Set release boundaries for the single-neuron trainer

Operational risks for the single-neuron logic-gate trainer center on hard-coding 'trained' weights without a verifiable training loop, plus the earlier failure mode (learning rate so large weights diverge, or claiming XOR success with one neuron). Safety lives in executable gates, allowlists, redaction, and a named owner—not in a warning paragraph under an unsafe function.

weights=[4.2,4.2]; bias=-1.9
assert all(abs(v)<20 for v in weights+[bias])
print('bounded parameters; binary inputs only')

Expected evidence: stage evidence for security ops. A failed assertion means stop, investigate, and do not publish the single-neuron trainer.

Name who can disable the feature, what data is retained, and how to roll back to the last known good artifact. Pin the reviewed configuration (versions, thresholds, allowlists) so “what shipped” is reconstructable for neural-nets-by-building.

Write the release blocker as a predicate, not a feeling: “Do not ship the single-neuron trainer if hard-coding 'trained' weights without a verifiable training loop.” Pair it with a passing control that shows the reviewed configuration still works for AND gate truth table. Name an owner and a rollback handle (git tag, docs_version, previous image).

Security pages must not paste the happy-path demo. If your gate code looks like the implementation page, replace it with a deny/allow check aimed at hard-coding 'trained' weights without a verifiable training loop.

State the data retention rule in one line (what is stored, for how long, who can read it). Then state the kill switch (env flag, config pin, or feature owner). The single-neuron trainer is not shippable without both, even when final predictions match gate table; loss decreases across epochs looks healthy.

At the safety and operations stage for neural-nets-by-building, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about AND gate truth table that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: untrained weight predictions on the gate table.

For this page specifically, success looks like an executable deny gate for the lab-specific threat while still centering the user decision to learn weights for a linearly separable gate by gradient steps you can print. If you cannot point to a file, command, or assertion that proves that for the single-neuron trainer, stay on this page instead of advancing.

Glossary: deep learning · Glossary: gradient descent

Previous · Next