On Information Processing Limitations In Humans and Machines

TL;DR

This paper explores the parallels between human and machine information processing limitations, highlighting the need for new frameworks to improve AI reliability based on insights from information theory and cognitive neuroscience.

Contribution

It discusses the implications of human information processing limitations for developing more reliable artificial intelligence systems and calls for novel conceptual frameworks.

Findings

Human response times increase with information complexity

Existing models assume linear uncertainty increase

Need for new frameworks in AI development

Abstract

Information theory is concerned with the study of transmission, processing, extraction, and utilization of information. In its most abstract form, information is conceived as a means of resolving uncertainty. Shannon and Weaver (1949) were among the first to develop a conceptual framework for information theory. One of the key assumptions of the model is that uncertainty increases linearly with the amount of complexity (in bit units) of information transmitted or generated. A whole body of data from the cognitive neurosciences has shown since that the time of human response or action increases in a similar fashion as a function of information complexity. This paper will discuss some of the implications of what is known about the limitations of human information processing for the development of reliable Artificial Intelligence. It is concluded that novel conceptual frameworks are needed to inspire future studies on this complex problem space.