The Inversion Error: Why Safe AGI Requires an Enactive Floor and State-Space Reversibility
This article discusses a systems design diagnosis of hallucination, corrigibility, and the structural gap that scaling cannot close in the pursuit of safe Artificial General Intelligence (AGI).
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Why it matters
This article offers a novel systems-level perspective on the challenges of achieving safe and reliable AGI, which is a critical goal for the AI research community.
Key Points
- 1Hallucination and corrigibility are key challenges in achieving safe AGI
- 2Scaling alone cannot close the structural gap required for safe AGI
- 3An
- 4 and state-space reversibility are necessary for safe AGI
Details
The article argues that safe AGI requires addressing the
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