Just-in-Time Ontologies: What Cheap Intelligence Means for Data — Hrishi Olickel, Southbridge AI

This talk addresses the limitations of universal schemas and confident agents in data transformation for AI. It proposes a 'just-in-time ontology' approach where transformations are built when a specific question is known, preserving context and evidence. The talk uses an Ohio campaign finance entity resolution project as an example, detailing the separation of models for pipeline design, job execution, and system optimization. Key elements discussed include the importance of definitions, held-out cases, disagreement checks, and model priors. It also outlines four requirements for self-optimizing pipelines: stable execution surfaces, bounded budgets, reusable methods, and defined failure modes, advocating for small pilots and independently checked work segments to manage cost, speed, and accuracy trade-offs while maintaining evidence.