TL;DR
This paper reviews the concept of a universal learning algorithm that explains mental processes, focusing on innate brain structures and the potential for all significant algorithms to be learned.
Contribution
It synthesizes current understanding to outline the ingredients of a general-purpose learning algorithm based on architectural and functional principles.
Findings
Identifies key architectural components of the brain relevant to learning.
Proposes that innate circuits serve as a foundation for acquiring complex mental algorithms.
Reviews existing theories to support the feasibility of a single general-purpose learning algorithm.
Abstract
There exists a theory of a single general-purpose learning algorithm which could explain the principles of its operation. This theory assumes that the brain has some initial rough architecture, a small library of simple innate circuits which are prewired at birth and proposes that all significant mental algorithms can be learned. Given current understanding and observations, this paper reviews and lists the ingredients of such an algorithm from both architectural and functional perspectives.
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Neural Networks and Applications · Machine Learning and Algorithms