Baseball players want robots to be their umps
The sports world has been dealing with the human error of referees and umpires for decades—it’s pretty much tradition at this point. But with technology that can assess the game more accurately, some athletes are ready to push the people calling balls and strikes off the field in favor of technology.
The news: On Tuesday, Chicago Cubs second baseman Ben Zobrist, one of the most vocal supporters of turning over baseball rulings to software, used an argument with the umpire as a chance to advocate for a change in the league.
“That’s why we want an electronic strike zone.”
—Zobrist, shortly before getting his first career ejection
The comment reinvigorated a long-standing debate over automation in sports.
You’re out! As you watch baseball on television, a graphic is often overlaid on the action that shows in real time whether a pitch is a ball or a strike. But human umps are still making the calls on the field based on nothing but their own eyes. Increasingly, viewers and players would rather have the technology take over.
The opponents: As Jason Gay wrote in the Wall Street Journal, “Humanity—and all the imperfections that go with it—is an integral part of sports, even when it means officials making costly mistakes. Instant replay has its upsides, but has also turned into a soul-crushing time suck.”
A collaborative solution: Professional tennis could be an example for baseball to follow. Rather than firing all the umpires, it has decided to embrace human-software collaboration, giving the final word to the “Hawk-Eye” program on disputed in-or-out rulings. The program is quick and accurate, and it even evokes an immediate response from the crowd. If baseball can find a system like this, it may be able to find a way for the traditionalists and tech fanatics to live in harmony on the diamond.
This story first appeared in our future of work newsletter, Clocking In. Sign up here.
Deep Dive
Artificial intelligence
Large language models can do jaw-dropping things. But nobody knows exactly why.
And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.
Google DeepMind’s new generative model makes Super Mario–like games from scratch
Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.
What’s next for generative video
OpenAI's Sora has raised the bar for AI moviemaking. Here are four things to bear in mind as we wrap our heads around what's coming.
Stay connected
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.