With few exceptions, the field ofMachine Learning (ML) research has largely ignored
\nthe browser as a computational engine. Beyond an educational resource for ML, the
\nbrowser has vast potential to not only improve the state-of-the-art in ML research,
\nbut also, inexpensively and on a massive scale, to bring sophisticated ML learning
\nand prediction to the public at large. This paper introduces MLitB, a prototype
\nML framework written entirely in Javascript, capable of performing large-scale
\ndistributed computing with heterogeneous classes of devices. The development
\nof MLitB has been driven by several underlying objectives whose aim is to make
\nML learning and usage ubiquitous (by using ubiquitous compute devices), cheap
\nand effortlessly distributed, and collaborative. This is achieved by allowing every
\ninternet capable device to run training algorithms and predictive models with no
\nsoftware installation and by saving models in universally readable formats. Our
\nprototype library is capable of training deep neural networks with synchronized,
\ndistributed stochastic gradient descent. MLitB offers several important opportunities
\nfor novel ML research, including: development of distributed learning algorithms,
\nadvancement of web GPU algorithms, novel field and mobile applications, privacy
\npreserving computing, and green grid-computing. MLitB is available as open source
\nsoftware.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 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