Neural Networks Archives

Connectionism links training data through a weighted neural network to model output; training updates weights while inference uses the current context.

Connectionism in AI: How Neural Networks Learn Relationships

Published September 14, 2026 by Paul Bryant

Understand how neural networks learn relationships, then separate model training from retrieval, context, and application memory. Use those distinctions to make clearer enterprise AI architecture decisions.

Training incident records produce learned patterns that must be tested on new incidents by renaming irrelevant details, changing facts, and removing evidence.

AI Generalization: Did Your Model Learn the Right Pattern?

Published September 11, 2026 by Paul Bryant

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.

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