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The method behind Google's machine learning madness

Matt Asay | May 20, 2016
When Google open-sourced its TensorFlow and SyntaxNet machine learning technology, it gave away its most precious intellectual property. Here's why that was smart

Some developers will cavil at this inherent control Google retains over the code, but that's a bit churlish. After all, no one is forcing developers to run their code on Google's cloud. Even if they don't, Google wins if developers rally to its machine learning projects and help to improve them.

The model for this is Kubernetes, an open source implementation of Google's internal management systems. In 2015, Google gave control of Kubernetes to the Cloud Native Computing Foundation, joined by IBM, AT&T, Huawei, and a range of others. Google may employ lots of incredibly smart engineers, but it doesn't employ all of them. By open-sourcing its code, Google gets access to the industry's brightest minds, without giving up its crown jewels.

Those crown jewels, as mentioned, really have little to do with software and everything to do with running the software at scale, all while amassing and putting to use mountains of data. It's why we can correctly say that Google is both open-sourcing its machine learning code and that machine learning will be its secret sauce.

The two aren't contradictory. They're complementary.

Source: Infoworld 

 

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