Artificial general segmentation

Abstract

We argue that the ability to find meaningful chunks in sequential input is a core cognitive ability for artificial general intelligence, and that the Voting Experts algorithm, which searches for an information theoretic signature of chunks, provides a general implementation of this ability. In support of this claim, we demonstrate that VE successfully finds chunks in a wide variety of domains, solving such diverse tasks as word segmentation and morphology in multiple languages, visually recognizing letters in text, finding episodes in sequences of robot actions, and finding boundaries in the instruction of an AI student. We also discuss further desirable attributes of a general chunking algorithm, and show that VE possesses them.