Training Machine Learning Models Through Synthetic Data

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Why it matters: Artificially generated data to be used to train AI systems to conquer the user’s privacy concerns about the personal data while upholding the model needs that require high-quality data to generate solutions. Synthetic data is cheap, easy to produce and has unique benefits. The trend of using this data type is quickly expanding in the field of data science. 

Every Internet giant is gathering more personal data from the users to make more effective and efficient AI models, the more a company takes the data the more it faces criticism from its users. Synthetic data offers a solution to this problem. 

How it works: Synthetic data is the form of data that is generated artificially using computer programs instead of being composed through the documentation of real-world events. 

For example, a computer vision system needs data in the form of photos of real people pulled off the internet or taken manually, but the same model can be trained using the synthetic data in the form of artificial and fake faces only drawn from real images instead of having them in use directly. 

According to Yashar Behzadi, the CEO of Synthesis AI, artificial data allows training systems in a completely virtual domain. The company has been generating synthetic data for computer models and is updating and converting its AI systems to fully work on artificially created data.

Details: Synthetic data has been in use for the training of robots, self-driving vehicles, security, fraud protection and healthcare. These domains often need high precise data for training the machine learning models. For instance, the synthetic data of a precise 3D position of an object which might be impossible to have in the real world can be easily drawn from the artificial dimensions.  

Synthetic data also comes handy when there is a need to generate new data from existing one. For example, if the data of darker or a lighter skin tone is needed to train the model, one just models the distribution and can equally represent the needed category. 

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Benefits: Being able to produce data artificially may seem like an easy way of generating a large amount of desired data but actually the synthetic data only replicates and adopts some of specific properties of the real data. 

However, synthetically produced data has definitely its own benefits. 

The major benefit of this data is that it is free from privacy rules and regulations because many times the real data faces constraints due to privacies on acquiring personal information. 

Sometimes AI models need data that is not available or doesn’t exist in the real world, synthetic data becomes the only solution. 

Synthetic data gives solutions to some common statistical limitations, it can fill the voids of a real data. 

Challenges: Synthetic data only imitates the real-world data, it is not the exact copy of it. Therefore, this is very difficult for synthetic data to have all the dimensions and stats that a real-world data set contains. 

Working efficiency of a model is highly correlated with the quality of the data source. Artificial data may reflect some biases in source data. 

Synthetic data generation requires time and effort. 

Bottom line: No doubt the synthetic data has some limitations but it can beat the real thing in many dimensions, but only if designed the right way.

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