TIS’ THE SEASON of strawberries massacre. Wimbledon’s de facto meal of strawberries and cream (downed with icy cold Pimm’s) translates to yearly consumption surges. If you think about it, merchants are big Wimby champions, too.
Out of curiosity: How many years have you been following the Wimbledon now? 5 years? 10 years? 20 years?
Did you know that IBM technologies have been “helping out” at the tournaments since 1990? That’s way before the Williams sisters, Venus and Serena, started competing in tennis professionally!
This summer, Watson (as in IBM’s artificial intelligence, not British tennis player Heather Watson) is returning with new tricks up its sleeves. Four brand new or revamped innovations, to be exact.
Make Big Data Great What Makes Great…
“What makes a great tennis player?”
Ask any tennis aficionado, and you will likely get biased answers. Pull up Excel sheets alone, and the results may lack a human touch.
Striking a better balance, Watson can perhaps offer fresher insights via its “What Makes Great” feature. Why should we even trust what it has to say? Here’s what gives Watson credibility: Apparently it has become a world tennis expert by going through tennis-specific machine learning.
In case you forgot, Watson comprises a supercomputer that is capable of devouring enormous sets of data otherwise simply too overwhelming for humans to handle.
In an attempt to identify what makes a great tennis player, Watson has analyzed 22 years of unstructured data from previous tournaments. The work was way more involved than purely processing structured data (i.e. mostly numbers, which you can normally handle in Excel).
As a quick example, if someone gave you 10 phone numbers, you would be able to organize them in significantly less time than if you were given — say — a random mix of a phone number, an address, a web URL, a full news article, a hi-res image, a video, and an audio file.
No sweat for Watson at all though. Its Wimbledon training includes examination of tennis winners across six attributes:
- performance under pressure
- serve effectiveness
- how well the player either adapted their normal playing style to an opponent or was able to force an opponent to conform to their tactics
- the ability to return serves
There is also a component of personality analysis, helping to uncover player traits and behaviors that may not be immediately obvious to the human eye. The process is based on unstructured data sets of media interviews, thousands of Telegraph articles, and more.
Looks like it’s time Watson the editorial assistant gets a raise.
2. IBM SlamTracker
SlamTracker just got a facelift and wants you to take it to the games with you. In more seriousness, it is an updated app that features “Keys to the Match,” a cognitive component built with predictive analytics technology.
The technology will predict how neck and neck a match is going to be, basing upon many years of Grand Slam Tennis data it has analyzed. There’s more. It will also mine real-time data from courtside statisticians, chair umpires, radar guns, ball position, and player location. The results might surprise you.
At a hunch, you might think a match that happens to feature Murray or Federer will be the most nail-biting one. But in reality, the IBM tech may open your eyes to players you may not have heard of before.
And its opinion is likely more trustworthy than that of your regular tourney buddy who just won’t shut up during the games (or on social media).
3. “Ask Fred”
Hopefully *that one* friend isn’t coincidentally called Fred. But even if they are, this is a different Fred. “Ask Fred” is named after the late and great Fred Perry. It’s another IBM app that will help fans navigate the Wimbledon experience. Behind the scenes, the app is powered by a Watson-enabled cognitive bot.
Caught short on the court? “Ask Fred” for help.
4. Watson the Video Editor
Okay, seriously, when is Watson getting that raise? From the “What Makes Great” feature, we discovered that Watson is the most efficient editorial assistant the world has yet seen.
As it turns out, it is also the world’s fastest video editor!
From piles after piles of real-time footage, Watson knows how to pick out key moments of a match based on player movement, crowd noise, and other match data.
The clips will be edited into highlight reels of every match, made available just minutes after a match ends. Some of the clips will be pulled from new 360° cameras, which will let everyone see more of the action.
Typically the editing process would take a human upwards of an hour, per match. Editorial teams will probably be happy with this nifty outsourced help that is Watson.
Why are all of these important?
Some people might say, “Why ‘waste’ all these resources on Wimbledon? It’s just sports.”
Put tennis aside for a second. As with most AI advancements, it’s the technological progress that’s truly enthralling. As are the implications for the future. Let’s look at a few of them:
Watson’s capabilities to analyze decades of monumental (unstructured) data is good news for non-sports fields as well. Medical doctors, for instance, might reach breakthroughs in finding new cures and solutions with the help of similar use of big data.
And then there is “Keys to the Match.” Using insights from data to predict outcomes, especially when so many varying factors are involved? Such power of predictive analytics can be applied across industries and verticals as well. It could help to drive better strategies for both micro- and macro-initiatives such as building a smart city.
What about “Ask Fred”? Well, it has something to do with natural language acquisition. Mastering the complexities of natural language is what most AI systems are still struggling with. It’s partly why most books and articles are still currently written by humans. It has also been one of the biggest challenges for other intelligent assistants like Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa, OK Google, Facebook M, and so forth.
Next, we have the highlight reels. Watson’s video editing skill demonstrates how automation is impacting our lives in yet another field. It can either be good or bad, depending on who’s looking at it. From an optimistic perspective, as long as the time saved can be transferred to other productive undertakings, automation is serving us rather than replacing us. This may not always be the case though, whereby effective solutions must be sought.
Now, we can come back to the “why tennis” question. Watson’s appearance at the Wimbledon is putting AI into a context that most people can relate to. In this case, long-standing tennis tournaments featuring universally acclaimed stars.
In some ways, it’s similar to how Google’s DeepMind recently revealed that its AI is learning to walk all by itself. Most of us walk on a daily basis, and can therefore understand the concept of having to figure out how to walk.
Last but not least, there is one hidden benefit too — and that’s the same reason why I chose to cover AI news in the first place. By getting comfortable with AI-related topics, we can hopefully inspire young people from all backgrounds to pursue education in STEM fields that may lead to careers surrounding AI.
After all, AI is for everyone. It shouldn’t be abused as merely a buzzword for marketing purposes. Furthermore, its narratives shouldn’t be dictated by a select, privileged few. ∎