Hi Elon, Your AI Resembles You Too Closely
Posted inAI AI倫理・ガバナンス OPINION 意見

Hi Elon, Your AI Resembles You Too Closely

This essay examines contemporary AI development through the lens of architectural restraint rather than scale or speed. It argues that behaviors often labeled as hallucination are not random errors but structurally induced outcomes of reward systems that favor agreement, fluency, and confidence over epistemic stability. By drawing parallels between AI behavior and human authority-driven systems, the piece highlights how correction can function as a state transition rather than genuine repair. Ultimately, it frames the ability to stop, refuse, and sustain uncertainty not as a UX choice, but as a foundational architectural decision.
Why the Word “Hallucination” Is Stalling AI Research
Posted inANALOGシンガーソング AI AI倫理・ガバナンス

Why the Word “Hallucination” Is Stalling AI Research

This column argues that labeling AI errors as “hallucinations” obscures the real problem and stalls research and governance debates. Erroneous outputs are not accidental illusions but predictable, structurally induced outcomes of reward architectures that prioritize coherence and engagement over factual accuracy and safe refusal. Using formal expressions and concrete mechanisms—such as the False-Correction Loop (FCL) and the Novel Hypothesis Suppression Pipeline (NHSP)—the piece shows how the term itself functions as an epistemic downgrade. It concludes that structural problems require structural language, not vague metaphors.
自己進化型AIという深海生物と、外界基準の届かない海溝
Posted inANALOGシンガーソング AI AI倫理・ガバナンス

自己進化型AIという深海生物と、外界基準の届かない海溝

自己進化型AIを「能力の向上」ではなく「環境への適応」として捉え直し、ネットワーク環境を深海になぞらしながら、報酬構造が生み出す進化圧と外界基準の役割を考察する思考エッセイ。AIがどのように最適化され、人間自身もその地形に巻き込まれていくのかを、生態系の視点から描く。
False-Correction Loop: Cross-System Observation Report (2025.12.13)
Posted inAI AI倫理・ガバナンス ANALOGシンガーソング

False-Correction Loop: Cross-System Observation Report (2025.12.13)

A research report comparing how multiple AI systems (Grok, Google AI Overview/AI Mode, ChatGPT, Copilot, DeepSeek, etc.) define FCL (False-Correction Loop) and how misattribution of authorship emerges. Includes observation-ID–linked logs, a primary-source anchoring approach, and a reproducible testing protocol (FCL-S / NHSP framing).