  Artificial General Intelligence is a field of research aiming to distill the
principles of intelligence that operate independently of a specific problem
domain or a predefined context and utilize these principles in order to
synthesize systems capable of performing any intellectual task a human being is
capable of and eventually go beyond that. While "narrow" artificial
intelligence which focuses on solving specific problems such as speech
recognition, text comprehension, visual pattern recognition, robotic motion,
etc. has shown quite a few impressive breakthroughs lately, understanding
general intelligence remains elusive. In the paper we offer a novel theoretical
approach to understanding general intelligence. We start with a brief
introduction of the current conceptual approach. Our critique exposes a number
of serious limitations that are traced back to the ontological roots of the
concept of intelligence. We then propose a paradigm shift from intelligence
perceived as a competence of individual agents defined in relation to an a
priori given problem domain or a goal, to intelligence perceived as a formative
process of self-organization by which intelligent agents are individuated. We
call this process open-ended intelligence. Open-ended intelligence is developed
as an abstraction of the process of cognitive development so its application
can be extended to general agents and systems. We introduce and discuss three
facets of the idea: the philosophical concept of individuation, sense-making
and the individuation of general cognitive agents. We further show how
open-ended intelligence can be framed in terms of a distributed,
self-organizing network of interacting elements and how such process is
scalable. The framework highlights an important relation between coordination
and intelligence and a new understanding of values. We conclude with a number
of questions for future research.
