Human-in-the-Loop

Opening — Why this matters now Autonomous driving research does not stall because of missing models. It stalls because of missing labels. Every promising perception architecture eventually collides with the same bottleneck: the slow, expensive, and error-prone process of annotating multimodal driving data. LiDAR point clouds do not label themselves. Cameras do not politely blur faces for GDPR compliance. And human annotators, despite heroic patience, remain both costly and inconsistent at scale. ...

Opening — Why this matters now Explainable AI has reached an awkward phase of maturity. Everyone agrees that black boxes are unacceptable in high‑stakes settings—credit, churn, compliance, healthcare—but the tools designed to open those boxes often collapse under their own weight. Post‑hoc explainers scale beautifully and then promptly contradict themselves. Intrinsic approaches behave consistently, right up until you ask who is going to annotate explanations for millions of samples. ...

Opening — Why this matters now Vision models have become remarkably competent at recognizing things. Dogs, cars, traffic lights—no drama. The problem starts when we ask them to recognize judgment. Is this image unhealthy food? Is this visual clickbait? Is this borderline unsafe? These are not classification problems with clean edges; they are negotiations. And most existing pipelines pretend otherwise. ...

Cool Heads Prevail: Human-in-the-Loop AI for Smarter HVAC Careers Heating, ventilation, and air conditioning (HVAC) systems are often taken for granted—until they fail or run up a massive electricity bill. But in a world facing both climate urgency and rising energy costs, the traditional thermostat just won’t cut it. Enter a novel Human-in-the-Loop (HITL) AI framework that could reshape how HVAC engineers, facility managers, and energy analysts approach their craft. ...