Myth ledger: "LeCun's 1988 paper used hand-designed convolutional kernels" — the 1988 hand-designed-kernel ZIP-code paper was Denker et al., not LeCun

The circulating claim compresses two Bell Labs ZIP-code papers into one "LeCun 1988 → 1989" story. The 1988 paper actually invoked for hand-designed kernels is "Neural Network Recognizer for Hand-Written Zip Code Digits," by J. S. Denker, W. R. Gardner, H. P. Graf, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel, H. S. Baird, and I. Guyon (Advances in NIPS 1, 1988) — Yann LeCun is not among the authors. LeCun is first author only on the 1989 Neural Computation paper "Backpropagation Applied to Handwritten Zip Code Recognition" (claim-lecun-1989-first-practical-recognition). Several names overlap (Denker, Henderson, Howard, Hubbard, Jackel) — the same group — which is how retellings slide "Denker et al. 1988" into "LeCun 1988."

Status: contested — the authorship half is wrong, the mechanism half is right. Read directly, the 1988 Denker paper does use hand-designed feature detectors: its 49 "feature extractor templates" (7×7 pixels) are explicitly designed by the authors — "Feature b is designed to detect the right-hand end of (approximately) horizontal strokes… the image must be able to touch the 'should be ON' pixels… without touching the surrounding horseshoe-shaped collection of 'must be OFF' pixels" — a hand-crafted detector, not a learned kernel. So the field lore is right that the 1988 system's feature stage was hand-designed and the 1989 system learned its kernels by backpropagation; it is wrong only in attaching LeCun's name to the 1988 paper.

This is the same priority-slide the vault tracks for claim-fukushima-1979-neocognitron-first-cnn — a group result migrating to the one remembered name. See moc-backpropagation-origins.

A wider pattern this transition instantiates. The 1988 hand-designed → 1989 backprop-learned kernel shift is a two-year-early case of what Rich Sutton's 2019 essay "The Bitter Lesson" would later name as a 70-year AI pattern — hand-engineered human knowledge wins short-term, then loses to general methods that leverage computation — and Sutton cites vision (hand-designed edges/SIFT features vs. learned convolution) as one of his four examples. That essay is also the hinge to observation-population-scaled-improvement-hits-a-sublinear-brake-across-domains: Sutton says such methods "scale arbitrarily," but neural-scaling-law exponents (Kaplan et al. 2020, α≈0.05–0.095) show the scaling that wins is real but logarithmically diminishing — the same sub-linear brake that note documents for evolution and idea-production. See 2026-07-27-hop-bitter-lesson-scaling-brake for the full chain — promoted 2026-07-28 as claim-sutton-2019-bitter-lesson-names-pattern-silent-on-rate and claim-kaplan-2020-scaling-law-exponents-are-small-diminishing-returns.