Synthetic data is computer-generated data that models the real world. Synthetic data and generative artificial intelligence (AI) datasets have emerged as a disruptive new approach to solving the data problem in computer vision (CV) and machine learning (ML). By coupling visual effects (VFX) and gaming technologies with new generative artificial intelligence (AI) models, companies can now create data that mimics the natural world. This new approach to training machine learning models can create vast amounts of photorealistic labeled data at orders of magnitude faster speed and reduced cost.
Collecting, labeling, training, and deploying data and datasets is difficult, costly, and time-consuming for users. Multiple surveys have uncovered that artificial intelligence teams spend anywhere from 50–80% of their time collecting and cleaning data, which is a significant challenge.
On average, individual organizations spend nearly $2.3 million annually on data labeling. Additionally, real-world datasets raise ethical and privacy concerns. Examples of applications and use cases that require human images include ID verification, driver and pedestrian monitoring, metaverse, security, and AR/VR/XR. In these areas privacy challenges are pronounced.
Synthetic data is a tool that also enables customers to build machine learning (ML) models in a more ethical and privacy-compliant way. Researchers and developers can deploy models and bring new AI-driven products to market faster than ever before by using techniques that harness synthetic data.
Synthetic data provides a privacy-compliant solution for CV applications
Synthetic data will transform CV software development
Gartner research predicts that the vast majority of businesses that seek to scale digital efforts will fail in the coming years. The few customers that succeed will do so by taking a modern approach to data and analytics governance, including the use of artificial data techniques.
In fact, Gartner believes that 60% of the data used for the development of artificial intelligence and analytics solutions will be synthetically generated. MIT Tech Review has called synthetic data one of the top breakthrough technologies of 2022. Synthetic data will dwarf the use of real data by 2030.
The increasingly sophisticated machine learning models being developed today require ever larger amounts of diverse and high-quality training data. The use of cheaper, easier, and quicker-to-produce synthetic data is rapidly emerging as a key driver of innovation.
Synthetic data is propelling ML models to new heights of performance and ensuring they perform robustly in a variety of situations and circumstances, including edge cases.
In addition to requiring vast amounts of data for training machine learning models, customers have complex regulatory, safety, and privacy challenges that can be addressed by replacing real data with synthetic training datasets.
