Artificial intelligence is a rapidly growing field. Concerns about the data used to train these systems are developing with the increased use of AI and machine learning in various industries. Personal information is an important part of the information that AI systems need to learn. This raises concerns about privacy and the possibility of using this system to discriminate against people when making decisions about employment, loans, housing, etc. Synthetic data is one solution that researchers have developed to address this problem. Artificially produced data, called synthetic data, mimics the statistical characteristics of real data.
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It can be done by simulating data using algorithms or computer programs based on particular assumptions and settings. The purpose of synthetic data is to create a large and diverse data set that can be used for various purposes, such as testing machine learning models or conducting research studies without compromising the privacy or security of real people or organizations.
In this article, we’ll explore what synthetic data is and how it works.
Privacy Preservation
Privacy protection is one of the reasons driving synthetic data research. Concerns are developing about the data used to train these systems because of how far AI and machine learning have progressed. These algorithms need a lot of data to learn, which is personal information. The system can reveal personal information or discriminate against people when hiring, lending and hosting.
Users can create other versions of data using synthetic data that does not include personal information about real people or organizations, ensuring that your data is secure and discreet. Therefore, synthetic data offers a safe way to conduct research and develop algorithms without jeopardizing user privacy.
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Overcoming cost and availability issues
Creating and maintaining any data set regardless of privacy concerns can be expensive. There may not be enough real world data available in some situations, such as when imaging is used to try to identify a rare medical disease.
According to its proponents, synthetic data can circumvent these problems by filling in gaps in data sets faster and cheaper than acquiring the missing information from the real world, if feasible. Researchers now have a practical means of circumventing data availability and accessibility issues.
create better data
“I want to get away from mere privacy,” says Mihaela van der Schaar, a machine learning researcher and director of the UK’s Cambridge Center for AI in Medicine. “I hope synthetic data can help us create better data.”
In addition to protecting privacy, synthetic data has become a powerful tool for improving data. Synthetic data users can create their data models and use them to produce different iterations of the data. Because they have control over the process, they can ensure that the data generated meets their needs and goals. Synthetic data allows researchers to produce newer, more varied, and more representative data sets.
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How is synthetic data created?
There are various approaches to data synthesis, but they are all based on the same idea. A computer analyzes a real data set using a machine learning algorithm or a neural network to learn statistical correlations. The process then generates a new data set with different data points from the original but with the same associations.
For example, the Generative Pre-trained Transformer (GPT-3) language creation engine studied billions of samples of human-written text. He also evaluated the relationships between the words and created a model of how they fit together. GPT-3 is based on this huge language model. When given a command such as “Write me an ode to ducks”, GPT-3 uses its knowledge of odes and ducks to generate a string of words. The choice of each word is influenced by the statistical probability that it will come after the previous one.
Our opinion
Synthetic data offers a potential alternative for researchers who need large and diversified data sets. But you can’t get real-world data due to cost, privacy concerns, or accessibility challenges. Users can generate other versions of data with synthetic data that does not include any personal information about real people or organizations, ensuring that your data is secure and discreet. Researchers can also model their data using synthetic data and then create different iterations of the data using those models. This gives them control over the output of the generated data. It also ensures that it is personalized for your use and objectives. This opens the door to more precise and exciting AI algorithms and applications. Therefore, it provides considerable promise for researchers in various domains.
More information: Commonly used machine learning algorithms (with Python and R code)
