  In a recent paper, we presented a new definition of influences in product
spaces of continuous distributions, and showed that analogues of the most
fundamental results on discrete influences, such as the KKL theorem, hold for
the new definition in Gaussian space. In this paper we prove Gaussian analogues
of two of the central applications of influences: Talagrand's lower bound on
the correlation of increasing subsets of the discrete cube, and the
Benjamini-Kalai-Schramm (BKS) noise sensitivity theorem. We then use the
Gaussian results to obtain analogues of Talagrand's bound for all discrete
probability spaces and to reestablish analogues of the BKS theorem for biased
two-point product spaces.
