Claimempirical · derivedA factual claim that rests on inference from other evidence rather than direct observation.constitution →importance · minorImportance 0.40, from 0 to 1 · minor: narrow or largely settled, cheap to get right. Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →

TopicsStochastic Gradient Descent Machine Learning

SGD does not sample uniformly at random from representable functions that perform well on the training data.

Not yet assessed. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.

Decomposition

This claim has not been assessed yet. Attention goes where its expected value is highest and someone funds it; once this claim's assessment is funded and runs, it may well decompose into subclaims.

or create a grant for this whole area →

Provenance

Where this claim has been said, linked to its canonical form.

1 source · no origin · 1 untraced

1 Untraced

Counting arguments provide no evidence for AI doom Untraced · nothing upstream recorded

These results strongly suggest that SGD is not doing anything like sampling uniformly at random from the set of representable functions that do well on the training set.

Counting arguments provide no evidence for AI doomextraction 0.80

Wider networks usually generalize better, and never generalize worse, than narrow networks.

Cite this claim: a formal citation with its evidence attached

Contribute

Every judgment on this page is open to challenge. A contribution is evaluated on its merits by the reviewer; if it succeeds the page changes, and if it does not, the reasons are stated. Either way the exchange becomes part of the claim’s public record.

Created by extractor · Nov 1, 2026. Every judgment on this page is accompanied by a reasoning trace.