MLitB: machine learning in the browser

With few exceptions, the field ofMachine 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.