人工智能如何助力美国网球公开赛 分享到
How Artificial Intelligence is Powering the US Open 2018-09-0971117

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The crowds arrive daily at Flushing Meadows Park in Queens.It’s the US Open and all eyes are on the top tennis players in the world.With 254 matches just in the men’s and women’s singles tournaments, it’s a lot to keep up with,but behind the scenes, IBM technology is doing just that.
每天人们都会抵达皇后区的法拉盛梅多斯公园。这是美国公开赛,所有人的眼睛都盯在世界顶级网球运动员身上。仅仅男子和女子单打锦标赛就有254场,想要跟进这些比赛就要费点力气,但在幕后,IBM的技术就在解决这个问题。
So this gets updated at the end of that points, we see how Watson evaluate at that point.With so much data to sort through, IBM is employing its artificial intelligence software Watson to help.In addition to delivering scores, Watson also scans game footage.It listens to the roar of the crowd.
IBM在结束时候得到更新的信息,让我们看看Watson是怎么评估的。有这么多的数据需要整理,IBM正在借用其人工智能软件Watson的帮助。除了传达分数,Watson还能扫描比赛片段。IBM能够分析人群的呐喊。
We’ve trained it to look for fist pumps and gestures of the players as well as to look for facial expressions.All of that contextual data is used to measure the momentum of any given match in update fans after each shot.A few years ago, we didn’t really perceive video as data, but it really is.There’s 17 courts, so it’s a lot of action, it’s a lot of video for the editors to digest, so now that you can quickly identify the most exciting moments and turn it around in near real time for fans.These highlights aren’t just for fans.Coaches also use them to prepare players.
我们训练它捕捉球员的拳头和手势、辨认面部表情。每次比赛后,所有数据都会用于衡量每场比赛的势头。。几年前,我们并没有将视频视为数据,但视频确实是数据。一共有17个球场,所以有很多动作、很多的视频等着编辑消化,所以现在您可以快速地欣赏到最激动人心的时刻,并几乎实时地为粉丝转换它。这些精彩的部分不只是为粉丝准备的。教练也用这些训练球员。
In the past, it was a really labor-intensive process where the matches had to be manually tagged, and by tagging,I mean somebody goes through the video footage and makes a note of every forehand, every backhand, every unforced error.It takes hours and hours.Watson’s artificial intelligence processes and indexes video in minutes, freeing up valuable time for personalized coaching.If the coach has a certain pattern in mind that they think will be effective in the next match, we can generate a playlist that shows the player executing those patterns and really reinforce and ingrain that in their mind.
在过去,这是非常费力的过程,这些比赛都需要手动标记匹配,通过标记,我的意思是,会有人去回顾这个视频,记录每一个正击打、反击和非受迫性失误。这需要花上几个小时。Watson的人工智能在几分钟内处理和索引视频,为个性化辅导腾出宝贵时间。如果教练有一定的训练模式,并且他们认为,这样的分析在下一场比赛中会显现效果,那么我们可以就生成一个播放列表,展示选手比赛的模式,并在脑海中加强和巩固。
Players facial expressions and body language can also be correlated to their performance.Even the way their grunting, there’s certain trends and you know sometimes, the counterintuitive, some players play better when they get a little bit angry, when they get a little upset.With the help of artificial intelligence, perhaps getting a little upset will lead to more upsets on the court.
选手的面部表情和肢体语言和他们的表现也有关系。即使他们嘟哝的方式,也是有某种趋势的,你也知道,有时候,有点违反常理,一些球员在他们有点生气、或者不高兴的时候会表现得更好。有了人工智能的帮助,或许心情有一点不好,就会导致球场上的严重失利。
Tina Turin, VOA News, New York
美国之音新闻,Tina Turin 纽约报道。

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