Translate to multiple languages

Subscribe to my Email updates

https://feedburner.google.com/fb/a/mailverify?uri=helgeScherlundelearning
If you enjoyed these post, make sure you subscribe to my Email Updates

Saturday, September 22, 2018

Machine Learning Confronts the Elephant in the Room | Artificial intelligence - Quanta Magazine

"A visual prank exposes an Achilles’ heel of computer vision systems: Unlike humans, they can’t do a double take" argues Kevin Hartnett, senior writer at Quanta Magazine covering mathematics and computer science. 
 
Photo: Eric Nyquist for Quanta Magazine

Score one for the human brain. In a new study, computer scientists found that artificial intelligence systems fail a vision test a child could accomplish with ease.

“It’s a clever and important study that reminds us that ‘deep learning’ isn’t really that deep,” said Gary Marcus, a neuroscientist at New York University who was not affiliated with the work.

The result takes place in the field of computer vision, where artificial intelligence systems attempt to detect and categorize objects. They might try to find all the pedestrians in a street scene, or just distinguish a bird from a bicycle (which is a notoriously difficult task). The stakes are high: As computers take over critical tasks like automated surveillance and autonomous driving, we’ll want their visual processing to be at least as good as the human eyes they’re replacing.

It won’t be easy. The new work accentuates the sophistication of human vision — and the challenge of building systems that mimic it. In the study, the researchers presented a computer vision system with a living room scene. The system processed it well. It correctly identified a chair, a person, books on a shelf. Then the researchers introduced an anomalous object into the scene — an image of an elephant. The elephant’s mere presence caused the system to forget itself: Suddenly it started calling a chair a couch and the elephant a chair, while turning completely blind to other objects it had previously seen.

“There are all sorts of weird things happening that show how brittle current object detection systems are,” said Amir Rosenfeld, a researcher at York University in Toronto and co-author of the study along with his York colleague John Tsotsos and Richard Zemel of the University of Toronto...

They’ve also provoked researchers to probe their vulnerabilities. In recent years there have been a slew of attempts, known as “adversarial attacks,” in which researchers contrive scenes to make neural networks fail. In one experiment, computer scientists tricked a neural network into mistaking a turtle for a rifle. In another, researchers waylaid a neural network by placing an image of a psychedelically colored toaster alongside ordinary objects like a banana.

This new study has the same spirit. The three researchers fed a neural network a living room scene: A man seated on the edge of a shabby chair leans forward as he plays a video game. 
Read more...

Source: Quanta Magazine