When humans need to learn a new skill, we can acquire knowledge through written books, including textbooks, tutorials, etc. However, current research for decision-making, like reinforcement learning (RL), has primarily required numerous real interactions with the target environment to learn a skill, while failing to utilize the existing knowledge already summarized in the text.

Object-centric event logs expand the conventional single-case notion event log by considering multiple objects, allowing for the analysis of more complex and realistic process behavior. However, the number of real-world object-centric event logs remains limited, and further studies are needed to test their usefulness. The increasing availability of data from team sports can facilitate object-centric process mining, leveraging both real-world data and suitable use cases. In this paper, we present a framework for transforming football (soccer) data into an object-centric event log, further enhanced with a spatial dimension. We demonstrate the effectiveness of our framework by generating object-centric event logs based on real-world football data and discuss the results for varying process representations. With our paper, we provide the first example for object-centric event logs in football analytics. Future work should consider variant analysis and filtering techniques to better handle variability.

One thing most game developers can agree on in the modern industry is that it's hard to drum up any awareness for your latest project without a mammoth marketing budget. Last year, almost 20,000 new titles were released on the PC gaming platform Steam alone, the majority disappearing into the content blackhole that is the internet. So when a smaller studio is offered the chance to get on the stage at the Summer Games Fest, an event streamed live to a global audience of around 50 million people, it's a big deal. Not something that you want to spectacularly misjudge. Enter Ian Proulx, cofounder of 1047 Games.

Large language models (LLMs) exhibit excellent performance in natural language processing (NLP), but remain highly sensitive to the quality of input queries, especially when these queries contain misleading or inaccurate information. Existing methods focus on correcting the output, but they often overlook the potential of improving the ability of LLMs to detect and correct misleading content in the input itself. In this paper, we propose a novel three-stage fine-tuning method that enhances the ability of LLMs to detect and correct misleading information in the input, further improving response accuracy and reducing hallucinations. Specifically, the three stages include (1) training LLMs to identify misleading information, (2) training LLMs to correct the misleading information using built-in or external knowledge, and (3) training LLMs to generate accurate answers based on the corrected queries. To evaluate our method, we conducted experiments on three datasets for the hallucination detection task and the question answering~(QA) task, as well as two datasets containing misleading information that we constructed. The experimental results demonstrate that our method significantly improves the accuracy and factuality of LLM responses, while also enhancing the ability to detect hallucinations and reducing the generation of hallucinations in the output, particularly when the query contains misleading information.

When humans need to learn a new skill, we can acquire knowledge through written books, including textbooks, tutorials, etc. However, current research for decision-making, like reinforcement learning (RL), has primarily required numerous real interactions with the target environment to learn a skill, while failing to utilize the existing knowledge already summarized in the text. In this paper, we discuss a new policy learning problem called Policy Learning from tutorial Books (PLfB) upon the shoulders of LLMs' systems, which aims to leverage rich resources such as tutorial books to derive a policy network. Inspired by how humans learn from books, we solve the problem via a three-stage framework: Understanding, Rehearsing, and Introspecting (URI). In particular, it first rehearses decision-making trajectories based on the derived knowledge after understanding the books, then introspects in the imaginary dataset to distill a policy network.

Evaluating the performance of human is a common need across many applications, such as in engineering and sports. When evaluating human performance in completing complex and interactive tasks, the most common way is to use a metric having been proved efficient for that context, or to use subjective measurement techniques. However, this can be an error prone and unreliable process since static metrics cannot capture all the complex contexts associated with such tasks and biases exist in subjective measurement. The objective of our research is to create data-driven AI agents as computational benchmarks to evaluate human performance in solving difficult tasks involving multiple humans and contextual factors. We demonstrate this within the context of football performance analysis. We train a generative model based on Conditional Variational Recurrent Neural Network (VRNN) Model on a large player and ball tracking dataset. The trained model is used to imitate the interactions between two teams and predict the performance from each team. Then the trained Conditional VRNN Model is used as a benchmark to evaluate team performance. The experimental results on Premier League football dataset demonstrates the usefulness of our method to existing state-of-the-art static metric used in football analytics.

You want to build a Machine learning model without much data? Machine learning is known to be data-hungry while gathering and annotating data requires time and is expensive.

Computer vision is a branch of deep learning that focuses on the utilization of deep neural networks to model problems from images. In this article, we'll be looking at how we can apply computer vision as a tool for football analytics. Football is a sport that involves 2 teams; with each team having 11 players and a goalkeeper. Here are some analytics that could be explored from football games using AI. The notebook for this work can be found here.

That's one of the most popular questions I get asked when I am presenting at AI self-driving car events and Autonomous Vehicles (AV) conferences. At the Cybernetic AI Self-Driving Car Institute, we are developing AI software for self-driving cars, and the aspects of driver controls are also of crucial attention to our efforts, along with being notable for the efforts of the auto makers and other tech firms that are developing self-driving cars or so-called driverless or robot cars. If you are willing to strap-in and put on your seat belt, I'll do a whirlwind tour through the nuances of the ongoing debate about driver car controls in AI self-driving cars. It's quite a story and it has both ups and downs, which might leave you in tears or you might be uplifted. In essence, the matter deals with whether or not there should be a steering wheel, a brake pedal, and an accelerator pedal -- which I'll henceforth herein refer to collectively as "driver controls," provided in AI self-driving ...

President Trump and Defense Secretary Mark Esper visit the Army-Navy locker rooms to deliver words of encouragement before the 120th Army-Navy football game in Philadelphia. A probe into hand gestures flashed by West Point cadets and Naval Academy midshipmen at last weekend's televised Army-Navy college football in Philidelphia game were not racist, separate military investigations conducted by the military academies found. Clips of the "OK" hand gestures by the service-academy students during a Dec. 14, ESPN College GameDay broadcast game went viral and raised concerns over whether the signs were associated with white nationalism. The gesture, which features the thumb and forefinger that touch in a circle with the other fingers outstretched, has been appropriated as a signal for white supremacy in recent years. The Naval Academy found that two of its midshipmen were participating in a "sophomoric game" and had no racist intent behind the hand signs.