0.24_Artificial_Intelligence

layout	article
title	UET Topic 0.24: Artificial Intelligence
description	AI scaling-law and sparse-architecture diagnostics in the UET framework.

0.24 Artificial Intelligence

Note

AI-Digest: This topic currently supports an internal AI scaling and sparse-architecture benchmark. It checks topic-local scaling-law constants, a small GPT-style scaling table, and dense/MoE active-parameter fractions. It does not yet prove AI alignment, ethics as a physical law, consciousness, or universal intelligence dynamics.

Current Claim Boundary

The accepted evidence for this topic is limited to reproducible scaling-law and sparse-architecture diagnostics. UET-specific bridges such as alpha_N ~= kappa, entropy-based learning-rate rules, alignment equilibrium, and consciousness models remain open until they have their own source-backed verifier artifacts.

Conceptual Diagram

flowchart LR
    A["scaling_laws.json"] --> B["alpha_N, alpha_D, alpha_C"]
    C["GPT3_Scaling_Laws.csv"] --> D["log-log alpha fit"]
    B --> E["scaling benchmark artifact"]
    D --> E
    F["deepseek_moe_data.json"] --> G["active_fraction and capacity ratio"]
    G --> E
    H["kappa_macro proxy"] --> I["alpha-kappa bridge check"]
    B --> I
    I --> E
    E --> J["Claim Class C boundary"]
    K["alignment / ethics / consciousness scripts"] --> L["future lanes only"]

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Evidence Matrix

Layer	Current status	Evidence / artifact	Claim allowed
Scaling-law constants	Source-referenced working copy	Data/03_Research/scaling_laws.json	benchmark input
Local exponent fit	Runnable diagnostic	Research_AI_Scaling_Audit.py	internal consistency check
MoE sparsity	Runnable diagnostic	deepseek_moe_data.json, verifier artifact	active-parameter comparison
alpha_N to UET kappa bridge	Heuristic/open	artifact blocker when mismatch is too large	no constant-identification claim
Entropy-learning engine	Implemented but not benchmark-validated	UET_AI_Core.py, FORMULA_AUDIT.md	future optimizer benchmark
Alignment/ethics/consciousness	Exploratory	limitations and future scripts	no physical-law claim

5x4 Grid Structure

Pillar	Purpose
Doc/	analysis notes for AI entropy, ethics, and alignment concepts
Ref/	referenced AI scaling, neural network, and transformer materials
Data/	topic-local scaling-law and architecture metadata working copies
Code/	engine, proof, research, competitor, and developmental-AI scripts
Result/	verifier artifacts, plots, and run logs

Quick Start

cd C:\Users\santa\Desktop\uet_harness
python docs/topics/0.24_Artificial_Intelligence/Code/03_Research/Research_AI_Scaling_Audit.py

Key Files

• FORMULA_AUDIT.md: reviewed formula/variable/unit/proof-status registry.
• VERIFICATION_SPEC.md: primary command, metrics, thresholds, and artifact contract.
• DATA_MANIFEST.md: data provenance, unit conventions, hashes, and benchmark roles.
• METHOD.md: method scope, evidence lanes, variables, and dependency policy.
• LIMITATIONS.md: blockers that prevent stronger AI/alignment claims.

Current Limitations

• Scaling and model metadata are local working copies, not a complete archival upstream provenance package.
• The kappa bridge is heuristic and must not be promoted as a derived law.
• MoE sparsity is not equivalent to full efficiency, intelligence, safety, or alignment.
• Ethics, alignment, consciousness, and developmental-AI claims remain future lanes until separately verified.

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