Synthetic Data is “any hypothetical production data applicable to an existing situation that isn’t observed directly by a direct observation” as per the McGraw-hill dictionary of technical and scientific terms; wherein Craig (1947) defined it as: “any data that can be utilized to make up an original or estimated estimate”. With the advent of modern technology, synthetic data has come a long way. The word “data” is derived from the Greek word “das”, which means “of a thing or idea”. Synthetic data, therefore, refers to an estimate, result, or data that can be used in place of or in addition to the actual or authentic data.
This was further developed into what is called “data mining”. With the aid of computers and various software packages, Synthetic Data mining has become a lot more effective. It takes various forms such as visual/spatial processing, financial trading, consumer surveys, quality assurance, and simulation to name a few. Visual/spatial processing relies on the processing of real or digital images and visual stimuli for form generating decisions, while financial trading uses simulations of forex market situations. Consumer surveys, on the other hand, use sophisticated survey tools coupled with mathematical methods and statistical analysis to generate feedback and opinions from the customers; while quality assurance uses data used in testing products and services for competitive advantage.
One popular form of synthetic data generation is the use of metagenomics. In this method, researchers create a database of genetically altered seeds that are then distributed to several researchers who are willing to manipulate them. Through the technique, the number of crops that can be planted in a particular region or area is limited only by the amount of genetic material that can be borrowed from a different source. Simulations in this case also play an important role in the generation of realistic estimates. But what exactly is a metagenome?
Metagenomics is one of the many applications of artificial intelligence and synthetic data include the study of ecosystems in natural settings. It aims to improve our understanding of nature through the study of genetics. This technology also allows users to experiment with the effects of climate change, control pollution, and protect wildlife. Robotic and AI machine learning systems used together will allow us to create synthetic data that mirrors the real world better than any existing technique.
Another application of artificial intelligence and synthetic data generation techniques is in the domain of medical research. Several pharmaceutical companies are using such techniques to improve the accuracy of clinical tests. The importance of accurate results cannot be stressed enough. Some pharmaceutical companies have already succeeded in deriving clinical test data from AI machines using the two-domain randomization technique. Domain randomization is a crucial aspect in the field of medicine since it allows experiments to be run repeatedly and with high precision, thereby increasing the chances of a positive result.
Computer vision applications and Synthetic Data Companies are also important in the imaging domain. Imaging labs now use artificial intelligence techniques and data analysis techniques to fine-tune images with higher resolution and less noise to make them better tools for medical professionals. The same technique can also be applied in machine vision projects. For example, images taken by computer vision cameras can be used to train the camera software to recognize different objects. Researchers at NASA’s space program have already used artificial intelligence techniques and synthetic data informing the tools necessary to capture images from space. Such solutions are important to the future of space exploration as well as for the training of machine vision software.
In the financial domain, test data from synthetic experiments can help test designers avoid costly mistakes. A prime example is when the synthetic data generated by machine-learning models are compared to the results predicted by the designers, an obvious discrepancy can indicate a poor machine-learning project. Such mistakes can be avoided through careful pre-analysis. The test data generated by machine learning models can help test designers achieve greater accuracy in producing better product quality.
Machine learning algorithms can generate synthetic data using a wide variety of empirical data sets. These can include results from fMRI scans or EEG recordings. Another great way to create synthetic data using empirical data sets is through the use of probabilistic neural networks. These networks allow researchers to leverage the statistical properties of a real data set to create new synthetic datasets with high accuracy levels and high precision.