16-01-2020 5:47 am Published by Nederland.ai Leave your thoughts

Xnor.ai, spun in 2017 from the non-profit Allen Institute for AI (AI2), has been acquired by Apple for around $ 200 million. A source close to the company confirmed a GeekWire report this morning.

Apple confirmed the reports with its standard statement for these types of silent acquisitions: “Apple occasionally buys smaller technology companies and we generally do not discuss our purpose or our plans.” (I asked for clarification in case.)

Xnor.ai started as a process to make machine learning algorithms very efficient – so efficient that they could even run at the lowest level of hardware, things like built-in electronics in security cameras that use only a little bit of power. But with the help of Xnor 's algorithms they could perform tasks such as object recognition, which in other circumstances might require a powerful processor or connection to the cloud.

CEO Ali Farhadi and his founding team assembled and spun the company at AI2 just before the organization formally launched its incubator program. It increased $ 2.7M in early 2017 and $ 12M in 2018, both rounds led by the Madrona Venture Group of Seattle, and has steadily grown its local operations and areas of business.

The $ 200M purchase price is approximate only, the source indicated, but even if the final number was less by half that would be a great return for Madrona and other investors.

The company is likely to move to Apple's offices in Seattle; GeekWire, who visits the offices of Xnor.ai (in bad weather, no less), reported that a move was clearly underway. AI2 confirmed that Farhadi is no longer working there, but he will retain his faculty position at the University of Washington.

An acquisition by Apple makes a lot of sense when you consider how that company has focused its efforts on edge computing. With a chip intended to perform machine learning processes in different situations, Apple clearly intends to make its devices work independently of the cloud for tasks such as face recognition, natural language processing, and augmented reality. It is just as good for performance as it is for privacy.

The camera software mainly makes extensive use of machine learning algorithms for both capturing and processing images, a calculation-intensive task that can possibly be made much lighter by incorporating the saving techniques of Xnor. After all, the future of photography is code – so the more you can do from it, and the less time and power it takes to do this, the better.

It could also point to new forays in the smart house, which with HomePod Apple has taken some cautious steps. But Xnor's technology is highly adaptable and as such quite difficult to predict what makes it possible for such a large company as Apple.

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