The quarterbacks who competed in Super Bowl 2021 are facing off again – on the cover of the upcoming "Madden NFL 22" video game. Tampa Bay Buccaneers' Tom Brady and the Kansas City Chiefs' Patrick Mahomes both appear on the cover of the game, due out Aug. 20 ($69.99, for PlayStation 5, Xbox Series X/SX; $59.99, for PS4, Xbox One, PCs and Google Stadia). They are the most-recent two Super Bowl MVPs, though Brady won the big game in February. It's a rarity for Madden NFL to have two players on the cover, though in 2010, the video game series featured Super Bowl XLIII participants Larry Fitzgerald of the Arizona Cardinals and Troy Polamalu of the Pittsburgh Steelers as co-cover athletes. Brady and Mahomes have each appeared previously on the Madden NFL cover; Brady in 2018, Mahomes in 2020.
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The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).
While day-to-day questions come with a variety of answer types, the current question-answering (QA) literature has failed to adequately address the answer diversity of questions. To this end, we present GooAQ, a large-scale dataset with a variety of answer types. This dataset contains over 5 million questions and 3 million answers collected from Google. GooAQ questions are collected semi-automatically from the Google search engine using its autocomplete feature. This results in naturalistic questions of practical interest that are nonetheless short and expressed using simple language. GooAQ answers are mined from Google's responses to our collected questions, specifically from the answer boxes in the search results. This yields a rich space of answer types, containing both textual answers (short and long) as well as more structured ones such as collections. We benchmarkT5 models on GooAQ and observe that: (a) in line with recent work, LM's strong performance on GooAQ's short-answer questions heavily benefit from annotated data; however, (b) their quality in generating coherent and accurate responses for questions requiring long responses (such as 'how' and 'why' questions) is less reliant on observing annotated data and mainly supported by their pre-training. We release GooAQ to facilitate further research on improving QA with diverse response types.
In Yeshiva University's engineering-focused M.S. in Artificial Intelligence (AI), offered by the Katz School of Science and Health, students will learn the key skills most valued in today's marketplace, including machine learning and deep neural networks, along with cutting-edge technologies such as reinforcement learning, voice recognition and generation, and image recognition and generation. In the program's project-based courses, students will build systems, models and algorithms using the best available artificial intelligence design patterns and engineering principles, all done in the heart of Manhattan, a global epicenter for artificial intelligence work and research. Prof. Andrew Catlin is the program director for the AI program, with a background as a data scientist and production systems developer who has worked with such major clients as Fidelity Investments; Smart Money; Donaldson, Lufkin and Jenrette; Manufacturers Hanover Trust; and the National Football League. He is also a founder of multiple tech startups, including Hudson Technology and Metrics Reporting. He teaches graduate courses in recommender systems, natural language processing and neural networks, among others.
In 2019, Anderson et al. proposed the concept of rankability, which refers to a dataset's inherent ability to be meaningfully ranked. In this article, we give an expository review of the linear ordering problem (LOP) and then use it to analyze the rankability of data. Specifically, the degree of linearity is used to quantify what percentage of the data aligns with an optimal ranking. In a sports context, this is analogous to the number of games that a ranking can correctly predict in hindsight. In fact, under the appropriate objective function, we show that the optimal rankings computed via the LOP maximize the hindsight accuracy of a ranking. Moreover, we develop a binary program to compute the maximal Kendall tau ranking distance between two optimal rankings, which can be used to measure the diversity among optimal rankings without having to enumerate all optima. Finally, we provide several examples from the world of sports and college rankings to illustrate these concepts and demonstrate our results.
A football player wears a vest holding a GPS sensor. The data captured feed into an algorithm.Credit: Matthew Ashton/AMA/Corbis via Getty In 2005, 17-year-old aspiring footballer Alessio Rossi tore two ligaments in his right ankle during training for lower league Italian football club USD Olginatese. The injury ended his dream of playing at the highest level. Today, Rossi is a postdoctoral researcher at the University of Pisa, Italy, where he collects and analyses reams of data to help prevent players at top teams getting injuries of their own. When Rossi was playing, his coaches' instincts and experiences were all they had to predict whether he might receive an injury.
There have been various types of pretraining architectures including autoregressive models (e.g., GPT), autoencoding models (e.g., BERT), and encoder-decoder models (e.g., T5). On the other hand, NLP tasks are different in nature, with three main categories being classification, unconditional generation, and conditional generation. However, none of the pretraining frameworks performs the best for all tasks, which introduces inconvenience for model development and selection. We propose a novel pretraining framework GLM (General Language Model) to address this challenge. Compared to previous work, our architecture has three major benefits: (1) it performs well on classification, unconditional generation, and conditional generation tasks with one single pretrained model; (2) it outperforms BERT-like models on classification due to improved pretrain-finetune consistency; (3) it naturally handles variable-length blank filling which is crucial for many downstream tasks. Empirically, GLM substantially outperforms BERT on the SuperGLUE natural language understanding benchmark with the same amount of pre-training data. Moreover, GLM with 1.25x parameters of BERT-Large achieves the best performance in NLU, conditional and unconditional generation at the same time, which demonstrates its generalizability to different downstream tasks.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. New York Giants General Manager Dave Gettleman was on the receiving end of some heat from former NFL wide receiver Steve Smith on Wednesday. Smith, a current analyst on the NFL Network who had a falling out with Gettleman while he played for the Carolina Panthers when Gettleman was the general manager, appeared to be angered over New York's lack of free agency moves especially failing at this point to sign a No. 1 wide receiver. "They don't want an alpha. They can't take JuJu (Smith-Schuster) because he plays in the slot and that's where (Sterling) Shepard plays. They already have a tight end. So what they want are robots who aren't going to make a stink, who are gonna fall in place, which will end up stunting the growth of your franchise quarterback because you either want a robot or 6-foot-2, 215 [pound] or above wide receiver. You don't want a playmaker. You want people that aren't gonna make a stink in the locker room. And that means you want to be average," Smith said, via NJ.com.
Cox unveiled a new feature that empowers people with disabilities to control their TV with their eyes. The Accessible Web Remote for Contour gives those who have lost fine motor skills – whether from degenerative conditions or paralysis – the ability to browse the video guide with a glance. Specifically, a free web-based remote control is navigable using various assistive technologies owned by customers, including eye gaze hardware and software, switch controls, and sip-andpuff systems, which the user controls by gently blowing into a tube. Eye-tracking technology gives people living with conditions like paraplegia, Parkinson's disease and amyotrophic lateral sclerosis (ALS) the same access to their TVs as customers with the latest edition of Contour. "Innovative technology like this gives people with disabilities an added level of independence," said Steve Gleason, founder of Team Gleason and former New Orleans Saints football player who has been living with ALS since 2011.
With the advances in computer power and the ability to both acquire and store huge quantities of data, so goes the corresponding advance of the machine (aka algorithm) to replace the human as a primary source of decision making. The number of successful applications is increasing at a rapid pace; in games, such as Chess and Go, medical imaging and diagnosing tumours, to automated driving, and even the selection of candidates for jobs. The notion of reinforcement learning is one key principle, whereby a game or set of decisions is studied and rewards recorded so a machine can learn long term benefits from local decisions, often negotiating a sequence of complex decisions. For example, Silver et al. (2017) discuss how a machine can become an expert at the game Go simply by playing against itself, with Bai and Jin (2020) looking at more general self-play algorithms.