Google Open-Sourcing TensorFlow

[Guest]

When Google open sourced its artificial intelligence engine last week—freely sharing the code with the world at large Lukas Biewald didn’t see it as a triumph of the free software movement. He saw it as a triumph of data.

 

That’s how you’d expect him to see it. He’s the CEO of the San Francisco startup CrowdFlower, which helps online companies like Twitter juggle massive amounts of data. But he also spent time at the Stanford AI Lab, and knows artificial intelligence. And his point is solid.

 

In open sourcing the TensorFlow AI engine, Biewald says, Google showed, when it comes to AI, the real value lies not in the software or the algorithms but in the data needed to make it all smarter. Google is giving away the other stuff, but keeping the data.

 

“As companies become more data-driven, they feel more comfortable open sourcing lots of [software]. They know they’re sitting on lots of proprietary data that nobody else has access to,” says Biewald, who also worked at Yahoo as a search engineer and helped bootstrap a notable search startup called Powerset, now owned by Microsoft. “What they’re not opening up is their data. They would never do that.”

 

The algorithms that drive these neural networks aren’t new. They date to the 1980s. What’s new is that, thanks to the Internet, their creators have the processing power and the enormous amounts of data to make these algorithms viable. To teach a system to recognize a cat, you need an awful lot of machines and an awful lot of cat photos.

 

After the rise of cloud computing, in which companies like Amazon and Microsoft rent access to the vast processing power of the net, we all have access to a vast arrays of machines. But the richest data sits inside massive companies like Google and Facebook. Billions of people use their services, which trade in a rich trove of information, from text to photos to videos to speech and beyond. Both companies are hard at work building powerful AI software. But their real competitive edge comes from having a vast quantity of high quality data they can use to teach this software to “think” more like a human.

 

''It's kinda hard for academics and startups to do really meaningful machine learning work because they don't have access to the same kind of datasets that a Google or an Apple would have.' - Lukas Biewald

 

Source: http://www.wired.com/2015/11/google-open-sourcing-tensorflow-shows-ais-future-is-data-not-code/

 

 

I believe that the future it's nice for Google to open-source some of their work to be able to allow researchers and engineers all around to help fix some of the flaws that are usually gazed upon.I really think that this is a great step in helping others have a chance to show their skill and help in developing machines.