Machine Learning for Real: Why Principles, Efficiency, and Ubiquity Matter
TimeWednesday, July 13th8:45am - 9:45am PDT
Location3008-3012, Level 3
DescriptionMarc Andreessen famously opined that “software is eating the world.” Recently, various people have suggested that AI is eating software. Deep learning seems to touch every discipline these days, but behind its startling magic tricks, it is surprisingly primitive. Most deep learning today requires vast data centers whose power consumption burdens an already overstressed planet. Add in the latency and loss of privacy, and it is clear that society would benefit from gadgets that do not require connection to the cloud to be “smart”. Even more concerning, however, is the strong dependence of today’s deep learning on folklore: on recipes and anecdotes, rather than scientific principles and explanatory mathematics. Instead, we can develop rigorous, scalable machine learning guided by information theory to create models that are predictive, power-efficient, and cost-effective.