trailThe indifference principle gives substantial reason to worry that future AI systems will become schemers.
The counting argument· for
Indifference over training-compatible goals reliably predicts the goals acquired by SGD-trained AI systems.
atomic
The argument from the failed overfitting prediction and SGD's inductive bias· against
The counting argument for AI scheming is relevantly analogous to a neural-network overfitting prediction that failed empirically.
bedrock · value premise
SGD does not sample uniformly at random from representable functions that perform well on the training data.
atomic
Counting arguments for AI scheming misuse the principle of indifference.
Naive function-counting arguments predict neural-network overfitting, contrary to observed generalization.
Counting arguments for AI scheming remain plausible under parameter-space-volume or simplicity-weighted priors.
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claim · evaluativeA judgment of worth or quality against some standard: good, fair, effective.constitution →claim page ↗︎
The indifference principle gives substantial reason to worry that future AI systems will become schemers.
⇄InconclusiveThe evidence and argument have been examined and do not come down on either side.constitution →
Nothing in the graph builds on this claim yet.
this rests on ↓
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