How is this better than using a typical smartphone?
Security and game cameras are a massively-unsolved problem, for instance. I'd like to capture footage of bears, coyotes, and other wildlife as it travels through my back yard, not to mention keeping an eye out for larger bipedal visitors. But it's almost impossible to convince the naive motion detection algorithms in my surveillance cameras not to respond to trees swaying back and forth in the wind, or to the resulting rapid movement of patches of dappled sunlight. Or to spiders crawling back and forth in front of the lens, building a web. Or to moths that seem to be attracted to the IR illuminator at dusk. Or to any number of other things that any human would instantly recognize as a false alert, but that are very difficult for software to reject without frequent mistakes in sensitivity, specificity or both.
It's hard to believe that anyone with an outdoor security camera hasn't had to deal with similar hassles. I'm sure there are other applications for a camera like this, but if I were an investor, I'd be very interested in the intersection of ML and security in general. I'm definitely interested as a homeowner.
Of course, neither would a camera that's made out of cardboard and runs on a Raspberry Pi. But for prototyping, this seems like it could offer a good start.
[1] http://www.tomshardware.com/news/movidius-fathom-neural-comp...