Schrödinger’s Cat and AI: Plausible Is Not Proven
Separate a plausible AI answer from a verified claim and an authorized action. Use the Schrödinger’s cat analogy carefully, then apply evidence gates to infrastructure decisions.
AI evaluation
AI Generalization: Did Your Model Learn the Right Pattern?
Test whether an AI model responds to the evidence that matters. Use controlled incident variations to expose shortcuts, leakage, brittle recommendations, and failures hidden by aggregate scores.
Entropy in AI and Human Thinking: Why Certainty Is Not Accuracy
Distinguish physical, information-theoretic, and psychological uses of entropy. Learn why predictable AI responses and repeated human beliefs still need independent evidence and practical validation.
AI Context Governance: From Test Results to Production Controls
Turn context-evaluation findings into production controls. Preserve critical evidence through retrieval and summarization, assign ownership, and keep release evidence separate from runtime execution authority.
Rehearse Before You Automate: Why AI Agents Should Practice Before They Act
Rehearse agent behavior in controlled environments before granting production authority. Test realistic failures, policy conflicts, recovery, and repeated trials against the scope the agent will actually receive.
Feedback Is the Control Plane: Preventing AI Systems From Repeating the Wrong Behavior
Decide which production experiences may influence future AI behavior. Qualify observations, route corrections to the right layer, evaluate changes, and control their promotion into durable knowledge or policy.