Towards intelligent Internet-roaming agents for mining and inference from medical data

Area of Science:

• Computational Logic
• Quantum Mechanics
• Artificial Intelligence
• Medical Informatics

Background:

• Explosion of distributed medical data (petabytes) necessitates decentralized analysis.
• Current privacy, security, and computational constraints limit centralized data analysis.
• Need for roaming agents to perform local analysis and return only conclusions.

Purpose of the Study:

• To explore requirements for programmable laws of probabilistic higher-order logical thought.
• To investigate unification of diverse inference approaches into a self-consistent system.
• To adapt quantum mechanics for probabilistic higher-order logic inference.

Main Methods:

• Conceptual exploration of decentralized data analysis requirements.
• Review of current inference methodologies and their limitations.
• Mathematical transformation of quantum mechanics using split complex numbers (h, where hh = +1) from standard complex numbers (i, where i*i = -1).

Main Results:

• Demonstration that transformed quantum mechanics can serve as an inference system for probabilistic higher-order logic.
• Identification of emergent properties within this adapted system.
• Potential for this system to offer insights into the nature of cognition.

Conclusions:

• A novel inference framework based on adapted quantum mechanics is proposed for decentralized AI.
• This approach addresses the need for robust, unified probabilistic logic in complex data environments.
• The system's emergent properties may provide a new lens for understanding higher-order thought.