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How Deep Learning Will Alter the Retail Space Posted on Nov 14 - 2017

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Artificial Intelligence has been a hot word across all industries lately. Think all the fuss around self-driving cars, Google’s updated Assistant and the general talks of how conversational interfaces are the future of tech.

Around 54 percent of retailers already use or plan to add artificial intelligence technology to their toolkit, with 20 percent planning to introduce some AI within the next 12 months, according to the latest report from SLI Systems.

The increased adoption of AI in retail can be specifically attributed to advances in the deep learning.

What is Deep Learning?

Deep learning is a specific machine learning approach to building and training neural networks. A neural network, in turn, is a system of hardware and/or software that is styled after the operations of neurons in the human brain.

In a nutshell, deep learning assumes that you will first “feed” your network with enough samples (data) so that it could make decisions about similar data based on what it “knows” so far.

For instance, a deep learning network can be used to differentiate different types of product – a dress and a pair of trousers. It could use differences in the light and dark areas of an image to establish how a dress looks. That’s the initial step. Next, it will take into account other factors such as the shape, angles, colors etc., to precisely understand how a dress look and learn to identify it out of all other garments.

Why Deep Learning Isn’t Mainstream in Retail Yet

However, the process of training a network isn’t that simple. All the steps described above run inside a black box and are controlled by the network’s memory – arrays of numbers (weights) that indicate how certain inputs are combined and recombined to obtain the result.

Dealing with an example like dress recognition requires creating larger arrays that can contain over 60 million numbers. Working with such larger data is complicated. View More


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