Synthetic Data Generation

“Synthetic data can be used to train data-hungry algorithms in small-data environments, or for data sets with severe imbalances. It can also be used to train and validate machine learning models under adversarial scenarios”

The Alan Turing Institute

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.

60%

percent of the data used to develop AI will be synthetically generated by 2024 - Gartner

Why is synthetic data generation important for AI?

Systems use a combination of sensors to recognize and interpret the world around them. Take autonomous vehicles for example, they have cameras recording 2-D and 3-D images and video, radar and LiDAR. Their systems are trained to recognize every object and environmental variable they could encounter in the real-world. This means practically an unlimited number of possible edge cases which would be too cost prohibitive to generate in the real world. Therefore, companies must use synthetic data - and lots of it. Waymo for example said that as of 2020, it had simulated 15 billion miles of driving when compared to just 20 million miles of actual driving. This is because:

Real world data is difficult to obtain. Many times, the most valuable information is that of ‘rare’ events. Rare events are by definition hard to get in sufficient volumes. An example of a rare event is a car accident. Car accidents don't happen often, so it would take decades to generate enough data by just relying on the real world. Synthetic data on the other hand can take seconds to generate and you can choose how many accidents you want to simulate while training your neural network
Real world data is messy. On numerous occasions, a situation will happen and your system doesn’t have enough data to appropriately classify it. This means that engineers have to go back to label that data by hand. Synthetic data allows engineers to easily represent any situation while simultaneously giving them the ground truth and the annotations required. They can test their “what if” scenarios and review different outcomes in a cost effective, accurate and scalable way

A shift is underway — are your synthetic data generation tools helping or hindering?

Collimator is the only tool that allows you to model test cases using Python or a graphical UI, generate synthetic data using HPC in the Cloud, and export the data via API to your neural network so you can focus on solving the technical challenge ahead

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Traditional Applications

Cannot quickly ingest or export the amounts of data required

Difficult to use open source libraries because they run proprietary languages

Involves extra time, effort and money to run HPC simulations

Seamlessly ingest or export data by directly connecting to your database via API

Easily access Python libraries or call your own code to generate your data directly

Quickly generate synthetic data over millions of runs using HPC in the Cloud