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Amazon Investing In More Artificial Intelligence Machine Learning

26 August 2015, 12:40 pm EDT By Mark Lelinwalla Tech Times
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Amazon is expanding its machine learning AI corps in New York and overseas in Berlin, using it to zero in on sales and fraud predictions.  ( Sean Gallup | Getty Images News )

Amazon is a well-oiled $248 billion machine ... one that needs to be highly powered at all times.

According to the Wall Street Journal, Amazon.com is beefing up its artificial-intelligence. The world's largest retailer is reportedly expanding its machine learning, rolling out its platform for European developers. That's not it, either. Amazon is also hiring scientists to complement its researchers in New York and Berlin, who calibrate machines to carry out activities such as the prediction of sales as well as the prediction of fraud.

"Machine-learning software predicts what a customer is likely to do in the next five seconds or in the next five weeks," Ralf Herbrich, European Union director of machine learning at Amazon and managing director of the Amazon Development Center in Germany, told the Wall Street Journal. "It's pattern recognition at scale."

Amazon currently counts 150 software engineers and scientists in Berlin, specializing in big data and machine learning. In New York, Herbrich said scientists will zero in on demand forecasting and predicting how consumers will select products and how likely they are to click on a relating link or purchase an item.

(Photo : Sean Gallup | Getty Images News)

 

Before landing at Amazon, Herbrich had worked for Microsoft Research and Facebook, two tech giants that also have AI labs in New York.

Amazon had already been using machine learning to predict prices for millions of products and for recommendation forecasting within the search function. As an example, the Journal mentioned 300 shoes being sold in Moscow in 2014 and the following prediction of how many shoes will be sold there in 2015 automatically generated.

Amazon's prediction forecasting will reportedly key in on fashion apparel and shoes — markets that are hard to predict, even when using historical data as a base.

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