Data is essential to AI and Machine Learning models. Due to many factors, high quality data can be hard to come by. VentureBeat suggests synthetic data can be a valuable solution to this issue. Synthetic data “reflects real-world data, both mathematically and statistically,” but is generated by ” computer simulations, algorithms, statistical modeling, simple rules, and other techniques”. This is in contrast to data that is “collected, compiled, annotated, and labeled” based on real-world sources and experimentation. Real-world data is almost always the best source of data insights for AI and ML models, though it is often unavailable and may contain errors due to biases.
Synthetic data, on the other hand, is often faster to obtain, does not need to be cleaned, and reduces constraints in using sensitive and regulated data, thus allowing for quicker insights. Many companies apply synthetic data to use cases such as software testing, marketing, creating “digital twins,” testing AI systems for bias, or simulating the future. Banking and financial institutions use synthetic data to explore market behaviours, analyze consumer demographics, and combat financial fraud. When used in conjunction with real data, synthetic data can create an enhanced dataset that can often times “mitigate the weaknesses of the real data”.
However, synthetic data comes with its own risks and limitations. Depending on the quality of the model, synthetic data can be misleading and potentially lead to results that impede on data privacy. Some have even referred to synthetic data as “fake data”. However, the “breadth of its applicability” makes synthetic data critical for AI and ML models since it makes AI possible where a lack of data makes AI “unusable due to bias or inability to recognize unprecedented scenerios”.
Many tech startups are entering the space of synthetic data, and similarly data giants like Google, Microsoft, Facebook, and IBM are already using synthetic data or developing programs to do so. For instance, Amazon has relied on synthetic data to fine-tune the virtual assistant ‘Alexa,’ combining real-world and synthetic data to complete training datasets. Synthetic data can be ‘ordered’ to the exact use case for which you are training a given ML model.
Read the full article from VentureBeat.