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The Stack Archive News Article

IBM Watson uses artificial intelligence at U.S. Open

Wed 30 Aug 2017

IBM is hoping to serve up an ace by using artificial intelligence (AI) at this year’s U.S. Open.

The Watson AI system will automatically generate match and player highlights by assessing images and video, as well as statistical tennis data and the reaction of the crowd and player. The system will take what it considers to be the best and most important highlights and auto-curate them.

IBM VP of sports and entertainment partnerships, Noah Syken, said: “The US Open is packed with so much action across so many courts that even the fastest video team is challenged to keep pace with what’s happening. To meet that challenge, Watson is now watching the matches alongside the USTA.”

Part of the intention of Watson’s involvement in the Open is to simplify the video production process and allow the US Tennis Association to create better video packages. Watson’s use at the U.S. Open came after testing at the Master’s Tournament and Wimbledon.

As well as analysing play for the purpose of creating highlights, Watson helps IBM’s SlamTracker, a scoring application, by applying its machine learning ability in order to build player style models.

IBM US open in text 1

Other uses of the technology include more refined content searches and video recommendations, as well as closed captioning technology that gets better as it learns ‘topic-specific terminology.’

In the longer term, IBM hopes Watson’s ability to perform deep analysis of objects, people and words, as well as deeper concepts such as emotional overtones, will help companies target customers with advertising and content.

The company argues that ‘Watson makes sense of the breadth and diversity of the world’s structured and unstructured data.’ As well as sport, the technology has been employed in industries such as health, travel and retail.

AI technology is taking hold in many sectors, particularly where the ability to learn and utilise extremely large sets of data can vastly improve on the success of processes typically performed by humans, such as the early detection of cancer.

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