Stable Diffusion vs. Safe Stable Diffusion
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Javad Taghia PRO
telcom
AI & ML interests
text-to-image, image-to-image
Unlearning, training models, model evaluation and safety/alignment benchmarking, alumni UNSW (PhD)
Recent Activity
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3 days ago
✅ New Article: *PoC Architecture for Education & Developmental Support*
Title:
🎓 Building an SI-Core Wrapped Learning Companion - PoC architecture for education and developmental support
🔗 https://huggingface.co/blog/kanaria007/poc-architecture-for-education-development-support
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Summary:
Most “AI tutors” are built as *LLM-first* systems. This article flips the default:
* The LLM is treated as an *untrusted proposal engine*
* *SI-Core owns* observation, consent, ethics, memory, and rollback
* Teachers and guardians get *real oversight*, not just chat transcripts
Scoped intentionally to *one subject × a small cohort (10–30 learners)*, this is a PoC you can actually ship—and audit.
> Don’t ask: “Can an AI replace teachers?”
> Prove: “Can we make an AI companion *safe, explainable, and governable* for real learners?”
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Why It Matters (for AI on real stacks):
• *Consent & accommodations* are first-class (especially for minors / neurodivergent learners)
• *Ethics decisions are logged* (ALLOW / DENY / ESCALATE) with traceable reasoning
• “*Why this?*” explanations are built in for learners—and deeper inspection for adults
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What’s Inside:
• A minimal reference architecture (frontend → SI-Gate → ethics/memory/logging → LLM APIs)
• Non-negotiables for the pilot (SI-wrapped LLM, Effect Ledger, ethics overlay, dashboards)
• Failure modes + safe-mode behavior
• Implementation checklist + rough effort/cost ballparks (kept explicitly non-normative)
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📖 Structured Intelligence Engineering Series
A deployable pattern for taking today’s LLM tutor ideas and making them *auditable, overrideable, and rollback-safe*.
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