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If you have an experimental error that is somewhat normally distributed around the mean, the the AI should, with enough examples, learn what the rules are that
by carlmr 6y ago
If you have an experimental error that is somewhat normally distributed around the mean, the the AI should, with enough examples, learn what the rules are that are closest to the mean. Because it will minimize the sum of errors.
So i do think the results could be more accurate than measurement.
- willj 6y agoI don’t think we can assume the errors are normally distributed. It’s possible researchers are biased in a particular “direction”, away from 0 on all dimensions of this problem.
- bollu 6y agoThat's fine. It's still a normal distribution with a different mean. The Gaussian is characterized by having only the first two moments: mean and variance.