Synthetic Data

Synthetic data is artificially generated data used to accelerate AI model training across many domains, such as robotics and autonomous vehicles.

Synthetic data generation is the process of creating text, 2D or 3D images in the visual and non-visual spectrum from computer simulations, generative AI models, or a combination of the two. This technique can be used for both structured and unstructured data and is often applied to fields where original data is scarce, sensitive, or difficult to collect.

Synthetic data generation addresses core data science challenges, improving the training of machine learning (ML) models and streamlining AI development.

• Data Scarcity: Synthetic data solves the scarcity of real data for novel use-cases. This is crucial for improving the performance and robustness of models, especially in niche applications with limited real-world data.
• Data Privacy: Synthetic data avoids privacy issues by generating training data that mimic real-world statistics without directly corresponding to individual records. This anonymization is critical in fields such as healthcare and financial services, where strict regulations control data privacy and usage.
• Data Quality: Real datasets can be imbalanced, which can result in biased outputs from generative models and ML models. Synthetically generated data can augment existing data for larger, more representative datasets. This helps to minimize model bias and improve accuracy.
• Testing: Synthetic test data powers true-to-life simulations for AI software testing and evaluation in safe environments before deployment in real-world scenarios.

Virtual scenes based on real-world data incorporate augmented data as well as entirely new digital assets.  

Generative models can be used to create both types of assets. 

Diffusion models can generate high-quality visual content from text or image descriptions. By learning the relationships between images and the text used to describe them, diffusion models can be used to programmatically change image parameters like layout, asset placement, color, object size, and lighting conditions.

Neural network architectures that support synthetic data generation include generative adversarial networks (GANs) and variational autoencoders (VAEs). GANs generate data through a competitive process between two neural networks, one of which generates data samples while the other evaluates them against real data.

Transformers, a type of deep learning model, are also capable of generating synthetic data. By learning complex patterns and dependencies in datasets, transformers generate entirely new data  that corresponds to the existing training data. For example, in natural language processing, transformers can be used to create new textual content that mimics the style and context of a given body of text. Transformers can mimic tabular data by treating each row and column in the dataset as a sequence, learning the relationships and patterns, and generating new data that maintains the characteristics of the original dataset.

From asset creation to code generation, generative AI helps to create synthetic datasets that can be used to enhance training datasets for models in different scenarios.

Synthetic data is powering AI across fields and use cases.

Synthetic data is critical for training generative physical AI models that power autonomous mobile robots (AMRs) for use in spaces such as warehouses, distribution centers, and other industrial spaces. Synthetic data generation is used to create and annotate data from 3D simulations to enhance training datasets to ensure robots can accurately detect objects, avoid obstacles, and safely interact with their environment. Virtual training enhanced with synthetic data helps refine the robots’ perception and decision-making capabilities and drastically reduces the time and resources needed for real-world testing.

 In the automotive sector, synthetic data is needed to train the perception, planning and prediction models that power self-driving cars. Since manually collecting and labeling vast amounts of data to account for every possible traffic scenario is prohibitively expensive and time-consuming, data generated from deep learning approaches can be used to augment data collected from sensors like LiDAR, cameras, and radars. With a richer dataset, developers can optimize and validate the vehicle’s AI.   

Computer vision algorithms for fixed cameras can detect, classify, and track objects to help improve safety in public spaces or industrial sites, enable automated checkout in stores, and flag product defects on assembly lines. However, collecting a large and diverse dataset of images to train accurate computer vision and automated optical inspection algorithms is a major challenge. Synthetic image data enables developers to quickly create diverse training datasets by varying parameters such as scene angle, location, lighting, and more. This enables developers to streamline development inspection algorithms for various use cases. 

Synthetic data is powering AI across fields and use cases.

Synthetic text generation has multiple applications, from training cybersecurity models to identify phishing emails to generating privacy-preserving medical records. For example, in the healthcare industry, data is often fragmented and kept in silos and is privacy protected, stifling technology innovation that relies on access to high-quality data. To overcome this barrier, AI can be used to generate synthetic medical datasets that accurately capture the statistical properties of real medical records but preserve the privacy of sensitive data. These datasets can be used without restriction, unlocking opportunities for medical software development for a variety of use cases.

For all of the above use cases, developers can benefit from building synthetic data vaults to store, organize, and catalog annotated data for future model training and AI projects.