Machine learning researchers build a better world model

Machine learning researchers have developed a technique that allows world models to make better predictions about how objects will behave.

World models are used to train physical AI systems like robots how to navigate the real world. They predict the consequences of actions. But they do so based on observed data. These models could provide better direction to robots if they were able to make inferences about possible actions without being trained for all the possible outcomes.

Randall Balestriero (Brown University, AMI Labs), Kerda Varaku, and Turing Award winner Yann LeCun (NYU, AMI Labs) describe an approach that might allow that to happen in a pre-print paper, "Counterfactual World Models: Natura Non Facit Saltus."

The Latin refers to Gottfried Leibniz's axiom " nature makes no leaps," which had a good run until it ran headlong into quantum mechanics. Yet for the authors, the saying explains how they have been able to devise a method for zero-shot physics inference.

Counterfactual world …