The Computational Theory of Intelligence: Information Entropy
This work addresses a foundational problem in AI by offering a new theoretical perspective on intelligence, which could influence broad areas of machine learning and computational theory.
The paper tackles the problem of defining computational intelligence by proposing an information theoretic framework, showing that intelligence can be viewed as an entropy-minimizing process, and demonstrates this with a simple clustering example.
This paper presents an information theoretic approach to the concept of intelligence in the computational sense. We introduce a probabilistic framework from which computational intelligence is shown to be an entropy minimizing process at the local level. Using this new scheme, we develop a simple data driven clustering example and discuss its applications.