On the link between conscious function and general intelligence in humans and machines
File(s)
Working paper
Author(s)
Juliani, Arthur
Arulkumaran, Kai
Sasai, Shuntaro
Kanai, Ryota
Type
Working Paper
Abstract
In popular media, there is often a connection drawn between the advent of
awareness in artificial agents and those same agents simultaneously achieving
human or superhuman level intelligence. In this work, we explore the validity
and potential application of this seemingly intuitive link between
consciousness and intelligence. We do so by examining the cognitive abilities
associated with three contemporary theories of conscious function: Global
Workspace Theory (GWT), Information Generation Theory (IGT), and Attention
Schema Theory (AST). We find that all three theories specifically relate
conscious function to some aspect of domain-general intelligence in humans.
With this insight, we turn to the field of Artificial Intelligence (AI) and
find that, while still far from demonstrating general intelligence, many
state-of-the-art deep learning methods have begun to incorporate key aspects of
each of the three functional theories. Given this apparent trend, we use the
motivating example of mental time travel in humans to propose ways in which
insights from each of the three theories may be combined into a unified model.
We believe that doing so can enable the development of artificial agents which
are not only more generally intelligent but are also consistent with multiple
current theories of conscious function.
awareness in artificial agents and those same agents simultaneously achieving
human or superhuman level intelligence. In this work, we explore the validity
and potential application of this seemingly intuitive link between
consciousness and intelligence. We do so by examining the cognitive abilities
associated with three contemporary theories of conscious function: Global
Workspace Theory (GWT), Information Generation Theory (IGT), and Attention
Schema Theory (AST). We find that all three theories specifically relate
conscious function to some aspect of domain-general intelligence in humans.
With this insight, we turn to the field of Artificial Intelligence (AI) and
find that, while still far from demonstrating general intelligence, many
state-of-the-art deep learning methods have begun to incorporate key aspects of
each of the three functional theories. Given this apparent trend, we use the
motivating example of mental time travel in humans to propose ways in which
insights from each of the three theories may be combined into a unified model.
We believe that doing so can enable the development of artificial agents which
are not only more generally intelligent but are also consistent with multiple
current theories of conscious function.
Date Issued
2022-03-24
Citation
2022
Publisher
arXiv
Copyright Statement
© 2022 The Author(s).
Identifier
http://arxiv.org/abs/2204.05133v1
Subjects
cs.AI
cs.AI
cs.NE
Publication Status
Published