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That Vicarious Funding Also Included Levie, Altman, Khosla and Others

The broad participation in the round reflects the growing interest in AI.

Vladgrin / Thinkstock

The Wall Street Journal reported this morning that artificial intelligence company Vicarious FPC raised a $40 million round led by Formation 8, with Facebook CEO Mark Zuckerbeg, Tesla CEO Elon Musk and Ashton Kutcher, who once played a CEO, participating, too.

Re/code confirmed all of that except for Musk’s investment. Tesla, Vicarious and a few others declined to comment on that.

But we also heard there were many other participants that didn’t make the Journal, including: Box CEO Aaron Levie, Y Combinator’s Sam Altman, Braintree founder Bryan Johnson, Khosla Ventures, Good Ventures Foundation, Felicis Ventures, Initialized Capital, Open Field Capital, Zarco Investment Group and Metaplanet Holdings. Founders Fund, which led the company’s seed round, also came back for more.

A Facebook spokesman said Zuckerberg made the investment personally, not on behalf of the company. Vicarious is developing “machine learning software based on the computational principles of the human brain.”

The broad participation in the round is the latest example of the growing interest in AI and its promising offshoot known as deep learning. Earlier this year, Google bought DeepMind for $400 million, and companies as varied as Google, Facebook, Baidu, IBM, Microsoft and Qualcomm have been battling for the limited talent in the space.

As Re/code explained in an earlier piece:

Deep learning is a form of machine learning in which researchers attempt to train computer algorithms to spot meaningful patterns by showing them lots of data, rather than trying to program in every rule about the world. Taking inspiration from the way neurons work in the human brain, deep learning uses layers of algorithms that successively recognize increasingly complex features — going from, say, edges to circles to an eye in an image.

Notably, these techniques have allowed researchers to train algorithms using unstructured data, where features haven’t been laboriously labeled by human beings ahead of time. It’s not a new concept, but recent refinements have resulted in significant advances over traditional AI approaches.

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