Data is an essential resource for AI developers. Unfortunately, data procurement can be time-consuming and expensive. Hand labeling the contents of visual data to train computer vision algorithms is also resource-intensive and prone to error.
Instead, companies are now turning to synthetic data.
Below, we’ll explore the potential of synthetic data generation for automating the process of data collection.
What is synthetic data?
“Synthetic data” is realistic computer-generated text or imagery that machine learning scientists can use to train algorithms. Good synthetic data is virtually indistinguishable from real data.
There are several advantages to using synthetic over real-world data. First, you have more control over the data’s appearance and structure. For example, you can produce images in various lighting and weather conditions and from different perspectives. Greater control over the contents of a synthetic dataset can not only increase general accuracy, but also reduce bias in AI.
Finally, it is possible to produce synthetic data in virtually infinite supply. High volumes of training data further contribute to AI model accuracy.
Why is synthetic data important now?
It has become increasingly difficult to obtain data from the real world for AI training. Privacy restrictions are limiting the amount of available data and the ways in which it can be used. More recently, the global pandemic limited photographers’ and videographers’ access to sites where they would need to collect data.
Synthetic data solves for these limitations. It does not contain PII and it can be obtained from a desktop. Furthermore, synthetic data can boost AI performance. “How to Win with Machine Learning” (Harvard Business Review, Sep-Oct. 2020) asserted that AI models perform best when trained on a combination of real and synthetic data.
What are synthetic data’s applications?
More and more companies are relying on AI to make decisions across industries, including manufacturing, retail and even law. Prevalent business and industry applications are below.
- Clinical and scientific trials
- Product development
- Surveillance and tracking
- Consumer Goods
- Financial Services
- Information Technology
- Smart Cities
Comparing synthetic vs. real data performance
Data scientists can train an AI model with real data, synthetic data or a combination.
Real data is effective, but can be difficult or even impossible to find for certain scenarios. For example, to train a machine to navigate the ocean, you would need a great deal of footage taken underwater in different lighting, thermal and weather conditions. In this and almost every instance, there is not enough real data available. Real data can also be difficult to annotate accurately. Further, real datasets can contain biases that ultimately produce a biased algorithm.
Synthetic data can be produced in virtually infinite amounts, and images for hard-to-find scenarios can be produced on demand. The AI.Reverie synthetic data platform also gives data scientists control over camera angles and conditions like lighting and weather conditions. A limitation to synthetic is the “domain gap,” or the difference in photorealism between computer generated imagery and photos from the real world — but that gap is quickly narrowing.
Case studies have shown that synthetic data is beginning to perform on par with, or better than, real data. Training on a combination of real and synthetic data can produce better results than either real or synthetic data alone.
What are the benefits of synthetic data?
As A.I. technology grows, so does the need for data to train it. Synthetic data, data that is artificially generated, could be critical to the expansion and proliferation of A.I.
For many developers, the greatest bottleneck to A.I. development is the lack of available data. Existing real datasets are still insufficient given the complexity of the A.I. systems being developed. Synthetic data could be the key to developing A.I. systems that can complete complex tasks.
There are several key benefits of synthetic data:
- Protects privacy: Synthetic data can replicate real data’s statistical properties without exposing real people’s information.
- Captures edge cases: Synthetic data is the only viable solution for data that does not currently exist. For example, while it is difficult to capture extensive footage of a real car crash, synthetic simulation engines can produce images for all possible crash scenarios.
- Safeguards against bias: Real datasets often contain more images than can be realistically reviewed by those who use them, and they may contain unknown biases. It is possible to proactively control for bias when producing balanced synthetic data.
How is synthetic data generated or created?
There are three main strategies for building synthetic data:
- Generative models: A generative model is an inference procedure that can create many training examples from a small set of latent variables. Generative models are particularly popular within the machine learning community for various applications, including the training of deep neural networks, image modeling, speech modeling, and behavioral modeling.
- Agent-based modeling: An agent-based model generates synthetic data by many intelligent agents, each of which possess a different subset of the knowledge required to complete the task. The agents are programmed to perform tasks at the local level, with patches of knowledge that link them to other agents.
- Deep learning models: Variational auto-encoder and generative adversarial network (GAN) models are synthetic data generation techniques that improve data utility by feeding models with more data.
What are synthetic data case studies?
Below is a look at one case study from CosmiQ Works and AI.Reverie that demonstrates the impact of synthetic data on A.I. model accuracy.
RarePlanes: Synthetic Data Takes Flight
RarePlanes is the largest openly-available very-high-resolution dataset built to test the value of synthetic data from an overhead perspective. The experiments with RarePlanes show:
- Synthetic data alone can train a robust object detection algorithm, as benchmarked against real-world data.
- Fine-tuning the synthetic only model with 10% of the observed dataset achieved roughly the same results as training on 100% of the observed dataset. This method would bypass 90% of the manual data labeling and collection effort.
The idea of synthetic data generation is to create structured data without any human intervention but using an intelligent algorithm. Once generated, the data is human-semantic, so it is not just a random set of meaningless data. It is structured, which can be very useful because it can be used for training algorithms on data that is not available but could be generated if needed.
To learn more about AI.Reverie’s synthetic data generation solutions, contact us.