Synthetic Data Generation

Going back 15 or so years ago, it was rare to use AI in system design or operation. The world lacked a few enabling factors to make it easy or cost effective to make AI useful. Today, AI is almost ubiquitous - 96% of business leaders surveyed by PWC said they planned to use AI simulations in 2022. The benefits of AI are numerous. One such benefit is that it allows engineers to train a system to solve a problem instead of explicitly programming the rules. For example, if you wanted to train a computer on how to play chess, instead of coding the billions of optimal moves it could make under different scenarios, you would instead feed it a number of games played and allow the neural network to statistically approximate the behavior of the moves and learn over time. 

AI systems learn by performing an action and comparing the result with the ground truth. Many people ask “how much data do I need to train my AI?” A general rule of thumb is that the more data you use, the more accurate your system will be. Thus there is no right answer and it depends on both the complexity of the problem and use case. For example, current estimates suggest that the amount of data needed to maintain a level-five autonomous vehicle would be 20 terabytes per hour, which means the amount of data required to train their neural network would be orders of magnitude larger. Getting this obscene amount of data is an extraordinary challenge and cannot realistically be accomplished with physical hardware. Thus companies hoping to develop these types of systems must therefore turn to synthetic data.