Connectionism

AI Decision Controls: From Learned Patterns to Authorized Actions

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TL;DR AI decision controls separate what a model proposes from what an application may execute. A useful recommendation still needs appropriate evidence, current authorization, bounded resource use, and outcome verification. Enforce those requirements outside the model, bind approvals to specific operations, and preserve uncertain execution outcomes instead of treating every timeout as a failed action. … Explore: AI Decision Controls: From Learned Patterns to Authorized…

AI Memory Architecture: Context, RAG, and Persistent State

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TL;DR AI memory architecture determines what persists, how it reaches the model, and whether it is still appropriate to use. Model parameters, request context, external records, and workflow state serve different purposes. A larger context window or a vector database does not establish provenance, current accuracy, or authorization. Design both the write and read paths, … Explore: AI Memory Architecture: Context, RAG, and Persistent State

AI Generalization: Did Your Model Learn the Right Pattern?

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TL;DR AI generalization is not demonstrated by reproducing familiar answers on unfamiliar ticket numbers. A useful model should preserve its recommendation when irrelevant details change, reconsider it when relevant evidence changes, and identify uncertainty when evidence is missing. Enterprise evaluation needs to distinguish those behaviors from shortcut learning, data leakage, and agreement with questionable historical … Explore: AI Generalization: Did Your Model Learn the Right…