Synthetic Data for AI Training

What is synthetic data?

Synthetic data is data which have been created using computer programs and algorithms. Such data possess similar mathematical properties to real ones but do not contain information from them. Synthetic data is modeled to be similar to real systems or processes but do not copy information from them. They merely generate new information based on existing data.

Synthetic data can be created in unlimited quantities and with all specified characteristics, which allows training artificial intelligence models on a large amount of data, even if real data are unavailable or very limited.

Thus, synthetic data is a powerful tool for training artificial intelligence, which can help overcome some limitations associated with a shortage of real data. However, creating synthetic data requires a careful approach and special knowledge in the field of modeling and machine learning.

Types of Synthetic Data

So, several types of synthetic data can be used depending on the specific task:

• Fully Synthetic. Such data is completely created from scratch using algorithms and models, without the use of real data. Instead, a computer program generates data using only the basic parameters and features of the source data. Then, based on this, the program randomly generates content using generative methods. Fully synthetic data can be useful in two cases: when it is necessary to create a large dataset that does not exist in reality, or when data is needed that meets certain criteria that are difficult to find in real data. For example, fully synthetic images can be created using computer graphics for training computer vision models.
• Partially synthetic. Such data is created by combining real data with synthetic ones. This can be useful when there is not enough real data for training models, but there is enough to serve as a basis for generating synthetic data. For example, partially synthetic images can be created by combining real images with synthetic images created using image generation algorithms.
• Hybrid data. This type of data is a combination of real and synthetic data. For example, hybrid data can be created by adding synthetic data to real data to increase their volume or to improve their quality. This can be useful when real data have some limitations, such as insufficient quantity, lack of necessary features, or noise in the data. For instance, if the real data contains noise or errors, we can create synthetic data that will correspond to the real data but will not contain noise or errors. Then we can combine the synthetic data with the real data to create hybrid data, which will have higher quality than the real data.

In addition to these three types, there are also other types of synthetic data, such as synthetic texts, synthetic audio recordings, synthetic time-series data, and so on.

Each type of synthetic data has its advantages and limitations. The choice of such data should be based on the goals of the project, the availability of real data, confidentiality requirements, and other factors. In any case, the use of synthetic data can significantly improve the efficiency of training artificial intelligence models, reduce the time and cost of data collection and processing, and ensure a high level of confidentiality and security.

How do synthetic data differ from real data?

Synthetic data differ from real data in that they are artificially created, not obtained from the real world. Real data represent a set of actual observations or measurements that have been obtained under real conditions, whereas synthetic data are merely created using computer algorithms and models based on real data.

One of the main advantages of synthetic data is that they can be created in any volume and with any characteristics, allowing researchers and developers to create data sets that fully meet their needs.

However, synthetic data also have their limitations. They may not reflect all the nuances and complexities of the real world, which may be important for a specific task. Also, creating synthetic data requires significant computational resources and computer power.

In general, synthetic data can be a useful supplement or alternative to real data in many areas of activity.

How can synthetic data be used?

Synthetic data can be used in all areas where a large amount of data is required for training artificial intelligence models, software testing, data analysis, and other purposes. Let’s consider some examples of using synthetic data:

• Training of artificial intelligence models. Synthetic data is well suited for training AI models. They can help overcome limitations associated with real data. For example, such as insufficient data, lack of necessary features, or noise in the data. For instance, NVIDIA uses synthetic data for training speech recognition models. Synthetic data allows the creation of a large number of diverse speech examples needed for training the model.
• Software Testing. Synthetic data can be used for testing software in any scenario and conditions. This can help identify errors and shortcomings in the software before it is used in real conditions. For example, Microsoft uses synthetic data to test its Windows operating system. Synthetic data allows you to create different software usage scenarios and test its performance under these conditions.
• Data Analysis. Synthetic data can be used for data analysis in a very wide range of areas, such as finance, healthcare, marketing, and others. They can help researchers explore different scenarios and hypotheses without risking real data. Typically, such analysis will be cheaper than actual practice. For example, “Sberbank” uses synthetic data for testing and optimizing its machine-learning algorithms in the financial sector. This allows the bank to improve the quality of its services and fight more effectively against fraudsters and criminal schemes.
• Training and Practice. Synthetic data can be used to train and practice people in different professions, such as medicine, aviation, military, and others. They can help create realistic simulations and scenarios for training and practice, without risking the lives of real people. For example, the Russian airline “Aeroflot” uses synthetic data for training pilots and flight attendants. This allows us to create realistic simulations of various emergencies and practice crew actions in conditions as close as possible to real ones.
• Preserving Confidentiality. Synthetic data can be used to maintain confidentiality in data processing and storage. They can be created in such a way as not to disclose personal information or other confidential data, while still preserving the necessary basis for analysis and processing. For example, Apple uses synthetic data to train its speech recognition algorithms, without disclosing users’ personal information. Synthetic data allows for the creation of necessary conversation examples, without revealing confidential information.

In general, synthetic data can be used in many areas where there is a need to work with a large amount of information or there is a need for its generation.

How and with what technologies is synthetic data generated?

Synthetic data generation can occur through such methods:

• Based on statistical distribution;

The generation of synthetic data using statistical distribution is based on extracting digits from observed real statistical distributions. This approach allows the reproduction of similar actual data, even when real data are not available.

To create a data set that will have a random sample distribution, it is necessary to have a correct understanding of the normal statistical distribution in real data. This can be achieved through various methods, such as the normal distribution, chi-square distribution, exponential distribution, and others. The accuracy of the trained model largely depends on the experience of the specialist in data processing and analysis in this method.

• Based on the agent to the model;

Synthetic data generation based on the agent in the model is based on the use of autonomous software agents that interact with each other and with the virtual environment according to specified rules and algorithms. This approach allows you to create synthetic data that mimic real interactions and behavior of objects in the system.

For example, when modeling a transport system, agents can be created representing cars, cyclists, pedestrians, and other road users. Each agent will act by the prescribed rules and algorithms, such as road traffic rules, behavior in collisions, acceleration, and braking.

The interaction of agents with each other and with the virtual environment will generate synthetic data that can be used for training machine learning models, testing, and optimizing the transport system.

• Based on the use of deep learning.

Generating synthetic data using deep learning models can be carried out using a Variational Autoencoder (VAE) or Generative Adversarial Network (GAN).

VAE is a type of unsupervised machine learning models that use an encoder to compress and represent actual data, while a decoder analyzes these data to create a representation of actual data. The goal of VAE is to ensure maximum similarity between input and output data.

GAN consists of two competing neural networks: a generator, which creates synthetic data, and a discriminator, which determines whether the data are fake or real. The discriminator improves the detection of fake data, while the generator adjusts the next batch of data accordingly.

There is also a method for generating additional data known as data augmentation. This method is a process of adding new data to an existing dataset, but it is not considered synthetic data generation.

Various tools such as Datomize, Gretel, Synthesized, Hazy, Sogeti, CVEDIA, Rendered.AI, Oneview, and MDClone are used to generate synthetic data. These tools use various algorithms and technologies to generate synthetic data that can be used for training machine learning models, testing, and data analysis.

In addition, synthetic data generation can occur using a GPT chat. So, synthetic data generation using GPT is a process of creating new data using a trained model. This model uses deep learning technology and natural language processing algorithms to understand and generate text similar to the one it was trained on.

To generate synthetic data, GPT uses a sampling method from the trained model. This means that the model generates new data based on probability distributions it has learned from training data. The model can generate both individual sentences and whole texts, depending on the task given to it.

GPT can be trained on a large amount of various data, such as news texts, articles, books, conversations, and others. This allows it to generate synthetic data that accurately reflects real scenarios and can be used to train other artificial intelligence models and algorithms.

In addition, GPT can be used to generate synthetic data in various fields, such as finance, biotechnology, blockchain, and others. This makes it a very flexible and universal tool for generating synthetic data.

How is synthetic data related to blockchain?

Synthetic data and blockchain are linked to each other, as both provide opportunities for improving data privacy and security.

Blockchain is a decentralized database that ensures transaction transparency and security. It can be used to store and process synthetic data to ensure their authenticity and immutability. For example, synthetic data can be written into the blockchain along with a hash sum, which will be used to verify their authenticity.

On the other hand, synthetic data can be used to enhance privacy in the blockchain. For example, instead of storing personal information in the blockchain, one can use synthetic data, which mimics real data but does not contain confidential information. This allows preserving data privacy, while still providing opportunities for their analysis and use.

Furthermore, synthetic data can be used for testing and optimizing blockchain systems. For example, developers can use synthetic data to test vulnerabilities and check the performance of the blockchain network.

However, not only is synthetic data beneficial for blockchain, but blockchain is also beneficial for them. Everything is interconnected.

Blockchain can also assist in managing access rights to synthetic data. By using smart contracts, blockchain can automate the process of providing access to synthetic data only to those who have the necessary permissions. This can help prevent unauthorized access to synthetic data and ensure that they are used only for legitimate purposes.

In addition, blockchain can assist in creating new business models related to synthetic data. For example, companies can create tokens representing synthetic data and sell them on the open market. This allows them to create new markets and business opportunities directly linked to synthetic data.

Therefore, blockchain can be beneficial for synthetic data, providing transparency, security, immutability, access rights management, and new business models. This makes blockchain and synthetic data complementary technologies that can be used together to create new opportunities and improve existing systems.

Our synthetic data generation ecosystem is based on the $SAI token, which is the foundation for its maximum openness and decentralization. This allows us to scale our ecosystem and allows both our regular and larger users to experiment with and use the data we generate on a large scale. We are confident that access to high-quality data is a key factor for the successful training of artificial intelligence models and algorithms. However, currently, access to real data may be associated with various restrictions, such as confidentiality, security, and legislative restrictions. This hinders progress in the field of artificial intelligence and prevents it from being used to its full potential. We solve this problem by generating synthetic data that accurately reflect real-world scenarios and provide reliable training results for artificial intelligence models and algorithms. Our specially trained AI-based algorithm allows us to create synthetic data on user requests, making our service flexible and scalable. Our synthetic data generation ecosystem is based on blockchain technology, which ensures transparency, security, and reliability of all our data. Thanks to the use of blockchain, we can guarantee that data cannot be altered or forged and that they are accessible only to those who have the appropriate access rights.

Therefore, we believe that our synthetic data generation ecosystem will become a key factor in the progress of artificial intelligence and will lead to discoveries and applications in various areas of life.

We invite all interested parties to join us and become part of our ecosystem, to scale and develop it together, and to bring closer the future in which artificial intelligence will work for the benefit of all humanity. Thanks to the use of blockchain, we can guarantee that our ecosystem will be safe, transparent, and reliable for all its participants.