- Synthetic data does not come with biases that can provide accurate data.
- But certain synthetic data forms cannot represent the constantly changing real-time environment.
AI needs to comply with restrictive data privacy regulations to provide accurate results with zero bias. Several solutions have been seen over a couple of years to address the challenges. These include tools to identify and reduce the bias, to anonymize user data, and programs. It ensures that data is collected only when the user consents. But each of the above-mentioned solutions faces its challenges. A new industry is emerging which promises to be a savior – synthetic data. Synthetic data is artificially generated by computers which can stand for data that is similar to real-world data.
A synthetic data set should have similar mathematical and statistical properties as a real-world data set. But in this method, does not represent real-life individuals explicitly. You can think of this as a digital mirror in the real-world data which statistically reflects the world. This enables the training of systems in a virtual realm. It can be easily customized for various use cases that range from healthcare to retail finance, transportation, and agriculture. More than 50 vendors have already developed synthetic data solutions as a result of research that StartUs Insights provided last June.
The problems with real-life data:
In the last few years, there has been increasing concern about how the data sets come with inherent biases. This leads to to the AI algorithms perpetuate systemic discrimination. Gartner predicts that by the time it is 2022, 85% of their projects will provide erroneous outcomes due to bias within data algorithms or the teams responsible for their management. With widely used AI algorithms, there has been a growing concern around the privacy of data. This has led to your stronger consumer data privacy and protection laws in the EU with GDPR and US jurisdictions which include California and most recently Virginia.
Such laws offer consumers and increased control over their data. For instance, the Virginian law allows consumers the right to access, correct, delete, and obtain a copy of personal data. The user can also opt-out of the sale of personal data and deny algorithmic access to personal data for targeted advertisement or consumer profiling. As access to the information is restricted, the individual gains a certain amount of protection at the cost of effectiveness of the algorithm. The more data and an algorithm can be trained on, the more accurate and effective the results would be.
Without access to massive data, the assistance with medical diagnosis and drug research would come down to a specific limit. One alternative for privacy concerns is anonymization. For instance, personal data can be anonymized by masking or elimination identifying characteristics. These characteristics include the removal of names and credit card numbers from IIT commerce transactions or removing identification data from healthcare records.
Solution of synthetic data:
Synthetic data promise is to provide the advantages of AI without the demerits. It takes personal data out of the equation and replaces it with synthetic data to perform better than real-world data. This is accomplished by correcting the bias which is often found in real-world data. Although such data is ideal for applications that use personal data, synthetic data can be used in other scenarios as well. One such example is complex computer vision modeling where various factors interact with each other in real-time.
Synthetic video data sets leverage advanced gaming engines and can create hyper-realistic imagery to portray all the possible eventualities in an autonomous driving scenario. On the other hand, trying to photograph or videos of the real world to capture the events would be impractical, impossible, or plain dangerous. The synthetic data sets can tremendously quicken up and improve the training of autonomous driving vehicles.
Ironically one of the primary tools to build synthetic data is the same to create deep fake videos. Both make use of generative adversarial networks (GAN) which is a pair of neural networks. One network generates synthetic data while the second one tries to detect if it is real. This operates in a loop with the generator network improving the quality of data until the discriminator cannot spot the difference between real and synthetic data.
There is growing evidence that even if the data is anonymized from the source it can be correlated with consumer data sets that are exposed through a security breach. By the combination of data from various sources, surprisingly it is possible to form a clear picture of our identity even if anonymization is applied. In some scenarios, this can be established by data correlation from public sources without a serious security hack.
The emerging ecosystem:
Forrester Research recently identified several critical technologies which included synthetic data that will comprise what they call “AI 2.0” which advances and radically expands AI possibilities. Synthetic beta also brings other huge benefits: you can quickly create data sets and often with the data labeled for supervised learning. Also, it does not need to be cleaned up and maintained the way real data requires. Theoretically, it comes with large time and cost savings.
Following are the big names that have participated in the generation of synthetic data:
- AiFi: Puts synthetically generated data to use for retail store simulation and shopper behavior.
- Ai.Reverie: it generates synthetic data to train computer vision algorithms for activity recognition, object detection, and segmentation.
- Anyverse: it simulates situations to create synthetic data sets through raw sensor data, image processing functions, and custom LiDAR configuration for the automotive industry.
- Cvedia: develops synthetic images that simplify the source of large volumes of labeled, real, and visual data. The simulation platform employs multiple sensors to synthesize photorealistic environments which result in empirical data set creation.
- DataGen: interior environment use cases like smart stores in home robotics and augmented reality.
- Gretel: It is aiming to be GitHub equivalent for data. The company produces synthetic data sets for developers that retain the same insights as to the original data source.
