Is Intelligence Artificial?
- URL: http://arxiv.org/abs/1403.1076v9
- Date: Wed, 17 Jul 2024 08:52:08 GMT
- Title: Is Intelligence Artificial?
- Authors: Kieran Greer,
- Abstract summary: This paper attempts to give a unifying definition that can be applied to the natural world in general and then Artificial Intelligence.
A metric that is grounded in Kolmogorov's Complexity Theory is suggested, which leads to a measurement about entropy.
A version of an accepted AI test is then put forward as the 'acid test' and might be what a free-thinking program would try to achieve.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Our understanding of intelligence is directed primarily at the human level. This paper attempts to give a more unifying definition that can be applied to the natural world in general and then Artificial Intelligence. The definition would be used more to qualify than quantify it and might help when making judgements on the matter. While correct behaviour is the preferred definition, a metric that is grounded in Kolmogorov's Complexity Theory is suggested, which leads to a measurement about entropy. A version of an accepted AI test is then put forward as the 'acid test' and might be what a free-thinking program would try to achieve. Recent work by the author has been more from a direction of mechanical processes, or ones that might operate automatically. This paper agrees that intelligence is a pro-active event, but also notes a second aspect to it that is in the background and mechanical. The paper suggests looking at intelligence and the conscious as being slightly different, where the conscious is this more mechanical aspect. In fact, a surprising conclusion can be a passive but intelligent brain being invoked by active and less intelligent senses.
Related papers
- Machine learning and information theory concepts towards an AI
Mathematician [77.63761356203105]
The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms of mathematical reasoning.
This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities.
It takes an information-theoretical posture to ask questions about what constitutes an interesting mathematical statement.
arXiv Detail & Related papers (2024-03-07T15:12:06Z) - On a Functional Definition of Intelligence [0.0]
Without an agreed-upon definition of intelligence, asking "is this system intelligent?"" is an untestable question.
Most work on precisely capturing what we mean by "intelligence" has come from the fields of philosophy, psychology, and cognitive science.
We present an argument for a purely functional, black-box definition of intelligence, distinct from how that intelligence is actually achieved.
arXiv Detail & Related papers (2023-12-15T05:46:49Z) - The Generative AI Paradox: "What It Can Create, It May Not Understand" [81.89252713236746]
Recent wave of generative AI has sparked excitement and concern over potentially superhuman levels of artificial intelligence.
At the same time, models still show basic errors in understanding that would not be expected even in non-expert humans.
This presents us with an apparent paradox: how do we reconcile seemingly superhuman capabilities with the persistence of errors that few humans would make?
arXiv Detail & Related papers (2023-10-31T18:07:07Z) - 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) - Suffering Toasters -- A New Self-Awareness Test for AI [0.0]
We argue that all current intelligence tests are insufficient to point to the existence or lack of intelligence.
We propose a new approach to test for artificial self-awareness and outline a possible implementation.
arXiv Detail & Related papers (2023-06-29T18:58:01Z) - Emergence of Machine Language: Towards Symbolic Intelligence with Neural
Networks [73.94290462239061]
We propose to combine symbolism and connectionism principles by using neural networks to derive a discrete representation.
By designing an interactive environment and task, we demonstrated that machines could generate a spontaneous, flexible, and semantic language.
arXiv Detail & Related papers (2022-01-14T14:54:58Z) - An argument for the impossibility of machine intelligence [0.0]
We define what it is to be an agent (device) that could be the bearer of AI.
We show that the mainstream definitions of intelligence' are too weak even to capture what is involved when we ascribe intelligence to an insect.
We identify the properties that an AI agent would need to possess in order to be the bearer of intelligence by this definition.
arXiv Detail & Related papers (2021-10-20T08:54:48Z) - 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) - 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.