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Allen Institute for AI’s Incubator expands with $10M fund from high-profile VCs

The Allen Institute for AI (AI2) started its incubator up two years ago, helping launch companies like Xnor.ai, Blue Canoe, and WellSaidLabs. Their success has attracted funding from not just local Seattle VC outfit Madrona, but Sequoia, Kleiner Perkins, and Two Sigma as well, resulting in a new $10M fund that should help keep the lights on.

The AI2 Incubator, led by Jacob Colker since its inception in 2017, has focused on launching a handful of companies every year that in some way leverage a serious AI advantage. Blue Canoe, for instance, does natural language processing with a focus on accent modification; Xnor.ai is working on ultra-low-power implementations of machine learning algorithms, and was just acquired yesterday by Apple for a reported $200M.

“We think the next generation of so called AI-first companies are going to have to graduate into building long term, successful businesses that start with an AI edge,” said the program’s new managing director, Bryan Hale. “And the people who can help do this are the ones who have helped build iconic companies.”

Hence the involvement of household names (in the startup community anyhow) Sequoia and Kleiner Perkins, and Two Sigma from New York. Seattle-based Madrona also recently invested in AI2 company Lexion. It’s a pretty solid crowd to be running with, and as Colker pointed out, “they don’t often come together.”

“But also, they looked up into the northwest and said, what’s going on up there?” added Hale. Indeed, Seattle has over the last few years blossomed into a haven for AI research, with many major tech companies establishing or expanding satellite offices here at least partly concerned with the topic: Apple, Google, Nvidia, and Facebook among others, and of course local standbys Amazon, Microsoft, and Adobe.

Practically speaking the new fund will let the incubator continue on its current path, but with a bit more runway and potentially bigger investments in the startups it works with.

“We just have a lot more resources now to help our companies succeed,” said Colker. “Previously we were able to write up to about a $250,000 check, but now we can write up to maybe $800,000 per company. That means they have a lot more time to build out their team, aggregate training data, test their models, all these points that are important for a team to raise a bigger, better VC funding round.”

AI2 prides itself on its large staff of PhDs and open research strategy, publishing pretty much everything publicly in order to spur the field onwards. Access to these big brains, many of which have bred successful startups of their own, is no less a draw than the possibility of more general business mentorship and funding.

Colker said the incubator will continue to produce 3-5 startups per year, each one taking “about 12-18 months, from whiteboard to venture funding.” AI, he pointed out, often needs more time than a consumer app or even enterprise play, since it’s as much research as it is development. But so far the model seems to work quite well.

“There are very few places in the world where an entrepreneur can come to take advantage of the brain power of a hundred PhDs and support staff. We’ve got a new research center with 70 desks, we’ve got plenty of space for those teams to grow,” he said. “We’re incredibly well positioned to support the next wave of AI companies.”

Source: TechCrunch

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Apple buys edge-based AI startup Xnor.ai for a reported $200M

Xnor.ai, spun off in 2017 from the nonprofit Allen Institute for AI (AI2), has been acquired by Apple for about $200 million. A source close to the company corroborated a report this morning from GeekWire to that effect.

Apple confirmed the reports with its standard statement for this sort of quiet acquisition: “Apple buys smaller technology companies from time to time and we generally do not discuss our purpose or plans.” (I’ve asked for clarification just in case.)

Xnor.ai began as a process for making machine learning algorithms highly efficient — so efficient that they could run on even the lowest tier of hardware out there, things like embedded electronics in security cameras that use only a modicum of power. Yet using Xnor’s algorithms they could accomplish tasks like object recognition, which in other circumstances might require a powerful processor or connection to the cloud.

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

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

The company will likely move to Apple’s Seattle offices; GeekWire, visiting the Xnor.ai offices (in inclement 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 perfect sense when one thinks of how that company has been directing its efforts towards edge computing. With a chip dedicated to executing machine learning workflows in a variety of situations, Apple clearly intends for its devices to operate independent of the cloud for such tasks as facial recognition, natural language processing, and augmented reality. It’s as much for performance as privacy purposes.

Its camera software especially makes extensive use of machine learning algorithms for both capturing and processing images, a compute-heavy task that could potentially be made much lighter with the inclusion of Xnor’s economizing techniques. The future of photography is code, after all — so the more of it you can execute, and the less time and power it takes to do so, the better.

It could also indicate new forays in the smart home, toward which with HomePod Apple has made some tentative steps. But Xnor’s technology is highly adaptable and as such rather difficult to predict as far as what it enables for such a vast company as Apple.

Source: TechCrunch