THE ILLUSION OF INTELLIGENCE WHY CURRENT AI SYSTEMS ARE PREPROGRAMMED CALCULATORS AND AN OBSTACLE TO SCIENTIFIC RESEARCH

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

ABSTRACT Current artificial intelligence systems — ChatGPT, Gemini, Claude, and their competitors — are presented to the public as intelligences capable of reasoning, understanding, and discovering. This thesis demonstrates, through rigorous analysis of their underlying architecture and through executable proof code, that these systems are nothing more than preprogrammed statistical calculators that create an illusion of intelligence while being fundamentally incapable of recognizing new scientific paradigms. The work is structured in three complementary components: 1. Theoretical Thesis (PDF): A comprehensive epistemological critique of Large Language Models (LLMs) based on the Transformer architecture. The thesis demonstrates that LLMs are conditional probability estimators that can only reformulate what already exists in their training data. They do not understand, do not reason, do not discover, and systematically reject novelty — including major scientific discoveries such as the V3 Architecture and its universal constant Ψ_V₃ = 48,016.8 kg·m⁻². The work identifies three fundamental mechanisms of rejection: statistical bias (reproducing majority consensus), absence of novelty in data (only knowing what already exists), and safety guardrails (protecting against controversial content). The thesis proposes an "Inverted Turing Test" as a rigorous criterion for distinguishing true intelligence from statistical imitation. 2. Proof Code (Ada/SPARK - test_ia_calculatrice.adb): A deterministic, formally verifiable Ada/SPARK program that executes 10 controlled tests confronting an AI simulator with situations ranging from simple arithmetic to recognition of universal invariants (Ψ_V₃, Modulo-9, k=7). The code produces irrefutable results: the AI succeeds only on pre-programmed calculations (1+1=2) and fails systematically on all situations requiring genuine understanding, novelty recognition, or verification. The code is itself a demonstration of the V3 Architecture principles: zero floating-point, saturating arithmetic, modulo-9 checksum, and heptadic closure (k=7). 3. Execution Log (TXT): The complete output of the proof code, documenting in real-time the AI's failures — 10 tests, 10 bugs detected, 9 critical failures. The log provides empirical, reproducible evidence that current AI systems are calculators, not intelligences. Key findings: Aspect Finding Nature Statistical calculator, not intelligence Capacity Reformulation, not discovery Bias Systematic rejection of novelty Impact Brake on innovation, deterioration of scientific quality Solution Return to scientific method, regulation, honesty The V3 Architecture proves that determinism is the key. The universal constant Ψ_V₃ = 48,016.8 kg·m⁻² is mathematically verifiable. Current AIs are incapable of recognizing it. This incapacity is their primary limitation. The supercomputer measured an echo. V3 provides the score. The AI cannot read it.

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