CONCEPT
Inherited Understanding
Hofstadter's term for what large language models actually possess — <em>understanding absorbed statistically from the residue that genuine insight leaves in human text</em>, rather than understanding generated through the strange loop of self-referential cognition.
The machine has inherited the understanding of its trainers — not through instruction but through absorption. The texts from which it learned were written by people who understood the structures, and the statistical patterns in those texts reflect the structural understanding of their authors. The machine mines the residue of human understanding, the traces that genuine insight leaves in the texts it produced, and assembles those traces into configurations that preserve, to a remarkable degree, the structural relationships the original understanding established. The result is outputs that have the form of deep analogical insight — connecting domains, illuminating both, producing surprise and recognition in the human reader. But the process that generates them is fundamentally different from the process that generates analogical insight in human minds.
In The You On AI Field Guide
The machine is not perceiving structural similarity. It is retrieving statistical associations that happen to reflect structural similarity, because the training data was written by minds that perceived it. In
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