We apply approximate matrix multiplication to artificial Neural Networks for image classification and to the robotics problem of Distributed Simultaneous Localization and Mapping increasing the speed of both applications by 15-20% while maintaining a 97% classification accuracy for NNs running on the MNIST dataset and keeping the average robot position error under 1 meter (vs 0.32 meters for the exact solution). However, both applications experience variance in their results.