Technology and VR

How we use artificial intelligence, machine learning, and big data for business

Big data

Big data

Big Data is a method of handling data that allows processing information in a distributed way. You can apply it to a huge dataset, such as the content of all web pages, or a small one — for example, a promotional leaflet.

Data center

In a sense, Big Data puts everything in its place. Imagine a grocery store where all the shelves are mixed up, with lettuce next to the cat food and frozen pizza in the dairy section. Big Data could help you systematize products by categories, learn their price and shelf life, and so on. What’s more, you could use it to find out who else purchases cat food and why they prefer Purina over Meow Mix.

Promoting sales in VR showrooms

We create real estate-focused VR showrooms that collect and systematize data about visitor movements. This allows us to split all customers into several categories — on-the-spot buyers, “window-shoppers”, and mortgage borrowers.

Our system observes the visitors and finds out specific behavioral scenarios for each category. For example, it might find out that on-the-spot buyers first go to the bathroom and then to the bedroom, while borrowers will examine the kitchen first. These scenarios can be used to give clues to the salesperson so that they know how to better structure their negotiations in order to close the deal.

Thus, Big Data helps improve sales and lets you know more about your clients. The same technology is used in auto VR showrooms. The system is agnostic to the data it feeds on, whether it’s the visitor’s movements, direction of sight, response to certain stimuli, and so on.

Augmented reality

Thus, Big Data helps improve sales and lets you know more about your clients. The same technology is used in auto VR showrooms. The system is agnostic to the data it feeds on, whether it’s the visitor’s movements, direction of sight, response to certain stimuli, and so on.

Machine Learning / Artificial Intelligence


Machine Learning (ML) is a process of “teaching” a computer program to solve creative and intellectual tasks, make decisions, and derive conclusions. It uses a huge variety of examples to allow the program to find patterns in the data. Then it uses them to make predictions on new data in similar conditions.

Say, you are looking for The Shining on a streaming website, and it suggests Something’s Gotta Give. Why? Before you, a million users told the computer which movies they love the most. These were the examples that the machine analyzed for common features. And it found an interesting pattern in user behavior: people prefer watching movies featuring actors they know, hence the suggestion of another movie starring Jack Nicholson.

Skeleton extraction based on six points

Originally, the PolygonVR platform captured a player’s position using 37 passive sensors located on all body points that bend, extend, or otherwise move relative to each other. To make the VR suite more practical, we decided to switch to just 6 active sensors — two on the hands, two on the feet, one on the head and one on the gaming backpack.

Now we had to teach the computer to recognize the player’s movements based on a limited number of sensors. We achieved this using artificial neural networks and machine learning. We had people wear both active and passive sensors and asked them to play different games, move around, and make random movements for a while.

The neural network considered all possible variations in the relative locations of active and passive sensors. Then we removed the passive ones, and the network managed to build a player skeleton based on just six points.

Ticket inspector simulator

A high-profile railway company asked us to create a VR interface that would help them teach their ticket inspectors how to handle certain scenarios.

The system was heavily reliant on the player’s speech, so there were two tasks involved. The first, pretty common one, was converting speech to text. The second one, which we had to solve all on our own, was extracting the text’s meaning. The algorithm we wrote could compare the extracted semantic components with the original inputs. For example, it could match the words spoken by an inspector with the bureaucratese of a company policy prescribing their actions in crisis situations.

Machine Learning in the ArtVR project

In this project, we delved into the artworks of the famous Russian artists, Natalia Goncharova and Kazimir Malevich. Our goal was to let people paint still lifes in the style of these great painters, and achieving it was no walk in the park. Here’s why.

A player can use a huge variety of items to paint a still life. But how can we make it look like the work of Malevich? To make it possible, we trained two artificial neural networks, which started training each other along the way. The first network would try painting in Malevich’s style, while the second one would guess if a given picture was actually painted by Malevich or made by the first network.

With every iteration, each of the networks learned something new. One would make even more masterful paintings, the other one would reveal plagiarism with an even higher degree of precision.

Forecasting the behavior of complex systems in VR

We use VR to predict all kinds of situations depending on people’s actions. Here’s how it works. We create a huge artificial neural network that feeds on the data from all processes of a given environment — e.g., an oil refinery.

We try to capture absolutely all processes — from hallway cleaning to tanker refueling. Then we simulate an industrial accident — some process going out of control. The neural network begins to evaluate the behavior of all systems depending on the consequences of the accident.

The thing is, the neural network can generate an infinite number of variations in emergency situations. The actions of the people involved can be used to draw conclusions about the logic behind the processes and ways to optimize them.

This technology results in an incredibly effective simulator, one that you cannot get used or adapt to.