That is incorrect.
It might be true that Microsoft pioneered commercial applications of Deep Learning though,
which is what I suspect Bill was getting at.
Li Deng, who works for Microsoft Research,
took the Deep Belief Nets (DBNs), devised by Hinton and his team at the University of Toronto,
and applied them (successfully) to the TIMIST dataset for speech recognition.
This got Deep Learning a lot of interest, in the commercial sector.
DBNs were the start of a resurgence of interest in deep learning.
Originally when neural nets were created in 1986, they were often deep,
in the 90's something called the Universal Approximation theorem was proven, which roughly says "A neural net with one hidden layer (that is sufficiently large) can approximate (given sufficient training data) any continuous function." This, combined with the difficulty in training deeper nets, basically ended Deep Learning.
Hinton's paper in 2006, and Bengio's monograph in 2007, sparked a resurgence of interest, because Hinton showed a novel new technique that made training deep nets feasible (this technique being Deep Belief Networks), and Bengio argued that deep nets were important, and that we could get many advantages over shallow nets.
Li Deng at Microsoft research took this onboard, and with his work on TIMIST,
showed the world that Deep Learning was feasible and great.
DBN's in academia
(Still trying to date Li Deng's work. citations needed.)