A Mathematical Approach to Constraining Neural Abstraction and the
Mechanisms Needed to Scale to Higher-Order Cognition
- URL: http://arxiv.org/abs/2108.05494v1
- Date: Thu, 12 Aug 2021 02:13:22 GMT
- Title: A Mathematical Approach to Constraining Neural Abstraction and the
Mechanisms Needed to Scale to Higher-Order Cognition
- Authors: Ananta Nair
- Abstract summary: Artificial intelligence has made great strides in the last decade but still falls short of the human brain, the best-known example of intelligence.
Not much is known of the neural processes that allow the brain to make the leap to achieve so much from so little.
This paper proposes a mathematical approach using graph theory and spectral graph theory, to hypothesize how to constrain these neural clusters of information.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Artificial intelligence has made great strides in the last decade but still
falls short of the human brain, the best-known example of intelligence. Not
much is known of the neural processes that allow the brain to make the leap to
achieve so much from so little beyond its ability to create knowledge
structures that can be flexibly and dynamically combined, recombined, and
applied in new and novel ways. This paper proposes a mathematical approach
using graph theory and spectral graph theory, to hypothesize how to constrain
these neural clusters of information based on eigen-relationships. This same
hypothesis is hierarchically applied to scale up from the smallest to the
largest clusters of knowledge that eventually lead to model building and
reasoning.
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