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pascalmusabyimana
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AI & ML interests
computer vision, nlp , machine learning and deeplearning
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✅ New Article: *Pattern-Learning-Bridge (PLB)*
Title:
🧩 Pattern-Learning-Bridge: How SI-Core Actually Learns From Its Own Failures
🔗 https://huggingface.co/blog/kanaria007/learns-from-its-own-failures
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Summary:
Most stacks “learn” by fine-tuning weights and redeploying — powerful, but opaque.
SI-Core already produces *structured evidence* (jump logs, ethics traces, effect ledgers, goal vectors, rollback traces), so learning can be *structural* instead:
*Upgrade policies, compensators, SIL code, and goal structures — using runtime evidence.*
> Learning isn’t a model tweak.
> *It’s upgrading the structures that shape behavior.*
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Why It Matters:
• Makes improvement *localized and explainable* (what changed, where, and why)
• Keeps “self-improvement” *governable* (versioned deltas + review + CI/CD)
• Turns incidents/metric drift into *actionable patches*, not postmortem PDFs
• Scales to real ops: ethics policies, rollback plans, semantic compression, goal estimators
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What’s Inside:
• What “learning” means in SI-Core (and what changes vs. classic ML)
• The *Pattern-Learning-Bridge*: where it sits between runtime evidence and governed code
• Safety properties: PLB proposes *versioned deltas*, never edits production directly
• Validation pipeline: sandbox/simulation → conformance checks → golden diffs → rollout
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📖 Structured Intelligence Engineering Series
A non-normative, implementable design for “learning from failures” without sacrificing auditability.
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