Synthetic data is annotated information generated by computer simulations or algorithms as a substitute for real-world data. To put it another way, synthetic data is generated in digital worlds instead of being accumulated or measured in the real world.
Although it is artificial, synthetic data statistically or mathematically represents real-world data. According to research, it can be as useful as, if not better than, information based on actual people, events, or objects for training an AI model.
As a result, deep neural network developers are increasingly using synthetic data for training their models. Indeed, according to a 2019 survey of the field, the use of synthetic data is “one of the most exciting general methodologies on the rise in contemporary deep learning, particularly computer vision,” which relies on unstructured data such as images and video.
Gartner predicted in a June 2021 study on synthetic data that by 2030, the majority of the data used in AI would be produced artificially by simulations, guidelines, statistical models, or other methodologies.
What Is the Importance of Synthetic Data?
Synthetic data is useful because it can be produced to fulfill specific requirements or conditions that are not met by existing (real) data. It can be useful in a variety of situations, including:
- When data availability is restricted due to privacy concerns or how it can be used
- Data is required for testing a product before it is released, but such data either does not exist or is not accessible to the testers.
- Machine learning algorithms require training data. However, such data is expensive to produce in real life, particularly in the case of self-driving cars.
Though synthetic data was first used in the 1990s, the enormous amount of storage space and computing power in the 2010s made synthetic data more widely used.
To train neural networks, developers require large, meticulously labeled datasets. In a broad sense, more diverse training data leads to more accurate AI models. The issue is that collecting and labeling datasets with tens of millions to tens of thousands of elements takes time and is often extremely expensive.
Enter Synthetic data. According to Paul Walborsky, co-founder of AI. Reverie, one of the first specialized synthetic data services, a solitary image that could cost $6 from a classifying service, can be produced artificially for six cents.
Saving money is only the beginning. Synthetic data is critical in dealing with privacy concerns and minimizing bias by guaranteeing data diversity to accurately reflect the real world. Since synthetic datasets are automatically classified and can include rare but critical corner cases, they are sometimes superior to real-world data.
How Is Synthetic Data Created?
To understand the creation process of synthetic data, you need to first know the methods used for creating it and the strategies that help to build it.
Methods for Creating Synthetic Data
When deciding on the best method of producing synthetic data, it is critical to first consider the kind of synthetic data you want. There are three main categories to choose from, each with its own set of advantages and disadvantages.
1. Fully Synthetic
There is no original data in this data. It implies that re-identification of any single unit is nearly impossible while all variables remain fully available.
2. Partially Synthetic
Only sensitive data is supplanted with synthetic data. It necessitates a strong reliance on the imputation model. This reduces model dependence, but it does allow for some disclosure due to the true values that persist in the dataset.
3. Hybrid Synthetic
This data is created by combining real and synthetic data. The fundamental allocation of real data is evaluated, and the closest neighbor of every data point is created while ensuring the correlation and authenticity of other parameters in the dataset.
For every record of the actual data, a comparable record in the simulated data is selected, and these are then merged to produce hybrid data.
Strategies for Building Synthetic Data
When it comes to building synthetic data, one of the following two main strategies is typically used.
1. Drawing Numbers from a Distribution
This technique works by analyzing real statistical distributions and simulating them with synthetic data. This can include the development of generative models.
2. Agent-Based Modeling
In this method, a model that describes an observed behavior is formed, and then random data is reproduced using the same model. It stresses comprehending the impacts of agent contacts on a system as a whole.
3. Deep Learning Models
In addition to the two main strategies, deep learning models are also utilized to build synthetic data. Generative Adversarial Network (GAN) and Variational Autoencoder models are artificial data generation methodologies that enhance data utility by providing more data to models.
Benefits of Synthetic Data
The ability to produce data that is similar to the real thing may appear to be an infinite way to create situations for testing and development. While this is true, it is worth remembering that any synthetic models derived from data can only recreate specific properties of the data, which means that they will eventually only be able to recreate general trends.
Having said that, synthetic data has several advantages over real data, including the following.
1. Overcoming Real-World Data Usage Constraints
Real-world data may be subject to usage restrictions as a result of privacy laws or other regulatory requirements. Synthetic data can recreate all-important statistical characteristics of real data without disclosing the real data, thereby addressing the problem.
2. Generating Data to Simulate Never-Before-Experienced Conditions
Synthetic data is the only option when real data is unavailable. With synthetic data, you can simulate or create conditions never experienced before.
3. Immunity to Some Common Statistical Challenges
Synthetic data offers immunity to some common statistical challenges. Skip patterns, item nonresponse, and other logical limitations are examples of these.
4. Focuses on Relationships
Instead of focusing solely on specific statistics, synthetic data looks to maintain multivariate associations between variables.
These advantages show that the creation and use of synthetic data will only increase as our data becomes more complicated and secure.
Challenges of Synthetic Data
Though synthetic data has numerous advantages that can help organizations with data science projects, it also has drawbacks. The following are some of them.
1. Potentially Missing Outliers
Synthetic data can only imitate real-world data; it cannot be a replica. As a result, synthetic data may not contain some outliers that exist in original data.
2. The Data Source Determines the Quality of the Model
The quality of synthetic data is strongly linked with the quality of the source data and the model used to generate the data. The biases in the original dataset may be reflected in synthetic data.
3. More Challenging User Acceptance
Synthetic data is a new concept, and consumers who have not yet witnessed its advantages may not accept it as valid.
4. It Takes Time and Effort to Generate Synthetic Data
Synthetic data, while easier to generate than actual data, is not free. Instead, a lot of time and effort is required to generate it.
5. Output Control is Mandatory
Comparing synthetic data with human-annotated or reliable data is the best way to make sure the output is correct, particularly in complex datasets. This is due to the possibility of discrepancies in synthetic data when attempting to recreate intricacies in original datasets.
The Role of Synthetic Data in Machine Learning
The importance of synthetic data in machine learning is rapidly growing. This is because machine learning algorithms are instructed with massive amounts of data, which can be hard to acquire or produce without synthetic data.
It can also play a major role in the development of image recognition algorithms and other tasks that are becoming the foundation of AI. There are several additional advantages to using simulated data to assist in machine learning development:
- Once a preliminary synthetic model/environment has been formed, data production becomes easier.
- Classifying accuracy that would be prohibitively expensive or even unobtainable by hand
- The ability of the synthetic environment to be altered as needed to improve the model.
- Usability as a replacement for data containing sensitive information
Generative Adversarial Networks (GAN) and self-driving simulations are two synthetic data applications that are gaining popularity in their corresponding machine learning communities.
Learning through real-world experiments is difficult in real life, and it is even more difficult for algorithms. It is especially difficult for people who are hit by self-driving cars, as in Uber’s fatal crash in Arizona. While Uber reduces its Arizona operations, it should probably increase its simulations to train its models.
Real-world experiments are costly: Waymo is erecting a whole mock city for self-driving simulations. However, only a few businesses can afford such costs.
On the other hand, industry leaders such as Google have relied on simulations to generate millions of hours of artificial driving data for training their algorithms to reduce data generation costs.
Ian Goodfellow et al. introduced Generative adversarial neural networks (GAN), in 2014. These networks represent a recent advancement in image recognition. They are made up of a generator network and a discriminator. While the generator network creates synthetic images that are as realistic as possible, the discriminator network attempts to distinguish between real and synthetic images.
Both networks create new nodes and layers to improve their performance. While this method is commonly used in neural networks for image recognition, it has applications outside of neural networks. It can also be utilized for other machine learning techniques.
Applications of Synthetic Data
Synthetic data enables us to keep building new and disruptive products and solutions even when the data needed is not present or available.
Today, the application of synthetic data is seen in financial services, automotive and robotics, healthcare, manufacturing, and more. Additionally, there are several application areas for synthetic data including machine learning, HR, marketing, and DevOps/agile development.
Ford researchers explained in a recent podcast how they merge generative adversarial networks (GANs) and gaming engines to generate synthetic data for AI training.
Additionally, BMW developed a virtual factory utilizing NVIDIA Omniverse, a simulation platform that allows organizations to work collaboratively using multiple tools, to improve the process of making cars. Data generation by BMW is used to fine-tune how production workers and robots collaborate to build cars efficiently.
Synthetic data is used by healthcare professionals in fields such as medical imaging for training AI models while safeguarding patient privacy. Curai, for instance, trained a diagnostic model on four hundred thousand simulated medical cases.
GANs are also gaining traction in finance. American Express investigated ways to use GANs to generate synthetic data to improve its AI models that detect fraud.
In retail, startups like Caper use 3D simulations to take as little as five images of a product and generate a synthetic dataset of 1000 images. These datasets activate smart stores in which customers can grab all that they need and walk out without having to wait in a checkout line.
Final Word
Synthetic data is a method of processing sensitive data or creating data for machine learning projects. If your organization has access to sensitive information that could be used to create useful machine learning models, such as synthetic images, Achievion can assist you in creating such models using synthetic data. You can learn more from our team by getting in touch today!
