From Prediction to Compilation: Embracing Intrinsic Reliability in AI
In an era where AI’s role grows more critical, understanding its reliability is paramount. This manifesto, authored by JanusPater, emphasizes that the true measure of AI isn’t just predictive accuracy but execution legitimacy.
Key Insights Include:
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Definitions:
- Predictive System: Outputs probabilities for future states/actions.
- Executable System: Directly influences physical changes.
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Axioms for Reliable AI:
- Non-Hallucination Axiom: Outputs must have unique execution paths.
- Prediction-Execution Separation: Probabilistic systems shouldn’t generate executable actions directly.
- Compilation Primacy: Actions must derive from deterministic models.
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Conclusion: For AI to function safely in high-stakes settings, it must prioritize execution legitimacy over mere predictive success.
Explore this thought-provoking manifesto and understand the future landscape of reliable AI. Join the conversation and share your perspective!
