Home Tech Etzioni on AI: What the World Cup tells us about the best...

Etzioni on AI: What the World Cup tells us about the best roles for humans and machines

17
0
Etzioni on AI: What the World Cup tells us about the best roles for humans and machines
Pregame ceremonies in Seattle on June 19, 2026, before the U.S.-Australia World Cup Group D match. (GeekWire Photo / John Cook)

In soccer, a single blown offside call can decide who advances and who goes home. But what can you do? Referees are only human.

Well, the 2026 World Cup has put computer vision and AI on the officiating crew: video review, a sensor inside the ball, semi-automated offside calls, cameras bolted into every rafter. And the tech has already decided a goal.

On June 15 in Monterrey, Sweden were busy thrashing Tunisia when Mattias Svanberg came off the bench and scored with his first touch. The linesman’s flag shot up. Offside. The goal was gone, until it wasn’t. Video review handed it back, because the ball itself had registered a touch the human eye missed: a faint flick off Alexander Isak that reset the play and left Svanberg onside. Yet the cameras missed the flick. The sensor inside the ball caught it.

How does a ball overrule a linesman? Start with what FIFA has actually wired into the tournament. Sony’s Hawk-Eye underpins the video review, the goal-line decisions, the semi-automated offside system, and a “last touch” feature that settles who knocked the ball out for a corner.

Chenliang Xu, a computer-vision researcher at the University of Rochester, told the university’s news service it’s “a very sophisticated system that glues together multiple computer vision techniques.” Underneath, that means calibrated cameras, models trained to spot the ball and the players and their poses, and a thin layer of logic that decides when a human should take a look. 

Player and ball tracking run on neural networks trained on millions of labeled images, the same lineage of models behind face unlock and the perception stack in a self-driving car.

Xu compares the training to “teaching a child how to recognize things”: feed a model enough examples and it learns what matters. Sixteen cameras ring each stadium,…

LEAVE A REPLY

Please enter your comment!
Please enter your name here