As artificial intelligence advances, the goal for modern tech companies is to build AI software that thinks for itself without human intervention.
Towards that end, Amazon Web Services just picked MXNet, as its favored deep-learning framework to facilitate that work, according to a blog post Tuesday by Amazon chief technology officer Werner Vogels.
Deep learning, as detailed in Coins2Day earlier this year, is a subset of AI that involves the use of software known as neural networks. Within this realm, software learns by churning through vast reams of data with the help of algorithms—not human programmers—to sort it out.
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Vogels said AWS will provide software code, documentation, and invest in the development of MXnet and the ecosystem of companies supporting it. While he noted that the company has supported other deep learning frameworks—including Caffe, CNTK, TensorFlow, and Torch—it seems clear now that they will now take a back seat to MXNet.
Alex Smola, the machine learning expert AWS hired out of Carnegie Mellon University last year to lead its Cloud Machine Learning Platform effort, is known to be a big fan of MXnet. So that may be one reason for this choice.
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TensorFlow and CNTK frameworks were nurtured by Amazon rivals Google (GOOGL) and Microsoft (MSFT) respectively. Caffe comes out of the Berkeley Artificial Intelligence Research Lab at the University of California, Berkeley.
The MXNet news comes a week before the annual AWS re:Invent conference at which Amazon (AMZN) execs are expected to talk about artificial intelligence opportunities and how they plan to open up tools powering Amazon’s consumer-focused Alexa personal assistant to AWS developers.
With the goals of AI in mind, in order to succeed, Amazon and its rivals are all also pursuing the biggest possible sets of data.
So if you wonder what Amazon, Microsoft, Apple (AAPL), and Google get out of Alexa, Cortana, Siri, and Google Home, respectively, remember this: All the people who use these home appliances are providing these companies with extremely valuable data, which feed these future-thinking projects.
