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23.05.2017

Fifteen years ago a technology company shipped PCs, operating systems, or word-processing software. Today, technology companies are at the core of most of the world’s major industries. Look at Facebook, Uber or Airbnb, who all leveraged technology to drive fundamental change in the way things are done. Machine learning is at the start of the same journey. Today it is still an emerging technology, used by a small number of cutting-edge companies to improve their products with more accurate insights or better user experiences.

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13.04.2017

Last week, Google DeepMind announced that they had open sourced Sonnet, a software library that draws on DeepMind’s internal best practices for quickly building neural network modules in TensorFlow. This is a great resource, leveraging the collective experiences of some of their 250 highly skilled engineers, released to enable others to more effectively apply machine learning to their problems. In fact, over the last few years, the world’s biggest tech companies (including Google, Facebook, Microsoft, IBM, Baidu, Amazon and more) and university research labs have open sourced at least 2.5 million lines of machine learning platform code (see table below), which equates to over 650 man years or $80m in development costs. These toolkits are now freely available online, and many such as TensorFlow and Paddle are accompanied by significant volumes of training and example materials. As such, they can be viewed as an incredible initial investment into any company looking to use machine learning as an enabling technology for their product.

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This post originally appeared on Medium,  If you look at the six most valuable public companies in the world today, you will notice something striking. Five of them are the tech companies Apple, Alphabet, Microsoft, Amazon and Facebook, founded on the West Coast of the US between 1975 and 2004, three of whom who have achieved this ‘most valuable’ status in the last five years.

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