Tennis


Predicting Sports Outcomes Using Python and Machine Learning

#artificialintelligence

The purpose of this course is to teach about how to use Python and machine learning in order to predict sports outcomes. It takes you through through all the steps, from collecting data using a web crawler to making profitable bets based on your predicted results. The course is built around predicting tennis games, but the things taught can be extended to any sport, including team sports. The course includes: 1) Intro to Python and Pandas. This course is geared towards people that have some interest in data science and some experience in Python.


10. Introduction to Learning, Nearest Neighbors

#artificialintelligence

Sign in to report inappropriate content. Instructor: Patrick Winston This lecture begins with a high-level view of learning, then covers nearest neighbors using several graphical examples. We then discuss how to learn motor skills such as bouncing a tennis ball, and consider the effects of sleep deprivation.


Table tennis-playing robot that can sense you getting frustrated and lower its skill level

Daily Mail - Science & tech

Japanese robotics company Omron and its table-tennis-playing bot are back at CES to serve up loads of fresh new tech. This year, though Omron may have reincarnated its crowd-pleasing table tennis bot, called Forpheus, the company managed to up the ante with a new emotional recognition system that gauges players' frustration level and their skill. In addition to being fun, Omron wants Forpheus to showcase its work in AI, computer vision and robotics. Its system, which watches players closely as they battle the bot in ping pong, has the capability of reading a players' face and even their heart rate and then interpreting that information to make inferences on skill and state-of-mind. Forpheus (pictured above) can reach to a volley using computer vision.


Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation

arXiv.org Machine Learning

Reinforcement learning (RL) has achieved tremendous success as a general framework for learning how to make decisions. However, this success relies on the interactive hand-tuning of a reward function by RL experts. On the other hand, inverse reinforcement learning (IRL) seeks to learn a reward function from readily-obtained human demonstrations. Yet, IRL suffers from two major limitations: 1) reward ambiguity - there are an infinite number of possible reward functions that could explain an expert's demonstration and 2) heterogeneity - human experts adopt varying strategies and preferences, which makes learning from multiple demonstrators difficult due to the common assumption that demonstrators seeks to maximize the same reward. In this work, we propose a method to jointly infer a task goal and humans' strategic preferences via network distillation. This approach enables us to distill a robust task reward (addressing reward ambiguity) and to model each strategy's objective (handling heterogeneity). We demonstrate our algorithm can better recover task reward and strategy rewards and imitate the strategies in two simulated tasks and a real-world table tennis task.


AI in the announcer's booth

#artificialintelligence

I like to watch rugby, even though I know very little about it. They rightfully believe they're talking to people who watch rugby a lot, so they feel no need to address me, personally, with rugby-for-dummies spiels that might give me an appreciation for the game. But emerging technology could soon solve my problem. Some companies are working on AI that will generate custom sports commentary, which means I could potentially tune into a streaming rugby game and listen to a human-sounding, AI-driven robot commentator that already understands my level of rugby savvy. Maybe my robot commentator will patiently explain the difference between a blood bin and a tight head.


AI Knows If The Pitch Is On Target Before You Do

#artificialintelligence

Pitching a baseball is about accuracy and speed. A swift ball on target is the goal, allowing the pitcher to strike out the batter. The system uses an NVIDIA Jetson AGX Xavier, fitted with a USB camera running at 100FPS. A Nerf tennis ball launcher is used to fire a ball towards the batter. Once triggered, the AI uses the camera to capture two successive images of the ball in flight.


IBM & Digital Reinvention of the US Open 2019 The MSP Hub

#artificialintelligence

For nearly three decades, IBM has partnered with the United States Tennis Association (USTA) to bring this prestigious sporting event to life for millions of fans, players, coaches, and even the media. This year players from Roger Federer to Serena Williams, along with their coaches, will be utilising IBM AI-powered tools to sharpen their game plan and skill. Better still, IBM Watson is also reinventing how fans experience the US Open, taking it far beyond simply reporting the very latest Novak Djokovic news. Watson Virtual Assistant will help the hundreds of thousands of fans visiting the tournament to successfully navigate everything from maps to dining options, along with making the US Open Schedule seem so much less daunting. IBM Watson's AI capacity enables it to see, hear, and understand the most exciting moments recorded on video.




Table tennis playing robot breaks world record - Meet The Record Breakers Japan

#artificialintelligence

The record breaking robot that teaches humans how to play table tennis. Whether you've got the stretchiest skin, know the world's smallest dog or want to create the largest human dominoes chain we want to hear about it. Here on the Guinness World Records YouTube channel we want to showcase incredible talent.