UPDATED 13:33 EDT / FEBRUARY 02 2017

EMERGING TECH

Xnor.io raises $2.6M to bring machine vision to every device

Artificial intelligence has reached a point where self-driving cars can recognize and avoid potential dangers better than humans in some cases. But doing so requires a great of deal of processing power, a barrier that Seattle’s Xnor.io Inc. wants to remove.

The startup today closed a $2.4 million seed investment to make machine vision technology more widely accessible. According to its funding announcement, the round was provided by Madrona Venture Group and the Allen Institute for Artificial Intelligence, the research organization where Xnor.io’s co-founders got their start. Celebrated computer scientist Oren Etzioni, who heads the foundation, is joining the board of directors as part of the move.

Xnor.io will use the capital to start commercalizing the technology that its team created while working at AI2. In a January profile, TechCrunch’s Devin Coldewey detailed that how the startup’s software takes a more efficient approach to object recognition than traditional machine vision systems. The software abstracts the complex calculations required for a computer to identify items into relatively simple binary operations that processors can handle much more easily.

Some data about the target object is lost during the conversation process, but Xnor.io claims that its technology is still capable of making deducations with an acceptable level of accuracy. It’s a trade-off that enables the software to operate potentially “several orders of magnitude” more efficiently than frameworks designed to work  in processor-packed data centers. And more efficiency in turn means lower hardware requirements.

According to Xnor.io, its algorithms require so little computational power that they can perform near-real-time object recognition on a simple smartphone. The software works with much simpler devices, too, including the Raspberry Pi miniature computer.

In the future, Xnor.io envisions its technology being used to let handets and other connected devices identify objects on their own instead of relying on a remote data center for processing as is the norm today. Eliminating the dependency on a back-end analytics environment can reduce operating costs while avoiding the need for end-points to have steady Internet connectivity, thus increasing their reliability. The latter should be particularly useful in industrial environments where access to public carrier infrastructure is often sporadic at best.

Lastly, the fact that Xnor.io’s software doesn’t require any data to be transmitted over the web is also a big boon for consumer services, which need to take user privacy into account. The startup plans to work with “developers, chip designers and companies developing AI applications” to explore the use cases for its technology.

Image via Pixabay

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