{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Leadership for the Physical AI Age","title":"EP 16: Teaching Robots to Understand Risk","html":"","width":"100%","height":180,"duration":2757,"description":"What does it take to teach autonomous physical systems how to quantify and respect human risk? In this episode of Leadership for the Physical AI Age, host Titto Thomas sits down with Sarah Huth—former Australian Defence Force (ADF) intelligence specialist turned heavy-industry safety and risk practitioner.\n\nDrawing from her background in defense intelligence and frontline experience across Australian mining, oil, and gas, Sarah breaks down why workplace safety has remained largely reactive and weighed down by administrative overhead. Together, Titto and Sarah explore the shift from static checklists to proactive risk intelligence: how to train AI agents to bridge the \"say-do gap,\" unify isolated data across operations and maintenance (CAMS), mitigate human cognitive bias and normalization of deviance, and apply Vision Language Models (VLMs) directly to field safety without crossing into invasive surveillance.\n\nKey Discussion Highlights\nThreats vs. Hazards: Applying enterprise risk management principles and battle-space environmental control from the military to heavy industrial worksites.\n\nThe \"Say-Do Gap\": Why written safety procedures fail on the ground due to human fatigue, distraction, peer pressure, and procedural drift.\n\nUnifying Siloed Systems: Connecting computerized asset management systems (CAMS), daily operations logs, and safety reports into a single, cohesive organism.\n\nStabilizing Risk Tolerance: How AI can standardize Risk Analysis Matrices (RAM), calibrate frontline hazard inputs, and surface weak signals before incidents happen.\n\nIndustry 5.0 vs. 6.0: The practical realities of keeping humans in the loop alongside autonomous mobile machinery and clustered worksites.\n\nVision Language Models (VLMs) & Governance: Using computer vision for sentiment mapping, situational awareness, and automated compliance tasks—such as performing chemical inventory stocktakes directly from a photo.\n\nSafety vs. Privacy: Balancing on-site monitoring and vehicle cameras...","thumbnail_url":"https://img.transistorcdn.com/Ihrz8HSnPRaMTVxRNvisLmWsNDNh5xUG7H6qCEdVfDQ/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS81MTgw/ZGIxM2UxOTE2ZGRl/OGFhMGE5Y2JhOTFh/ZGUwOC53ZWJw.webp","thumbnail_width":300,"thumbnail_height":300}