by Shashank
Introduction:
Today, the AI models are performing so well because we have this huge data to train those models to support the user as they want and also make it easy for the user to do almost anything. But this comes with a cost of privacy. Where does this huge training and testing data comes from? The easiest answer is "from us". Everything that we are doing online, everything that we are doing digitally is somehow creating a data, and that’s being fed to the AI models to work better with our lives.
Issue with Data:
Developing successful AI and ML models requires access to large amounts of high-quality data. However, collecting such data is challenging because there are certain issues with that.
Privacy:
To solve many medical problems and other problems, we need access to sensitive customer data such as personal health data from wearable devices, smartphones, and personal information from documents and different websites. Collecting all this data creates a privacy concern for the users. Leakage of this private data can cause a huge problem everywhere. For this reason,
privacy regulations such as GDPR and CCPA restrict the usability and also collection of personal data and use them for training AI models.
Cost:
Some types of data are much costly or rare. Assume that we need all of the data for the road, traffic signals, and other things in order to create a self-driving car model. Before this idea, no one would have thought to click images or videos of every corner of their country. So that was costly in the sense of collecting and cost. Another example is for fraud data for different banks, there are thousands of banks and there are thousands of different rules so to find fraud data which is also rare is a pretty heavy task. Furthermore, we cannot train the model for each bank separately.
For all these problems the synthetic data generation comes to the rescue, that’s what we are going to discuss next.
Synthetic Data:
Before we get into the different synthetic data generation models and techniques, we need to understand what synthetic data is and how we use it to create AI and ML models.
What is Synthetic Data?
Synthetic data is data that is created artificially rather than generated by real-world events. There are certain rules and ranges for creating each sector's data in synthetic data generation and also different ideas for creating synthetic data.
Categories:
There are three broad categories to choose from, each with different benefits and drawbacks:
Fully Synthetic Data: In this data generation process it doesn’t contain any original data from real-world. That means there is no possibility of identifying any single unit.
Partially Synthetic Data: This partial word is represents what this process actually does but in a different way. The only data that is sensitive or may cause privacy issues are replaced with synthetic data. This requires a heavy dependency on the imputation model. But there’s an issue too, since generating the synthetic data as a replacement might also represent the actual data we had.
Hybrid Synthetic Data: Hybrid synthetic data is a mixture of both real-world data and synthetic generation processes. This process requires understanding the co-relation between data features and the underlying relations between them. After getting all the features and data points. the closest data points are selected as the synthetic data.
For creating AI/ML models now, it needs a huge amount of data, where synthetic data comes to the rescue. This synthetic data generation can also be used for creating specified condition data that are not available in real data or maybe can not be created from scratch.
General Methods for Creating Synthetic Data
Drawing Numbers from a distribution - In this method the model observes the statistical distributions and reproduce the synthetic data with that observation.
Agenda-based modeling - To create synth
etic data with this method, a model is created that can understand the observed behaviour and then reproduce random data with the same model.
Deep Learning models - Variational autoencoder and generative adversarial network (GAN) models are synthetic data generation techniques that improves the utility with feeding more data to it.
Generative Adversarial Network [ GAN ]
In 2014, Ian Goodfellow introduced GAN or Generative Adversarial Neural Networks. These networks are composed of one discriminator and one generator work. While the genera
tor network generates new images that are similar to the real world images, the discriminator tries to classify them.
One interesting fact about this network is these networks build new nodes and layers to learn to become more better in their certain tasks.
Challenges of Synthetic Data
Synthetic data has a huge usability in today’s world but that has a limitation too:
Missing Outliers: Synthetic data is not a copy of the real data, it is a mimic of the real data. So it may not have the outliers that could possibly be a usable point for an analysis.
Quality of Data: As we can create synthetic data that doesn’t mean it can replace the usability of the actual data. Sometimes if the source of the real data is not controlled that creates a mess in the synthetic data.
Output Control: After creating a synthetic data there is a validation process where we have to validate the synthetic data and the actual data to ensure it has the same quality as the actual data.
Conclusion :
Synthetic Data generation and creating models for different tasks this is still a new concept for maximum people out there. So there is a obvious question that is - If users that are going to use those model facilities are going to accept this process, if they will trust some medical field model that was trained on synthetic data that is not from some other users. Before they use it and get benefit it will be hard to make them understand the whole concept and also prove this data can actually work somehow same as the real data we used to work with.
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