How to Create Artificial Superintelligence

I have three articles that outline my model for creating sentient AI:

1. Reser, J. 2016. Incremental change in the set of coactive cortical assemblies enables mental continuity. Physiology and Behavior. 167: 222-237.

2. Reser, J. 2022. A Computational Architecture for Machine Consciousness and Artificial Superintelligence: Updating Working Memory Iteratively. arXiv: 2203.17255 

3. Reser, J. 2022. Artificial Intelligence Software Structured to Simulate Human Working Memory, Mental Imagery, and Mental Continuity. arXiv 2204.05138 

The first of these three articles is my model of working memory published in “Physiology and Behavior” in 2016. This model is meant to be instantiated within a computer to create the organizational complexity necessary for the development of computer consciousness. The full text article can be found here:

https://www.sciencedirect.com/science/article/pii/S0031938416308289

Everything on this site, including all figures and illustrations are free for you to use. There are no copyrights or patents and I want to encourage you to use the methods discussed here to create your own inventions. All content was written by Jared Edward Reser and is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Highlights

• The term state-spanning coactivity (SSC) is introduced to refer to shared content between successive brain states.

• SSC is made possible by sustained firing, binding, and recurrent processing.

• Sustained activity ensures that consecutive topographic maps are interrelated.

• SSC also permits continuous, algorithmic, and progressive alterations to mental imagery.

• Incremental change in SSC (icSSC) allows the continuity necessary for updating, modeling, and systemization.

Abstract

This opinion article explores how sustained neural firing in association areas allows high-order mental representations to be coactivated over multiple perception-action cycles, permitting sequential mental states to share overlapping content and thus be recursively interrelated. The term “state-spanning coactivity” (SSC) is introduced to refer to neural nodes that remain coactive as a group over a given period of time.

SSC ensures that contextual groupings of goal or motor-relevant representations will demonstrate continuous activity over a delay period. It also allows potentially related representations to accumulate and coactivate despite delays between their initial appearances. The nodes that demonstrate SSC are a subset of the active representations from the previous state, and can act as referents to which newly introduced representations of succeeding states relate.

Coactive nodes pool their spreading activity, converging on and activating new nodes, adding these to the remaining nodes from the previous state. Thus, the overall distribution of coactive nodes in cortical networks evolves gradually during contextual updating. The term “incremental change in state-spanning coactivity” (icSSC) is introduced to refer to this gradual evolution. Because a number of associated representations can be sustained continuously, each brain state is embedded recursively in the previous state, amounting to an iterative process that can implement learned algorithms to progress toward a complex result.

The longer representations are sustained, the more successive mental states can share related content, exhibit progressive qualities, implement complex algorithms, and carry thematic or narrative continuity. Included is a discussion of the implications that SSC and icSSC may have for understanding working memory, defining consciousness, and constructing AI architectures.