Despite the promise of brain-inspired machine learning, deep neural networks (DNN) have frustratingly failed to bridge the deceptively large gap between learning and memory. Here, we introduce a Perpetual Learning Machine; a new type of DNN that is capable of brain-like dynamic 'on the fly' learning because it exists in a self-supervised state of Perpetual Stochastic Gradient Descent. Thus, we provide the means to unify learning and memory within a machine learning framework. We also explore the elegant duality of abstraction and synthesis: the Yin and Yang of deep learning.
@article{arxiv.1509.00913,
title = {On-the-Fly Learning in a Perpetual Learning Machine},
author = {Andrew J. R. Simpson},
journal= {arXiv preprint arXiv:1509.00913},
year = {2015}
}