Medium Article: https://medium.com/@jsmith0475/building-human-facing-agentic-systems-the-psychology-and-sociology-of-super-intelligence-1571cf565d51

Dr. Jerry Smith's January 2025 Medium article explores the psychological, sociological, and ethical implications of "human-facing agentic systems," AI powered by large language models (LLMs). These systems, leveraging multi-headed attention mechanisms, aim to collaborate with humans, not merely serve as tools. The article emphasizes the potential for both empowerment and manipulation inherent in these systems, highlighting the crucial role of self-interest, emotion, and identity in human-AI interaction. Smith concludes with a call for responsible development guided by transparency, inclusivity, and justice, warning against the potential for these systems to become instruments of control.

Building Human-Facing Agentic Systems: The Psychology and Sociology of Super Intelligence

Medium Article: https://medium.com/@jsmith0475/we-solved-ais-reproducibility-crisis-by-treating-it-like-a-physics-problem-8936aed52923
The article "Cognitive Anchoring," by Dr. Jerry A. Smith, details a novel solution to the reproducibility crisis in large language models (LLMs) by treating the issue as a physics coordination problem. The core proposal, cognitive anchoring, uses principles from gauge theory to synchronize the attention heads within transformer models, which otherwise drift and produce inconsistent reasoning paths. The authors introduce four specific anchoring mechanisms—symbolic, temporal, spatial, and symmetry—to constrain representational degrees of freedom without sacrificing logical content, leading to a 38% improvement in symbolic consistency during complex tasks like discovering field equations. The framework is presented as a mechanistic alternative to prompt engineering and is demonstrated to generalize across scientific discovery and behavioral science applications, such as modeling complex cultural multipliers in athletic valuation. Ultimately, the paper establishes anchoring as a foundational protocol for achieving stable and reliable inference in AI reasoning systems.

We Solved AI's Reproducibility Crisis by Treating It Like a Physics Problem

In-Depth Explorations of Neuroscience-Inspired Architectures Revolutionizing AI.

We Solved AI's Reproducibility Crisis by Treating It Like a Physics Problem

Dr. Jerry A. Smith

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