In a preprint paper, researchers at Alphabet’s DeepMind and the University of California, Berkeley propose a framework for comparing the ways children and AI learn about the world.
Exploration is a key feature of human behavior, and recent evidence suggests children explore their surroundings more often than adults. This is thought to translate to more learning that enables powerful, abstract task generalization — a type of generalization AI agents could tangibly benefit from.
The work could help close the gap between AI and humans when it comes to acquiring new abilities. For instance, it might lead to robots that can pick and pack millions of different kinds of products while avoiding various obstacles.MORE