How can we understand AI as a model of human cognition?

Abstract

Artificial intelligence is a foundational technology applied to the modern society. Deep learning models, the core techniques in the modern AI, lacks, however, human readability or comprehensiveness of their internal structure. I consider representational arbitrariness in the AI model as a universal function approximator, and point out how human cognition and its bias plays a crucial role in choosing a natural representation for a given task. As many of AI models inspired and mimicking human cognition, I hypothesize that understanding cognitive computation would lead a principled design of the new AI architecture, that learns both data and how the data should be preprocessed.