A Cognitive Architecture for Machine Consciousness and Artificial Superintelligence: Thought Is Structured by the Iterative Updating of Working Memory

This article provides an analytical framework for how to simulate human-like thought processes within a computer. It describes how attention and memory should be structured, updated, and used to search for associative additions to the thought process. The working memory of mammals is made possible by two forms of persistent activity: sustained firing (preserving information on the order of seconds) and synaptic potentiation (preserving information on the order of minutes to hours). The article uses a series of over 40 original figures to systematically demonstrate how the iterative updating of these working memory stores provides dynamic, functional structure to thought and consciousness. In an AI implementation, these two stores should be updated continuously and in an iterative fashion, meaning that, in the next state, some proportion of the coactive representations should always be retained. Thus, the set of concepts coactive in working memory will evolve gradually and incrementally over time. This makes each state a revised iteration of the preceding state and causes successive states to overlap and blend with respect to the set of representations they contain. It is argued that without this overlap, AI systems cannot achieve mental continuity or machine consciousness. Persistent activity spreads activation energy throughout the hierarchical network to search for the next associative update. This search of long-term memory locates the most appropriate representation to be added to the global workspace. The result is a chain of associatively linked intermediate states capable of advancing toward a solution or goal. Iterative updating is conceptualized here as an information processing strategy, a computational and neurophysiological determinant of the stream of thought, and an algorithm for designing and programming artificial general intelligence.