Synthetic Data: Paving the Way for Automation

Synthetic data is consistently able to fill the gaps where real-world data can't quite manage to hit the mark. Whether it's for the advancement of artificial intelligence or enhancement of robust simulations, synthetic data has one thing that real-world data never will have; controlled variation.

Synthetic data being created artificially gives a major advantage which allows us to control test conditions and variations within the data. Instead of needing to rely on real-world data to satisfy every single test condition you can think of, synthetic data fills each of those gaps with ease, and allows for not only progression, but automation as well. Soon, artificial intelligence will be able to improve itself by synthesizing its own simulated data and automate its own evolution. 

Think of it; if artificial intelligence is able to automate its own testing and training and improve itself until completion, there won't be a need for real-world data anymore. AI would just need to create its own data to adjust itself to, which let's face it, would cover more ground a lot more quickly than any non-synthetic data counterparts. 

For example, self-driving cars being able to calculate the quickest route to any given destination on the fly and adjusting accordingly based on upcoming traffic, accidents that may have occurred, or any other predicted trouble on the road would innovate the automobile industry to no end.

This also begs the question, if everyone is using synthetic data for automation, who will do it best? Will AI compete with each other to automate itself best? Only time will tell.