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Synthetic data is fictional or computer-generated data that is not directly reflected in the real world and does not come from actual observations or records. They are created or modeled using software or algorithms for use in various fields such as scientific research, machine learning training, software testing, and many other fields. Synthetic data can be used to create virtual environments, simulations, train artificial intelligence models, analyze and test algorithms, and many other tasks. They are designed to provide researchers, developers, and analysts with access to data for experimentation and analysis in environments where real-world data may be limited, unavailable, or impractical to use.
One of the key advantages of synthetic data is its controllability and the ability to create different scenarios and conditions for research without the limitations that can be associated with real data. Synthetic data helps in solving problems where real data may be limited or sensitive from a privacy point of view. In a huge number of fields, from science to technology, synthetic data are used to achieve specific goals and solve tasks that require the use of fictional or created data instead of real information resources. Synthetic data has many advantages over real data. They are cheaper, faster to process, much more diverse and easier to customize. Synthetic data allows solving some of the most difficult problems in AI – training models on rare or sensitive data, testing models for vulnerabilities and biases, and transferring models to new tasks or languages.
Synthetic data can be produced in almost unlimited quantities using two main approaches: computer simulations and generative AI.
Computer simulations use graphics engines to create realistic images and videos in a virtual world;
Generative AI uses special machine learning architectures such as transformers, diffusion models, and GANs (generative adversarial networks) to generate realistic text, images, tables, and other types of data from underlying data.
Both approaches allow you to create synthetic data on demand, configuring it according to the necessary parameters and characteristics.
One of the key advantages of synthetic data is that it is pre-labeled. Data labeling is the process of assigning labels or descriptions to data so that AI models can understand them.
Labeling real data manually is a time-consuming, expensive, and often impossible process. For example, to train a model to recognize objects in an image, you need to describe each object in each image of the dataset. Synthetic data eliminates the need to manually label information because the machine already understands the data it creates.
Another advantage of synthetic data is that it allows companies to circumvent some of the regulatory issues associated with the processing of personal data. Personal data is protected by different privacy laws in different countries and regions, so companies should be very careful when collecting, storing and using personal data of their customers or employees.
Synthetic data allows you to create information that is traceable to specific individuals, but retains the statistical properties of the original data. Thus, organizations can use synthetic data for product development, trend analysis, fraud detection, and other purposes without violating privacy regulations.
Replacing real data with synthetic data can speed up and make it cheaper to train and deploy AI models. AI models require large amounts of data to train and achieve high accuracy, and collecting and labeling real data can be time-consuming and costly.
Synthetic data can reduce the amount of real data needed or even completely replace it. Synthetic data can also be more effective for pre-training AI models for certain tasks.
In addition, replacing real data with synthetic data reduces the likelihood that the model will contain hidden biases or vulnerabilities.
Bias is the tendency of Model II to give wrong or unfair answers based on gender, race, age, or other characteristics.
The vulnerability is a flaw in the AI model that allows attackers to trick the model with fake or altered data.
Synthetic data allows you to find and fix problems in AI models with the help of special tools for generating test cases, counterfactual data (opposite to the facts) and other validation methods. Thus, we can make AI models more fair, robust, and transferable to other tasks.
Synthetic data is a powerful tool for AI development in today’s evolving data age. Such data opens up new opportunities for building and testing AI models on any type of data without limitations in terms of quantity, quality, or privacy. Synthetic data also helps improve the efficiency and safety of AI models, as well as extend their applicability to new domains and languages.
As the term “synthetic” suggests, synthetic datasets are created using computer programs, rather than being compiled using documentation of real-world events. The primary goal of a synthetic dataset is to be versatile and robust enough to be useful for training machine learning.
To be useful for a machine learning classifier, synthetic data must have certain properties. Although the data can be categorical, binary, or numeric, the length of the data set must be arbitrary and the data must be randomly generated. The random processes used to generate the data must be controlled and based on different statistical distributions. Random noise can also be placed in the dataset.
If synthetic data is used for the classification algorithm, the amount of class separation should be adjusted so that the classification problem can be made easier or more difficult according to the requirements of the problem. Meanwhile, for the regression problem, non-linear generative processes can be used to generate the data.
As machine learning frameworks like TensorfFlow and PyTorch become easier to use, and pre-engineered models for computer vision and natural language processing become more ubiquitous and powerful, a major challenge scientists must face is data collection and processing. Companies often have difficulty obtaining large amounts of data to prepare an accurate model over a period of time. Manual data labeling is an expensive and slow way to obtain data. However, the creation and use of synthetic data can help scientists and companies overcome these obstacles and develop reliable machine learning models more quickly.
The use of synthetic data has several advantages. The most obvious way in which the use of synthetic data benefits data science is that it reduces the need to derive data from real-world events, and for this reason it becomes possible to generate data and create a dataset much faster than a dataset that depends on a real world event. This means that large amounts of data can be generated in a short period of time. This is especially true for rare events, because if the event is rare in nature, more data can be compiled from some real data samples. In addition, data can be automatically labeled as it is generated, greatly reducing the amount of time required to label data.
Synthetic data can also be useful for training data for edge cases that may be rare but are critical to the success of your AI. Edge cases are events that are very similar to the main goal of artificial intelligence, but with important differences. For example, objects that are only partially in the field of view can be considered edge cases when developing an image classifier.
Finally, synthetic datasets can minimize privacy concerns. Attempts to anonymize data may not be effective because even if sensitive/identifying variables are removed from the data set, other variables may act as identifiers when combined. This is not a problem with synthetic data because it was never based on a real person or real event.
Synthetic data has a wide range of uses because it can be applied to almost any machine learning task. Common use cases for synthetic data include unmanned vehicles, security, robotics, fraud protection, and healthcare.
One of the initial use cases for synthetic data was self-driving cars, as synthetic data is used to create training data for cars in situations where it is difficult or dangerous to obtain real training data on the road. Synthetic data is also useful for generating data used to train image recognition systems, such as CCTV systems, much more efficiently than manually collecting and labeling reams of training data. Robotic systems can learn and evolve slowly using traditional data collection and training techniques. Synthetic data allows robotics companies to test and design robotic systems through simulation. Fraud protection systems can benefit from synthetic data, and new fraud detection techniques can be trained and tested on data that is constantly new when synthetic data is used. In healthcare, synthetic data can be used to develop accurate health classifiers that preserve people’s privacy because the data will not be based on real people.
Although the use of synthetic data has many advantages, it also presents many challenges.
When synthetic data are created, they often lack outliers. Outliers occur naturally in data, and although they are often removed from training datasets, their existence can be necessary to train truly robust machine learning models. In addition, the quality of synthetic data can vary greatly. Synthetic data are often generated using input or output data, and therefore the quality of the data may depend on the quality of the input data. If the data used to generate synthetic data is biased, the data generated may perpetuate that bias. Synthetic data also require some form of raw data/quality control. It needs to be checked against human annotated data, otherwise the authentic data is some form.
Synthetic data is created programmatically using machine learning techniques. Classic machine learning techniques such as decision trees as well as deep learning techniques can be used. Synthetic data requirements will influence what type of algorithm is used to generate the data. Decision trees and similar machine learning models allow companies to create non-classical multimodal data distributions trained on real data examples. Generating data using these algorithms will provide data that is highly correlated with the original training data. In cases where the typical distribution of the data is known, the company can generate synthetic data using the Monte Carlo method.
Synthetic data generation methods based on deep learning typically use either a variational autoencoder (VAE) or a generative adversarial network (GAN). VAEs are unsupervised machine learning models that use encoders and decoders. The VAE encoder part is responsible for compressing the data into a simpler, more compact version of the original dataset, which the decoder then analyzes and uses to create a representation of the underlying data. A VAE is trained to have an optimal relationship between input and output data when the input and output data are extremely similar.
As for GAN models, they are called “adversarial” networks due to the fact that GANs are actually two networks that compete with each other. The generator is responsible for generating synthetic data, while the second network (the discriminator) works by comparing the generated data with the real data set and trying to determine which data is fake. When the discriminator catches spurious data, the generator is notified and it makes changes to try to get a new data packet from the discriminator. In turn, the discriminator becomes better at detecting fakes. The two networks train against each other, and the fakes become more and more realistic.
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