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kanaria007
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posted an update about 20 hours ago
✅ New Article: *From Effect Ledger to Goal-Aware Training Data* Title: 🧾 From Effect Ledger to Goal-Aware Training Data — How SI-Core turns runtime experience into safer models 🔗 https://huggingface.co/blog/kanaria007/effect-ledger-to-training --- *Summary:* Most ML pipelines treat “training data” as an opaque byproduct of logs + ETL. SI-Core flips that: runtime experience is already structured (observations, decisions, effects, goals, ethics traces), so learning can be *goal-aware by construction* — and *auditable end-to-end*. > Models don’t just learn from data. > They learn from *traceable decisions with consequences.* --- *Why It Matters:* • *Provable lineage:* answer “what did this model learn from?” with ledger-backed evidence • *Safer learning loops:* labels come from realized goal outcomes (not ad-hoc annotation) • *Governance-native training:* ethics and risk are first-class signals, not bolt-ons • *Redaction-compatible ML:* erasure/remediation ties back to the same ledger fabric • *Real deployment gates:* rollout is constrained by system metrics, not leaderboard scores --- *What’s Inside:* • A clean mental model: *event / episode / aggregate* layers for SI-native learning data • How to define training tasks in *goal + horizon* terms (and derive labels from GCS/rollback signals) • A practical ETL sketch: extract → join → label → filter → splits (with SI-native filters like OCR) • Continual/online learning patterns with *automatic rollback on degradation* • Distributed learning with *federation + DP*, bounded by governance scopes • Lineage + audit templates: from a trained model *back to the exact ledger slices* it used --- 📖 Structured Intelligence Engineering Series A practical bridge from “structured runtime” to *goal-aware training* you can explain, govern, and repair.
published an article about 20 hours ago
From Effect Ledger to Goal-Aware Training Data
posted an update 2 days ago
✅ New Article: *Proving Your SIL Code Behaves* Title: 🧪 Proving Your SIL Code Behaves - Property Tests and Structured Checks for SIL / SIR / sirrev 🔗 https://huggingface.co/blog/kanaria007/proving-your-sil-code --- Summary: SIL is meant to make decision logic *auditable* — but you still need a practical way to say: *“this code still behaves, and we can show you why.”* This mini-guide is a *non-normative* “Hello, Structured Testing” playbook for SIL: turn domain rules into QuickCheck-style properties, wire SIR/*sirrev* into structural checks, and run it all in CI like SIL patches are potentially dangerous code. > Tests aren’t a vibe. > *They’re part of the structured stack.* --- Why It Matters: • Makes “trustworthy decision code” achievable for normal engineers (without turning everyone into a formal methods specialist). • Separates what to test at each layer (*SIL → SIR → sirrev*) so you can catch semantic drift, compiler regressions, and structural weirdness early. • Connects local tests to global system signals (e.g., determinism / consistency / coverage), so “testing” feeds the same measurement language as the rest of the SI stack. --- What’s Inside: *Foundation stack:* • Mental model: *SIL → SIR → sirrev → metrics* (and why each needs different checks). *Practical recipes:* • Property tests for invariants (bounds, monotonicity, determinism). • Golden diffs for SIR (did the compiler preserve meaning?). • sirrev structural checks (no nondet in DET, effects guarded by CON, balanced frames). *Escalation ladder (when you need stronger guarantees):* • V1 property testing → V2 symbolic execution → V3 SMT → V4 theorem proving (and when to climb). 📖 Structured Intelligence Engineering Series
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