Tennis


Chatbot vs. Live Chat: Which is Winning Customer Service Game and Why?

#artificialintelligence

The last time you were on a live chat, was your question answered immediately, or did you have to leave an email address and wait for them to get back to you? More than likely, you had to wait. It's very annoying when you need a quick answer but you can't get it. You may leave to find other brands, or perhaps you'll send an email, expecting a prompt reply -- but end up waiting 48 hours. Live chat is an awesome technology.


Tennis Champion Used AI to Help Win Wimbledon tournament

#artificialintelligence

Over the course of the past year, it seems that more and more attention is being paid to ensuring that AI is used in ethical ways. Google and Microsoft have both recently warned investors that misuse of AI algorithms or poorly designed AI algorithms presents ethical and legal risks. Meanwhile, the state of California has just decided to pass a bill that bans the use of face recognition technology by California's law enforcement agencies. Recently, startups such as Arthur have been attempting to design tools that will help AI engineers quantify and qualify how their machine learning models perform. As reported by Wired, Arthur is trying to give AI developers a toolkit that will make it easier for them to discover problems when designing financial applications, like unveiling bias in investment or lending decisions.


Novak Djokovic Used A.I. to Train for Wimbledon

#artificialintelligence

Just watching was a feat of endurance. The 2019 men's final at Wimbledon lasted four hours and 57 minutes, making it the longest on record at the All England Club. Roger Federer and Novak Djokovic seemed to be perfectly matched, until they weren't. In the end, Djokovic prevailed, and fans were left to debate what allowed the Serbian great to finally notch the win. They would probably be surprised to learn that some of Djokovic's advantage could have come from artificial intelligence, which he incorporated in his game for the first time during this year's Wimbledon.


Knowledge Graph -- A Powerful Data Science Technique to Mine Information from Text (with Python code)

#artificialintelligence

Lionel Messi needs no introduction. Even folks who don't follow football have heard about the brilliance of one of the greatest players to have graced the sport. We have text, tons of hyperlinks, and even an audio clip. The possibilities of putting this into a use case are endless. However, there is a slight problem. This is not an ideal source of data to feed to our machines.


How Would A Robotic Machine Learning Velociraptor Learn To Play Goalie? CleanTechnica

#artificialintelligence

The 1.5 meter, silvery gray velociraptor lunges forward, interrupting the flight of the tennis ball with its head before the ball can get to the soccer net at the end of the gym. Its tail stretches out, stopping another ball. It pivots, somewhat clumsily, and runs three steps in the other direction to intercept a third ball. Robots building Teslas aren't as sophisticated as AI velociraptors that tend goals It's been doing this for an hour, running back and forth as a trio of tennis ball machines toss yellow balls in various loopy ways toward the net. It's a game that its creators have invented to rapidly improve its coordination. But then it stops trying to intercept the balls, although it still twitches toward them.


How to build a Knowledge Graph from Text Using spaCy

#artificialintelligence

Lionel Messi needs no introduction. Even folks who don't follow football have heard about the brilliance of one of the greatest players to have graced the sport. We have text, tons of hyperlinks, and even an audio clip. The possibilities of putting this into a use case are endless. However, there is a slight problem. This is not an ideal source of data to feed to our machines.


Random forest model identifies serve strength as a key predictor of tennis match outcome

arXiv.org Machine Learning

Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefit ted from the popularity of rigorous record - keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of particular interest to bettors and betting companies alike is potential use of sports records to predict tennis match outcomes prior to match start. We compiled, cleaned, and used th e largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods. Using such methods allows for rapid fit and prediction times to readily incorporate new data and make real - time predictions. We were able to predict match outcomes with upwards of 80% accuracy, much greater than predictions using betting odds alone, an d identify serve strength as a key predictor of match outcome. By combining prediction accuracies from three models, we were able t o nearly recreate a probability distribution based on average betting odds from betting companies, which indicates that betting companies are using similar information to assign odds to matches. These results demonstrate the capability of relatively simpl e machine learning models to quite accurately predict tennis match outcomes.


Commercial cloud service providers give artificial intelligence computing a boost

#artificialintelligence

Neural networks have given researchers a powerful tool for looking into the future and making predictions. But one drawback is their insatiable need for data and computing power ("compute") to process all that information. At MIT, demand for compute is estimated to be five times greater than what the Institute can offer. To help ease the crunch, industry has stepped in. An $11.6 million supercomputer recently donated by IBM comes online this fall, and in the past year, both IBM and Google have provided cloud credits to MIT Quest for Intelligence for distribution across campus.


What a little more computing power can do

#artificialintelligence

Neural networks have given researchers a powerful tool for looking into the future and making predictions. But one drawback is their insatiable need for data and computing power ("compute") to process all that information. At MIT, demand for compute is estimated to be five times greater than what the Institute can offer. To help ease the crunch, industry has stepped in. An $11.6 million supercomputer recently donated by IBM comes online this fall, and in the past year, both IBM and Google have provided cloud credits to MIT Quest for Intelligence for distribution across campus.


HapPenIng: Happen, Predict, Infer -- Event Series Completion in a Knowledge Graph

arXiv.org Artificial Intelligence

Event series, such as the Wimbledon Championships and the US presidential elections, represent important happenings in key societal areas including sports, culture and politics. However, semantic reference sources, such as Wikidata, DBpedia and EventKG knowledge graphs, provide only an incomplete event series representation. In this paper we target the problem of event series completion in a knowledge graph. We address two tasks: 1) prediction of sub-event relations, and 2) inference of real-world events that happened as a part of event series and are missing in the knowledge graph. To address these problems, our proposed supervised HapPenIng approach leverages structural features of event series. HapPenIng does not require any external knowledge - the characteristics making it unique in the context of event inference. Our experimental evaluation demonstrates that HapPenIng outperforms the baselines by 44 and 52 percentage points in terms of precision for the sub-event prediction and the inference tasks, correspondingly.