How Unity Is Extending The Power Of Synthetic Data Beyond The Gaming Industry?

Unity is testing the limits of what synthetic data can accomplish. We’ll look at how the company is empowering clients in a variety of industries to accelerate innovation in this post.

Finding a balance between art and science in the gaming world is critical to achieving the desired result: fun. For more than 15 years, Unity has been developing and operating real-time 3D (RT3D) content for the gaming industry. Our patented technology connects gamers and game developers, allowing developers to create games that fans will like. Now, we’re leveraging artificial intelligence (AI), machine learning (ML), and synthetic data to assist creators in other industries to make better data-driven decisions (data that is derived from simulations based on real-world data).

Understanding the importance of synthetic data in a data-rich world;

Most businesses struggle to get the correct data into the right place when they first start using machine learning and artificial intelligence. A smaller business may find it challenging to generate or collect the amount of data required to make good projections. This is where synthetic data comes in, and it’s why Unity 3d development has amassed such competence in this field since its inception.

It's possible to have too much a synthetic data;

There are a few frequent issues I’ve experienced with folks getting started with AI and ML in all of the work we have been fortunate enough to undertake in this ever-changing sector. Here are two simple pointers to keep in mind as you begin your AI/ML journey.  

1. Determine how much data you actually require.

In general, more data is better, but there is a limit where the benefits reduce. Consider the data that simulations will create. It’s possible to create a thousand years’ worth of video at 30 frames per second in just 24 hours.


Measure the results frequently, then decide what “good enough” means in your situation. Create predictive models after that. That’s also when you’re likely to build a strong data culture, in which your internal users trust and rely on data to make better decisions.

2. Simulated data is more accurate than actual data.

The real world is complex, and it isn’t always equitable. It’s simple for many data scientists or machine learning teams to obtain and use real-world data to train their systems. However, data describing a world where 80 percent of software engineers are men can readily be incorporated into ML models. Based on real-world data, the ML engine might learn that 80 percent of software engineers are male, and subsequently prefer male engineers over female engineers when creating a model to identify engineers in photographs.

It’s also worth noting that personal data isn’t required for cutting-edge ML, AI, and data analytics work. Gender, age, and other personal data, for example, are irrelevant to gaming in our engine. It’s all about how you play the game. Other applications can and should use this approach.


Building a future with simulated data:

In our work with customers, we’ve already seen some pretty great results, and there’s so much more potential as these new technologies come together. When you combine reinforcement learning with spatial simulations, you may create a robot with vision capabilities that learns on the fly and can perform tasks that would normally be performed by humans.

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