On the link between conscious function and general intelligence in
humans and machines
- URL: http://arxiv.org/abs/2204.05133v1
- Date: Thu, 24 Mar 2022 02:22:23 GMT
- Title: On the link between conscious function and general intelligence in
humans and machines
- Authors: Arthur Juliani, Kai Arulkumaran, Shuntaro Sasai, Ryota Kanai
- Abstract summary: We look at the cognitive abilities associated with three theories of conscious function.
We find that all three theories specifically relate conscious function to some aspect of domain-general intelligence in humans.
We propose ways in which insights from each of the three theories may be combined into a unified model.
- Score: 0.9176056742068814
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Theory of Mind Enhances Collective Intelligence [1.8434042562191815]
We argue that flexible collective intelligence in human social settings is improved by our use of a specific cognitive tool: our Theory of Mind.
We then place these capabilities in the context of the next steps in artificial intelligence embedded in a future that includes an effective human-AI hybrid social ecology.
arXiv Detail & Related papers (2024-11-14T03:58:50Z) - AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
Mathematics is one of the most powerful conceptual systems developed and used by the human species.
Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
arXiv Detail & Related papers (2023-10-19T02:00:31Z) - The Nature of Intelligence [0.0]
The essence of intelligence commonly represented by both humans and AI is unknown.
We show that the nature of intelligence is a series of mathematically functional processes that minimize system entropy.
This essay should be a starting point for a deeper understanding of the universe and us as human beings.
arXiv Detail & Related papers (2023-07-20T23:11:59Z) - Memory-Augmented Theory of Mind Network [59.9781556714202]
Social reasoning requires the capacity of theory of mind (ToM) to contextualise and attribute mental states to others.
Recent machine learning approaches to ToM have demonstrated that we can train the observer to read the past and present behaviours of other agents.
We tackle the challenges by equipping the observer with novel neural memory mechanisms to encode, and hierarchical attention to selectively retrieve information about others.
This results in ToMMY, a theory of mind model that learns to reason while making little assumptions about the underlying mental processes.
arXiv Detail & Related papers (2023-01-17T14:48:58Z) - Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs [77.88043871260466]
We show that one of today's largest language models lacks this kind of social intelligence out-of-the box.
We conclude that person-centric NLP approaches might be more effective towards neural Theory of Mind.
arXiv Detail & Related papers (2022-10-24T14:58:58Z) - Mind the gap: Challenges of deep learning approaches to Theory of Mind [0.0]
Theory of Mind is an essential ability of humans to infer the mental states of others.
Here we provide a coherent summary of the potential, current progress, and problems of deep learning approaches to Theory of Mind.
arXiv Detail & Related papers (2022-03-30T15:48:05Z) - A World-Self Model Towards Understanding Intelligence [0.0]
We will compare human and artificial intelligence, and propose that a certain aspect of human intelligence is the key to connect perception and cognition.
We will present the broader idea of "concept", the principles and mathematical frameworks of the new model World-Self Model (WSM) of intelligence, and finally an unified general framework of intelligence based on WSM.
arXiv Detail & Related papers (2022-03-25T16:42:23Z) - Social Neuro AI: Social Interaction as the "dark matter" of AI [0.0]
We argue that empirical results from social psychology and social neuroscience along with the framework of dynamics can be of inspiration to the development of more intelligent artificial agents.
arXiv Detail & Related papers (2021-12-31T13:41:53Z) - Inductive Biases for Deep Learning of Higher-Level Cognition [108.89281493851358]
A fascinating hypothesis is that human and animal intelligence could be explained by a few principles.
This work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing.
The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities.
arXiv Detail & Related papers (2020-11-30T18:29:25Z) - Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of
AI/AGI Using Multiple Intelligences and Learning Styles [95.58955174499371]
We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains.
Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom.
arXiv Detail & Related papers (2020-08-07T21:00:13Z) - Machine Common Sense [77.34726150561087]
Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI)
This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
arXiv Detail & Related papers (2020-06-15T13:59:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.