Hydro-Québec turns to drones, AI and robots to keep workers safe - Canadian Occupational Safety
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safetyOffline Constrained RLHF with Multiple Preference Oracles
arXiv:2604.00200v1 Announce Type: new Abstract: We study offline constrained reinforcement learning from human feedback with multiple preference oracles. Motivated by applications that trade off performance with safety or fairness, we aim to maximize target population utility subject to a minimum protected group welfare constraint. From pairwise comparisons collected under a reference policy, we estimate oracle-specific rewards via maximum likelihood and analyze how statistical uncertainty propagates through the dual program. We cast the constrained objective as a KL-regularized Lagrangian whose primal optimizer is a Gibbs policy, reducing learning to a convex dual problem. We propose a dual-only algorithm that ensures high-probability constraint satisfaction and provide the first finite-s
When smart AI gets too smart: Key insights from Vector’s 2025 ML Security & Privacy Workshop
Vector Institute’s 2025 Machine Learning Security & Privacy Workshop revealed critical AI safety breakthroughs and concerning vulnerabilities in current machine learning (ML) security methods. This comprehensive analysis covers the latest […] The post When smart AI gets too smart: Key insights from Vector’s 2025 ML Security & Privacy Workshop appeared first on Vector Institute for Artificial Intelligence .
How MAGA learned to love AI safety - Transformer | Substack
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