Xnor.ai, In 2017, the Allen Institute for AI (AI2) nonprofit institute was acquired by Apple for approximately $ 200 million. A source close to the company corroborated a report this morning from GeekWire in that regard.
Apple He confirmed the reports with his standard declaration for this type of silent acquisition: “Apple buys smaller technology companies from time to time and we generally do not discuss our purpose or plans” (I have asked for clarifications just in case).
Xnor.ai began as a process to make machine learning algorithms highly efficient, so efficient that they could run even at the lowest level of hardware, things like electronic devices integrated into security cameras that use only a minimum of power. However, using 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 gathered the company on AI2 and developed it just before the organization formally launched its incubator program. He raised $ 2.7M in early 2017 and $ 12M in 2018, both rounds led by Seattle’s Madrona Venture Group, and has steadily increased its local operations and business areas.
The purchase price of $ 200 million is only approximate, the source said, but even if the final number were less than half, that would be a great return for Madrona and other investors.
The company will probably move to Apple offices in Seattle; GeekWire, visiting the offices of Xnor.ai (in adverse weather conditions, nothing less), reported that a movement was clearly on its way. AI2 confirmed that Farhadi no longer works there, but will maintain his teaching position at the University of Washington.
An acquisition by Apple makes a lot of sense when one thinks about how that company has been directing its efforts towards cutting-edge computing. With a chip dedicated to running machine learning workflows in a variety of situations, Apple clearly intends that its devices work independently of the cloud for tasks such as facial recognition, natural language processing and augmented reality. It is for both performance and privacy purposes.
The software of his camera especially uses machine learning algorithms for both capturing and processing images, a task of great computing capacity that could be made much lighter with the inclusion of Xnor economics techniques. The future of photography is the code, after all, so the more you can run, and the less time and power it takes to do it, the better.
It could also indicate new incursions into the smart home, towards which Apple HomePod has taken some tentative steps. But Xnor’s technology is highly adaptable and, as such, quite difficult to predict in terms of what it allows for a company as vast as Apple.