What is it about?
Sensory environments can be complex to perceive, analyze and respond to effectively. Generative self-learning models create simplified but highly informative models of sensory environments by learning to reproduce a representative selection of samples with high accuracy. This paper examines informative models of simple visual environments, such as images of basic geometrical shapes created by generative neural models.
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Why is it important?
Understanding the process of creation of structured, conceptual representations of sensory environments can be instrumental in examination of intelligent functions and evolution of intelligence.
Perspectives
In conducting the experiments and writing this paper it was exciting to observe at the tip of the experimental models how even simple intelligent systems, of artificial or biological nature can acquire some key facets and pieces of intelligence such as conceptualization, abstraction and others, in the process of recreation of observable data under some natural constraints.
Serge Dolgikh
National Aviation University
Read the Original
This page is a summary of: Geometry and Topology of Conceptual Representations of Simple Visual
Data, Current Chinese Science, April 2023, Bentham Science Publishers,
DOI: 10.2174/2210298103666221130101950.
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