  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.
