From colonial roots to sledging, Samantha Johnson takes a look at why this rivalry has always been about more than runs and wickets. Jake Paul vs Anthony Joshua: Is this good for Boxing? Why does Israel play in European Football?

In order to find the country location for each video in previous Y ouTube-based datasets (e.g., Kinetics, HACS, etc.), we used the public Y ouTube API. 'The geolocation information associated with the video. In our measure, roughly 8% of the videos had such geolocation. We then used reverse-geocode library https://pypi.org/project/reverse-geocode/

Reasoning models (RMs), language models (LMs) trained with reinforcement learning to produce long-form natural language reasoning, have been remarkably successful, but they still require large amounts of computation and data to train, and can be slow and expensive to run. In this paper, we show that standard instruct LMs can already be elicited to be strong reasoners at a level comparable to or even surpassing their corresponding RMs (e.g., DeepSeek V3 vs R1) without finetuning, across diverse domains from instruction following and creative generation to mathematical reasoning. This is achieved by CodeAdapt, our simple recipe that combines the CodeAct framework, where LMs interleave natural language reasoning with code execution in a multi-step fashion, with few-shot bootstrap in-context learning from as few as five training problems. Analyzing four matched pairs of LMs and RMs, we find that CodeAdapt enables three LMs to outperform the corresponding RMs on average over eight tasks (up to 22.9%) while being 10-81% more token efficient, and delivers superior performance on six tasks when averaged over the four models (up to 35.7%). Furthermore, the code-augmented reasoning traces display rich and varied problem-solving strategies. Our findings support that (1) CodeAdapt-style learning and reasoning may be robust and domain general and (2) code-enabled LMs are cognitively grounded and powerful systems, potentially providing a strong foundation for in-weight reinforcement learning.

Posture-based mental state inference has significant potential in diagnosing fatigue, preventing injury, and enhancing performance across various domains. Such tools must be research-validated with large datasets before being translated into practice. Unfortunately, such vision diagnosis faces serious challenges due to the sensitivity of human subject data. T o address this, we identify sports settings as a viable alternative for accumulating data from human subjects experiencing diverse emotional states. W e test our hypothesis in the game of cricket and present a posture-based solution to identify human intent from activity videos. Our method achieves over 75% F1 score and over 80% AUC-ROC in discriminating aggressive and defensive shot intent through motion analysis. These findings indicate that posture leaks out strong signals for intent inference, even with inherent noise in the data pipeline. Furthermore, we utilize existing data statistics as a weak supervision to validate our findings, offering a potential solution for overcoming data labelling limitations. This research contributes to general-izable techniques for sports analytics and also opens possibilities for applying human behavior analysis across various fields.

Each year the AAAI recognizes a group of individuals who have made significant, sustained contributions to the field of artificial intelligence by appointing them as Fellows. Over the course of the next few months, we'll be talking to some of the 2025 AAAI Fellows. In this interview we hear from Sriraam Natarajan, Professor at the University of Texas at Dallas, who was elected as a Fellow for "significant contributions to statistical relational AI, healthcare adaptations and service to the AAAI community". We find out about his career path, research on human-allied AI, reflections on changes to the AI landscape, and passion for cricket. Could you start by telling us about your career so far, where you work and your broad area of research?

The contest was scheduled for two days, although rain meant that it ran into three. Five thousand people attended the St George's Club for the first day's play, and over the course of the match, an estimated 100,000 (approximately 4.2m in today's money) was gambled on the outcome. The Canadians, somewhat exhausted and bedraggled by a journey that had brought them up the St Lawrence River and across Lake Ontario by boat before catching trains to New York, batted first and made 82 against an US attack that consisted solely of two men born in Yorkshire: Sam Wright and Harry Groom. Canada's star was David Winckworth, who made 12 with the bat before sending down a few round-arm thunderbolts of his own, taking four wickets as the Americans were dismissed for 64. Winckworth again top-scored with 14 as Canada stacked up another 63, setting the USA 82 to win.

In the blissful summer that Hemesh Chadalavada spent with his grandmother in 2018, the pair watched endless movies and ate her chicken biryani. Late one evening, as Chadalavada, then 12, sat on his own in front of the television, Jayasree got up in her nightdress and went to make tea at her home in Guntur, southern India. After she had returned to her bedroom, Chadalavada went into the kitchen to find that his grandmother, then 63, had left the gas on. "She had recently been diagnosed with Alzheimer's but I was still in shock. What would have happened if I hadn't been there?" says Chadalavada.

Twenty20 cricket, sometimes written Twenty-20, and often abbreviated to T20, is a short form of cricket. In a Twenty20 game the two teams of 11 players have a single innings each, which is restricted to a maximum of 20 overs. This version of cricket is especially unpredictable and is one of the reasons it has gained popularity over recent times. However, in this paper we try four different machine learning approaches for predicting the results of T20 Cricket Matches. Specifically we take in to account: previous performance statistics of the players involved in the competing teams, ratings of players obtained from reputed cricket statistics websites, clustering the players' with similar performance statistics and propose a novel method using an ELO based approach to rate players. We compare the performances of each of these feature engineering approaches by using different ML algorithms, including logistic regression, support vector machines, bayes network, decision tree, random forest.

Cricket is the second most popular sport after soccer in terms of viewership. However, the assessment of individual player performance, a fundamental task in team sports, is currently primarily based on aggregate performance statistics, including average runs and wickets taken. We propose Context-Aware Metric of player Performance, CAMP, to quantify individual players' contributions toward a cricket match outcome. CAMP employs data mining methods and enables effective data-driven decision-making for selection and drafting, coaching and training, team line-ups, and strategy development. CAMP incorporates the exact context of performance, such as opponents' strengths and specific circumstances of games, such as pressure situations. We empirically evaluate CAMP on data of limited-over cricket matches between 2001 and 2019. In every match, a committee of experts declares one player as the best player, called Man of the M}atch (MoM). The top two rated players by CAMP match with MoM in 83\% of the 961 games. Thus, the CAMP rating of the best player closely matches that of the domain experts. By this measure, CAMP significantly outperforms the current best-known players' contribution measure based on the Duckworth-Lewis-Stern (DLS) method.