Databases support explicit projection of a sample space prior to evaluation, allowing logic to be restricted dynamically at query time. In contrast, most machine learning systems collapse restriction, representation, and inference during training, exposing only a fixed inference interface at runtime. This paper introduces a projection-aware inference model in which restriction is a first-class, parameterized operation orthogonal to learning. By separating projection from inference semantics, models regain query-like behavior without retraining, enabling precision analysis, anomaly detection, and individualized reasoning that are difficult or impossible under traditional population-trained paradigms.  

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John Harby
CEO
Autonomic AI, LLC
Temecula CA
9513835000
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