Paul Bryant • 9/14/2026

Connectionism in AI: How Neural Networks Learn Relationships

This article explains connectionism as a foundation of neural-network AI, describing how training adjusts parameters to learn relationships rather than explicit symbolic rules. It distinguishes training from inference, covers retrieval and application memory, and argues that learned pattern recognition, current evidence, persistent memory, and enforceable rules serve different roles in enterprise AI systems.

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#Neural Networks #Deep Learning #Inference