A Computational Architecture for Machine Consciousness and Artificial Superintelligence: Updating Working Memory Iteratively

This theoretical article examines how to construct human-like working memory and thought processes within a computer. There should be two working memory stores, one analogous to sustained firing in association cortex, and one analogous to synaptic potentiation in the cerebral cortex. These stores must be constantly updated with new representations that arise from either environmental stimulation or internal processing. They should be updated continuously, and in an iterative fashion, meaning that, in the next state, some items in the set of coactive items should always be retained. Thus, the set of concepts coactive in working memory will evolve gradually and incrementally over time. This makes each state is a revised iteration of the preceding state and causes successive states to overlap and blend with respect to the set of representations they contain. As new representations are added and old ones are subtracted, some remain active for several seconds over the course of these changes. This persistent activity, similar to that used in artificial recurrent neural networks, is used to spread activation energy throughout the global workspace to search for the next associative update. The result is a chain of associatively linked intermediate states that are 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 intelligence.

Artificial Intelligence Software Structured to Simulate Human Working Memory, Mental Imagery, and Mental Continuity

This article presents an artificial intelligence (AI) architecture intended to simulate the human working memory system as well as the manner in which it is updated iteratively. It features several interconnected neural networks designed to emulate the specialized modules of the cerebral cortex. These are structured hierarchically and integrated into a global workspace. They are capable of temporarily maintaining high-level patterns akin to the psychological items maintained in working memory. This maintenance is made possible by persistent neural activity in the form of two modalities: sustained neural firing (resulting in a focus of attention) and synaptic potentiation (resulting in a short-term store). This persistent activity is updated iteratively resulting in incremental changes to the content of the working memory system. As the content stored in working memory gradually evolves, successive states overlap and are continuous with one another. The present article will explore how this architecture can lead to gradual shift in the distribution of coactive representations, ultimately leading to mental continuity between processing states, and thus to human-like cognition.