  With few exceptions, the field of Machine Learning (ML) research has largely
ignored the browser as a computational engine. Beyond an educational resource
for ML, the browser has vast potential to not only improve the state-of-the-art
in ML research, but also, inexpensively and on a massive scale, to bring
sophisticated ML learning and prediction to the public at large. This paper
introduces MLitB, a prototype ML framework written entirely in JavaScript,
capable of performing large-scale distributed computing with heterogeneous
classes of devices. The development of MLitB has been driven by several
underlying objectives whose aim is to make ML learning and usage ubiquitous (by
using ubiquitous compute devices), cheap and effortlessly distributed, and
collaborative. This is achieved by allowing every internet capable device to
run training algorithms and predictive models with no software installation and
by saving models in universally readable formats. Our prototype library is
capable of training deep neural networks with synchronized, distributed
stochastic gradient descent. MLitB offers several important opportunities for
novel ML research, including: development of distributed learning algorithms,
advancement of web GPU algorithms, novel field and mobile applications, privacy
preserving computing, and green grid-computing. MLitB is available as open
source software.
