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pascalmusabyimana

pascal-maker
DaveEds's profile picture davidberenstein1957's profile picture Mi6paulino's profile picture
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https://pascal-maker.github.io/developedbypascalmusabyimana/

AI & ML interests

computer vision, nlp , machine learning and deeplearning

Recent Activity

liked a model about 13 hours ago
mlx-community/GLM-4.7-REAP-50-mxfp4
reacted to kanaria007's post with 👀 7 days ago
✅ 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 --- 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.* --- 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 --- 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 --- 📖 Structured Intelligence Engineering Series A non-normative, implementable design for “learning from failures” without sacrificing auditability.
reacted to MikeDoes's post with 👀 8 days ago
What if an AI agent could be tricked into stealing your data, just by reading a tool's description? A new paper reports it's possible. The "Attractive Metadata Attack" paper details this stealthy new threat. To measure the real-world impact of their attack, the researchers needed a source of sensitive data for the agent to leak. We're proud that the AI4Privacy corpus was used to create the synthetic user profiles containing standardized PII for their experiments. This is a perfect win-win. Our open-source data helped researchers Kanghua Mo, 龙昱丞, Zhihao Li from Guangzhou University and The Hong Kong Polytechnic University to not just demonstrate a new attack, but also quantify its potential for harm. This data-driven evidence is what pushes the community to build better, execution-level defenses for AI agents. 🔗 Check out their paper to see how easily an agent's trust in tool metadata could be exploited: https://arxiv.org/pdf/2508.02110 #OpenSource #DataPrivacy #LLM #Anonymization #AIsecurity #HuggingFace #Ai4Privacy #Worldslargestopensourceprivacymaskingdataset
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