Sports pushes the science of keeping time forward. A broadcast drone hovers as Britain's Makayla Gerken Schofield competes in the freestyle skiing women's moguls. Breakthroughs, discoveries, and DIY tips sent six days a week. Athletes competing in this year's Winter Olympic Games in Milan will do so surrounded by a complex web of AI-enabled cameras, stopwatches, sensors, and fast-flying drones capable of tracking performance down to fractions of a second. The high-tech timekeeping system, the culmination of nearly a century of constant iteration, is fundamentally reshaping how viewers at home experience the Games.

Officially named'Essential,' the torch was designed by Carlo Ratti and weighs only 2.5 pounds. Breakthroughs, discoveries, and DIY tips sent six days a week. Every Olympic Games has a torch. Every torch has a designer. For the 2026 Milano Cortina Olympic Games and Paralympic Games, that designer is MIT engineer and architect Carlo Ratti .

--This study analyzes Chinese and English media reports on the Paris Olympics using topic modeling, Large Language Model (LLM) prompt engineering, and corpus phraseology methods to explore similarities and differences in discourse construction and attitudinal meanings. Common topics include the opening ceremony, athlete performance, and sponsorship brands. Chinese media focus on specific sports, sports spirit, doping controversies, and new technologies, while English media focus on female athletes, medal wins, and eligibility controversies. Chinese reports show more frequent prepositional co-occurrences and positive semantic prosody in describing the opening ceremony and sports spirit. The Paris Olympics, held from July 26 to August 11, 2024, marked France's return to hosting the Summer Games after a 100-year gap. As a global sporting event and ceremonial medium, the Olympics have significant cultural, political, and economic impact, attracting intense media attention worldwide. Media reports not only document the events but also reflect the cultural perspectives and values of the reporting countries, shaping global perceptions of the Olympic spirit and the host nation.

In this paper, we introduce a dataset of multilingual news articles covering the 2021 Tokyo Olympics. A total of 10,940 news articles were gathered from 1,918 different publishers, covering 1,350 sub-events of the 2021 Olympics, and published between July 1, 2021, and August 14, 2021. These articles are written in nine languages from different language families and in different scripts. To create the dataset, the raw news articles were first retrieved via a service that collects and analyzes news articles. Then, the articles were grouped using an online clustering algorithm, with each group containing articles reporting on the same sub-event. Finally, the groups were manually annotated and evaluated. The development of this dataset aims to provide a resource for evaluating the performance of multilingual news clustering algorithms, for which limited datasets are available. It can also be used to analyze the dynamics and events of the 2021 Tokyo Olympics from different perspectives. The dataset is available in CSV format and can be accessed from the CLARIN.SI repository.

This paper presents a comprehensive framework for time series prediction using a hybrid model that combines ARIMA and LSTM. The model incorporates feature engineering techniques, including embedding and PCA, to transform raw data into a lower-dimensional representation while retaining key information. The embedding technique is used to convert categorical data into continuous vectors, facilitating the capture of complex relationships. PCA is applied to reduce dimensionality and extract principal components, enhancing model performance and computational efficiency. To handle both linear and nonlinear patterns in the data, the ARIMA model captures linear trends, while the LSTM model models complex nonlinear dependencies. The hybrid model is trained on historical data and achieves high accuracy, as demonstrated by low RMSE and MAE scores. Additionally, the paper employs the run test to assess the randomness of sequences, providing insights into the underlying patterns. Ablation studies are conducted to validate the roles of different components in the model, demonstrating the significance of each module. The paper also utilizes the SHAP method to quantify the impact of traditional advantages on the predicted results, offering a detailed understanding of feature importance. The KNN method is used to determine the optimal prediction interval, further enhancing the model's accuracy. The results highlight the effectiveness of combining traditional statistical methods with modern deep learning techniques for robust time series forecasting in Sports.

Large Language Models (LLMs) have been shown to be biased in prior work, as they generate text that is in line with stereotypical views of the world or that is not representative of the viewpoints and values of historically marginalized demographic groups. In this work, we propose using data from parallel men's and women's events at the Olympic Games to investigate different forms of gender bias in language models. We define three metrics to measure bias, and find that models are consistently biased against women when the gender is ambiguous in the prompt. In this case, the model frequently retrieves only the results of the men's event with or without acknowledging them as such, revealing pervasive gender bias in LLMs in the context of athletics.

We present \llinstruct: An 8B instruction-tuned model that is designed to generate content for English Language Proficiency Assessments (ELPA) and related applications. Our work involves creating a new dataset of 70K instructions and explanations in the ELPA domain and using these to fine-tune Llama-3 8B models (SFT) of different sizes (e.g., SFT-17K, SFT-50K and SFT-70K). Human evaluations are conducted over unseen instructions to compare these SFT models against SOTA models (e.g., Dolly-2, Mistral, Llama-3 base version, and GPT-3.5). The findings show although all three SFT models perform comparably, the model trained on largest instruction dataset -- SFT-70K - leads to the most valid outputs ready for assessments. However, although the SFT models perform better than larger model, e.g., GPT 3.5 on the aspect of explanations of outputs, many outputs still need human interventions to make them actual ready for real world assessments.

Large language models (LLMs) have become a dominant approach in natural language processing, yet their internal knowledge structures remain largely unexplored. In this paper, we analyze the internal knowledge structures of LLMs using historical medal tallies from the Olympic Games. We task the models with providing the medal counts for each team and identifying which teams achieved specific rankings. Our results reveal that while state-of-the-art LLMs perform remarkably well in reporting medal counts for individual teams, they struggle significantly with questions about specific rankings. This suggests that the internal knowledge structures of LLMs are fundamentally different from those of humans, who can easily infer rankings from known medal counts. To support further research, we publicly release our code, dataset, and model outputs.

For the first post-COVID Olympics, there are some major changes now in place at the Paris 2024 Games. First of all, there are no physical tickets for visitors. All tickets are digital, but spectators can separately purchase an additional paper souvenir ticket for their event. While this is significantly a COVID legacy, it's also a sign of the times, as more of the Olympic Games moves into the digital world. If we dig deeper, we see how the DNA of this transformation is a story about data and its expansion – and how the ability of the Olympics to grow economically relies on it being harnessed and exploited. As AI steadily changes the strategic positioning of all aspects of life, the sports world has rapidly begun a similar journey.

The drone scandal surrounding the Canadian women's soccer team could have bigger implications than just this year's Games in Paris. Head coach Bev Priestman was removed from her position on Thursday night after two staff members were sent home from Paris after an investigation found that analyst Joseph Lombardi had used a drone to spy on New Zealand's practice sessions. Head coach Beverly Priestman reacts during the Women's Gold Medal match between Canada and Sweden on day 14 of the Tokyo 2020 Olympic Games at International Stadium Yokohama on Aug. 6, 2021 in Yokohama, Kanagawa, Japan. "Over the past 24 hours, additional information has come to our attention regarding previous drone use against opponents, predating the Paris 2024 Olympic Games," Canada Soccer CEO Kevin Blue said in a statement. "In light of these new revelations, Canada Soccer has made the decision to suspend Women's National Soccer Team Head Coach, Bev Priestman for the remainder of the Paris 2024 Olympic Games, and until the completion of our recently announced independent external review."