  In this paper the problem of {\em learning} appropriate domain-specific bias
is addressed. It is shown that this can be achieved by learning many related
tasks from the same domain, and a theorem is given bounding the number tasks
that must be learnt. A corollary of the theorem is that if the tasks are known
to possess a common {\em internal representation} or {\em preprocessing} then
the number of examples required per task for good generalisation when learning
$n$ tasks simultaneously scales like $O(a + \frac{b}{n})$, where $O(a)$ is a
bound on the minimum number of examples required to learn a single task, and
$O(a + b)$ is a bound on the number of examples required to learn each task
independently. An experiment providing strong qualitative support for the
theoretical results is reported.
