Humatics, an MIT spinout, is developing an indoor radar system that should give robots and other industrial systems the ability to track people's movements very precisely. This could make industrial systems significantly safer, make it possible to track worker performance in greater detail, and lead to more effective new forms of collaboration between people and machines. The technology might improve the efficiency of an industrial manufacturing line because workers could grab something a robot has finished working on without fear of being injured. Meanwhile, inside many warehouses and fulfillment centers such as those operated by Amazon, robots are increasingly helping people move items around more efficiently (see "Inside Amazon's Warehouse: Human-Robot Symbiosis").
A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). Feeding data into deep learning software to train it for a particular task is much more resource intensive than running the system afterwards, but that still takes significant oomph. Intel has slowed the pace at which it introduces generations of new chips with smaller, denser transistors (see "Moore's Law Is Dead. It also motivates the startups--and giants such as Google--creating new chips customized to power machine learning (see "Google Reveals a Powerful New AI Chip and Supercomputer").
The algorithms can also be biased due to the way they are trained, says Anil Jain, head of the biometrics research group at Michigan State University. To work, face matching software must first learn to recognize faces using training data, a set of images that gives the software information about how faces differ. In 2012, Jain and several colleagues used a set of mugshots from the Pinellas County Sheriff's Office in Florida to examine the performance of several commercially available face recognition systems, including ones from vendors that supply law enforcement agencies. Likewise, East Asian algorithms performed better on East Asian faces than on Caucasian ones.