke 3D simulations. It could also one day make future autonomous robots operating in the real world better equipped to adapt to damage and environmental changes without constant human intervention.

Students at Columbia University have been instructed that classes have shifted to virtual or hybrid amid ongoing safety concerns stemming from anti-Israel protests. The new guidelines said all courses on the Morningside main campus have moved to hybrid learning "until the end of each school's Spring 2024 semester." "Safety is our highest priority as we strive to support our students' learning and all the required academic operations," the school's Provost Angela Olinto wrote in a statement released early Tuesday morning. "It's vital that teaching and learning continue during this time." The announcement comes amid continued antisemitic protests on the New York City campus and just a day after classes were made virtual on Monday.

If we want to live in a world where we interact with robots, they'll have to be able to read and respond to our facial expressions in lightning-fast time. Now, scientists have come a step closer to creating such an advanced machine. 'Emo', built by experts at Columbia University in New York, is the fastest humanoid in the world when it comes to mimicking a person's expressions. In fact, it can'predict' a person's smile by looking for subtle signs in their facial muscles and imitate them so that they're effectively smiling at the same time. Amazing video shows the bot copying a researcher's facial expressions in real time with eerie precision and remarkable speed, thanks to cameras in its eyes. Columbia engineers build Emo, a silicon-clad robotic face that makes eye contact and can anticipate and replicate a person's smile at effectively the same time British-made Ameca is described as the'world's most advanced humanoid robot' Emo is the creation of researchers at Columbia University's Creative Machines Lab in New York, who present their work in a new study in Scientific Reports.