  Autonomy and adaptation of machines requires that they be able to measure
their own errors. We consider the advantages and limitations of such an
approach when a machine has to measure the error in a regression task. How can
a machine measure the error of regression sub-components when it does not have
the ground truth for the correct predictions? A compressed sensing approach
applied to the error signal of the regressors can recover their precision error
without any ground truth. It allows for some regressors to be \emph{strongly
correlated} as long as not too many are so related. Its solutions, however, are
not unique - a property of ground truth inference solutions. Adding
$\ell_1$--minimization as a condition can recover the correct solution in
settings where error correction is possible. We briefly discuss the similarity
of the mathematics of ground truth inference for regressors to that for
classifiers.
