Amazon Go – Computational artefacts and sociotechnical systems

What we lack in knowledge, we make up for in data.

This sentence is so powerful, as more and more we see the significance of "being surrounded" by data. The data generated by all our computerized machines and services was once a by-product of digital technology, and computer scientists have done a lot of research on data-bases to efficiently store and manipulate large amounts of data. Sometime in the last two decades, all this #data became a resource; now, more data means more #information that can be stored in order to use #algorithms for pattern recognition and predictions. When we start to ask ourselves what can be done with this much information and data, then data starts to drive the operation; it is not the programmers anymore but the data itself that defines what to do next.

Case study: #AmazonGo

Let’s take a look at Amazon Go, another service of Amazon that allows the customers to try another shopping experience with no lines and no check out. Of course this has a drastic impact on the economy itself, and the number of workers that are needed to run a store. And this is where the debate starts when it comes to using #technology. At one point of view, we are making our shopping experience easier and we’re saving time. On another note, we’re cutting the number of workers that would run the store. For the purposes of this post, I’d like to expand on the technology that is used to make this shopping experience possible. Whether I like this shopping experience or not, we’ll have to see, since I haven’t tried it myself.

Amazon Go requires a store to be outfitted with machine vision, deep-learning algorithms, and an array of cameras and sensors to watch a customer’s every move. These sensors and cameras  look at what every item is, and when it’s been picked up and put back, so it can charge a shopper’s account.

Before you enter the store, you have to download the Amazon Go app and log in with your amazon account. Once you have that, you use the app, you scan the code and that’s how you have access in the store. Each of the shelves has sensors that track the weight associated with a product, and the cameras also feed the information on which item has been picked up. The items that you pick up and put in your bag are also being tracked on a “virtual cart” which is associated with your amazon account when you entered the store. Only when you leave the store, a receipt is emailed to you and your account is charged.

A lot of the “How’s” and what exact algorithms are used to make this experience possible are not publicized. But there is information hypothesizing and trying to understand how everything works.

An article on Wired magazine, explains that  all the cameras that are placed everywhere around the stores, on shelves, and above aisles, don’t use facial recognition technology, but instead computer vision. Think of it as a network of cameras that allows the software to see and determine what that object is, and also keep track when items get picked up from the shelves. This network of cameras also determines one customer from another, so the right customer is charged with the things that they bought. Behind the computer vision is the deep learning, where the systems are basically advanced pattern recognition and allow for the system to draw conclusions from vast datasets.

The main theory underlying machine learning comes from statistics, where going from particular observations to general descriptions is called inference and learning is called estimation. Classification is called discriminant analysis in statistics.

“Machine learning, and prediction, is possible because the world has regularities. Things in the world change smoothly. We are not “beamed” from point A to point B, but we need to pass through a sequence of intermediate locations”. (Alpaydin)

So, once you try to “de-blackbox” terms such as machine learning, you understand that at the basis of it lies statistics and statistical analysis and math models that have been used in may different fields, but more recently these became hot topics in the field of computer science and information science.

As Deborah G. Johnson and Mario Verdicchio suggest on their research, a critically important ethical issue facing the #AI research community has to do with how AI research and AI products are responsibly conceptualized and presented to the public. They argue that most of the issues relating to AI can be tackled by distinguishing AI computational artefacts and AI sociotechnical systems, which include computational #artefacts.

We need to keep in mind that as more new technologies are being used, it is the human actor that makes these technologies present and not the computational artefacts in them, or what media or businesses use as “buzz words” from a profit perspective.

Have you ever been inside a GO store? What was your experience like?