The Deterministic Unification Model: Completing AI Theory Through State-Transition Computation
Description
This white paper introduces the Deterministic Unification Model, a unified theoretical framework that resolves eighty years of fragmented artificial intelligence theory. By integrating contributions from Turing, Shannon, Boole, Feynman, Pearl, Hinton, LeCun, Goodfellow, Paige, and Bin into a single deterministic state transition equation, the model provides a foundation for safe, reproducible, and auditable machine intelligence.
It describes the mathematical structure of deterministic computation, contrasts it with probabilistic architectures, and outlines implications for aerospace, healthcare, automotive systems, robotics, entertainment engineering, and cybersecurity.
A United States non provisional patent application covering this architecture was filed on November 25, 2025 (Application Number 19 400 020). A provisional patent application establishing priority was filed on April 18, 2025.
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The Deterministic Unification Model.pdf
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Additional details
Related works
- Is supplemented by
- Report: 10.5281/zenodo.17766646 (DOI)
Dates
- Issued
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2025-12-02This report introduces the Deterministic Unification Model. It presents a mathematical framework that integrates state transition computation with principles drawn from Turing, Shannon, Boole, Feynman, Pearl, and contemporary representation learning. The model addresses core limitations of probabilistic AI including nondeterminism, representational drift, adversarial sensitivity, and the absence of reproducible inference. It defines machine intelligence as a deterministic function of system state, structured input, and rule governed transitions. The report demonstrates applicability across aerospace, healthcare, automotive systems, robotics, cybersecurity, and large scale visualization. It establishes determinism as the necessary foundation for safe, interpretable, and fully auditable machine intelligence.