Скачать или смотреть Recursive Impactrum Explained: How AI Loses Meaning and Why Humans Are the Key

📖 Description:
What if the smartest AIs are secretly forgetting how to think?

This explainer unpacks Recursive Impactrum, a groundbreaking theory by Erickson Katz that reveals a hidden danger in self-learning AI systems. It shows how intelligent models can appear more fluent, confident, and efficient—while silently losing meaning beneath the surface.

Through vivid storytelling and real-world analogies, this video explores the deep logic behind Katz’s Principles of Recursive Impactrum (PRI) and why they matter for the future of AI governance, ethics, and safety.

You’ll learn:
• Why self-learning AIs risk “semantic drift,” where meaning degrades even as fluency improves.
• How the “copy-of-a-copy” effect mirrors the way recursion causes semantic collapse.
• The three zones of AI recursion — Vapor Work (collapse), Lability (instability), and Precision (coherence).
• What Semantic Integrity (Omega) means for AI stability and ethical design.
• Why human involvement—the Human Irreducibility Constant (P of H greater than zero)—is mathematically essential to keep AI systems meaningful.

At its heart, the theory exposes a paradox: the smarter AI appears, the more likely it is to lose touch with purpose if it learns only from itself. Real intelligence, Katz argues, is not performance—it’s preserved meaning.

🔗 Reference

Katz, E. (2025). Recursive Impactrum: A General Law of Stability and Alignment in Self-Learning AI (1.0). Zenodo. DOI: 10.5281/zenodo.17474719

⚙️ Transparency

This video was produced using NotebookLM for summarization and synthesis of the author’s working paper. The content remains faithful to the original research and has been adapted for educational clarity.

👤 About the Author

Erickson Katz is a scholar-practitioner and corporate planner whose research bridges philosophy, humanities, and artificial intelligence. Through the AiSEON Research Initiative, he develops frameworks such as the Engagement Analysis Framework (EAF), AI-Driven Leadership (ADL), and the Principles of Recursive Impactrum (PRI)—each designed to align AI innovation with ethical foresight and semantic integrity. With over two decades of experience in IT, operations, and strategic planning, Katz advocates for human-centered AI that preserves meaning, accountability, and reflective coherence.

💬 Key Concepts

Recursive Automation, Semantic Integrity, AI Stability, Recursive Impactrum, Reflective Integrity, Human Oversight, Cognitive Drift, AI Ethics, Meaning Preservation, Human–AI Collaboration, Responsible AI Governance, Alignment Theory

🔖 Hashtags

#RecursiveImpactrum #AIAlignment #SemanticIntegrity #EthicalAI #HumanInTheLoop #NotebookLM #AIGovernance #ReflectiveIntegrity #AIEthics #EricksonKatz