Data is the new currency now more than ever with the rise of artificial intelligence.

Synthetic data has emerged as a new form of currency that is revolutionizing the way businesses and organizations train their algorithms and models.

The future will provide tremendous computer computational power. But what might be trailing behind is the availability of data that is essential in processing and training. The Future is, at least in part, synthetic data.

Synthetic data is data that is generated by computer algorithms rather than being collected from the real world. It is becoming increasingly valuable in artificial intelligence (AI) for several reasons.

First, synthetic data can be used to train machine learning models when real-world data is not available or is difficult to obtain. Second, synthetic data can be used to test and evaluate AI models, especially in situations where real-world data is too scarce or too costly to use. Third, synthetic data can be used to generate new data sets that can be used to train and test models, which can be used to improve the performance of the models and increase their generalizability. Finally, synthetic data can be used to create data sets with specific properties, such as data sets that are balanced or that contain certain features, to train models that can perform well on specific tasks.

Gartner estimates that by 2024, 60% of the data used for the de­vel­op­ment of AI and an­a­lyt­ics projects will be syn­thet­i­cally gen­er­ated.

In medicine, synthetic data is used in a variety of ways. One common use case is in the development and training of medical imaging algorithms. For example, synthetic CT or MRI images can be generated that mimic real-world scans, and can be used to train deep learning models to detect and diagnose diseases. This can be useful in situations where obtaining real-world medical images is difficult or impossible, such as rare diseases or conditions that are difficult to replicate in a laboratory setting.

Another use case for synthetic data in medicine is in drug discovery and development. For example, synthetic data can be used to model the interactions between drugs and proteins, and can be used to predict the potential side effects of a drug before it is tested in humans.

Synthetic data can also be used to test the robustness and generalization of AI models in medicine. Since the model is trained on synthetic data, the model can be tested on the real-world data to validate its performance.

Synthetic data has the potential to revolutionize many areas of computing, including machine learning, computer vision, and natural language processing. By creating realistic simulations of data, synthetic data can be used to train and test AI models in a controlled environment, reducing the need for expensive and time-consuming data collection efforts. Additionally, synthetic data can be used to protect sensitive information by allowing organizations to share data without revealing sensitive information. In the future, synthetic data is likely to become an increasingly important tool in the development and deployment of AI systems. It certainly should be on your digital radar.