Step by step, AI has been permeating virtually every application we use. From consumer-facing interactions to more advanced predictive B2B analytics, AI and ML algorithms consume increasing amounts of data. While thousands of companies have begun collecting data in vast quantities, the problem is that these data take a while to clean and prepare for AI consumption.
The efficacy of an AI system depends on the quality of the data it’s trained with. Real-world data comes with significant restrictions regarding its use and is limited in variance. As a result, the number of scenarios in which any given algorithm can be trained is often limited.
Synthetic data sets have begun making an impact in industries where AI use is critical. Here’s how four big sectors are using Synthetic data to power their AI applications.
There is, arguably, no industry where the use of AI is making a bigger difference than in defensive systems. This sector has become increasingly reliant on diverse AI use cases from risk assessment and threat mitigation to preventing the loss of human life. Given the chaotic nature of battlefields and threat scenarios, training AI using solely real-world data is impractical.
For starters, the frequency with which incidents occur is unpredictable. Secondly, it’s impossible to train ML algorithms to recognize every permutation of an extreme situation. Synthetic datasets generated based on real-world data sets or simulated scenarios can help defense departments build AI systems to respond to any threat imaginable.
A big reason for this is synthetic data’s flexibility. “You can create synthetic data for everything, for any use case,” notes Don Herman, co-founder and CEO of synthetic data generation company OneView, “which brings us to the most important advantage of synthetic data – its ability to provide training data for even the rarest occurrences that by their nature don’t have real coverage.”
Energy and infrastructure development companies face regular headaches monitoring their assets and the progress of their projects. Manual processes require employees to fly out to the site and report progress back to their teams. A more modern approach is to use satellite imagery or drone footage.
However, even these systems have a manual bent to them. Employees have to review footage and make sense of imagery that might not be of the highest quality. The process is time-consuming and inefficient. AI usage is increasing in infrastructure monitoring, but the lack of real-world data is a stumbling block.
“The reality is that the cost of quality data acquisition is high, and this is acting as a barrier preventing many from considering AI deployment,” writes Darminder Ghataoura, Fujitsu’s AI lead. “To tackle this challenge, organizations are increasingly looking towards synthetic data to address the data shortfall that is preventing AI adoption.”
The data quality gap is especially poignant in infrastructure modeling projects.
