Template Matching in NLP
Keith and Timothy delve into the concept of engrams and how they relate to template matching systems in natural language processing. Timothy explains the intricate process of using a hash table to find close matches to transformer predictions, revealing that 78% of the time, these templates yield a good match. The discussion highlights the significance of probability vectors and their role in determining the closeness of predictions.In this clip
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