This AI Model Can Intuit How the Physical World Works

The original version of this story appeared in Quanta Magazine.

Here’s a test for infants: Show them a glass of water on a desk. Hide it behind a wooden board. Now move the board toward the glass. If the board keeps going past the glass, as if it weren’t there, are they surprised? Many 6-month-olds are, and by a year, almost all children have an intuitive notion of an object’s permanence, learned through observation. Now some artificial intelligence models do too.

Researchers have developed an AI system that learns about the world via videos and demonstrates a notion of “surprise” when presented with information that goes against the knowledge it has gleaned.

The model, created by Meta and called Video Joint Embedding Predictive Architecture (V-JEPA), does not make any assumptions about the physics of the world contained in the videos. Nonetheless, it can begin to make sense of how the world works.

“Their claims are, a priori, very plausible, and the results are super interesting,” says Micha Heilbron, a cognitive scientist at the University of Amsterdam who studies how brains and artificial systems make sense of the world.

Higher Abstractions

As the engineers who build self-driving cars know, it can be hard to get an AI system to reliably make sense of what it sees. Most systems designed to “understand” videos in order to either classify their content (“a person playing tennis,” for example) or identify the contours of an object—say, a car up ahead—work in what’s called “pixel space.” The model essentially treats every pixel in a video as equal in importance.

But these pixel-space models come with limitations. Imagine trying to make sense of a suburban street. If the scene has cars, traffic lights and trees, the model might focus too much on irrelevant details such as the motion of the leaves. It might miss the color of the traffic light, or the positions of nearby cars. “When you go to images or video, you don’t want to work in [pixel] space because there are too many details you don’t want to model,” said Randall Balestriero, a computer scientist at Brown University.