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We live in a world where artificial intelligence (AI) and machine learning (ML) are no longer terms of the future. Machine learning algorithms surround our current lifestyles. A lot of the apps on our smartphones are equipped with AI.
Artificial Intelligence has penetrated many industries such as manufacturing, agriculture, administration, waste management, transportation, digital marketing, banking & finance, hospitality, healthcare, insurance, retail, and the list goes on. As the market is vast and the opportunities are unlimited, many people have been moving to data science and machine learning as a career. As a result, machine learning algorithms are being created on an enormous scale. However, the machine learning algorithm needs large & accurate data sets that train the algorithm. It is a tedious task to create data sets as per our preferences from real data collected from around the world. This problem has created a bottleneck in the evolution process of AI. The solution to this problem is Synthetic data.
What is synthetic data and synthetic datasets?
As the name suggests, synthetic data replicates real data, but it is generated through algorithms instead of collecting it from actual events. Synthetic data sets can be data sets created by algorithms imitating multiple scenarios in the real world and can be used to train ML models. It can be categorical, binary, or numerical. It can be used for various purposes such as test data for new products or tools, for model validation as well as for AI training purposes. To make synthetic data work more efficiently, the data sets’ length should be arbitrary, and the data should be randomly created. Random noise should be placed in the data set.
Why synthetic data?
Synthetic data can be used instead of real data for AI training purposes. The main benefits of synthetic data are –
No shortage of data – Shortage of real data stands as a major challenge in the development of AI. It is difficult to have real data for all different scenarios due to a lot of factors. Synthetic data can be created through algorithms and in enormous quantities overcoming the issue of data shortage. This is specifically useful in training ML models because of the scarcity in real data availability as per our requirements and specifications.
The privacy of the people – Synthetic data cancels out a massive drawback in real data, which is humans’ privacy. Accurate data capture a lot of private information about humans, and it can lead to policy infringement. Entire data sets of real data have to clear GDPR. Synthetic data is free from any regulations as the unique identifiers in real data, which give out private information of people, are replaced with fake data reducing the risk of privacy infringement.
Avoid data bias – Real data, at times, give out biased outputs due to various reasons like human error during data labeling, increase in data bias as the deep learning model processes more images. Synthetic data can curb the data bias that occurs in real data. This helps in training the ML model to a much higher accuracy.
Pixel perfect annotations – Data labeling is a difficult process when it comes to real data due to issues of clarity as well as human bias. In synthetic data, every item in the image can be clearly annotated to the last pixel. Also, teaching the model from some angles that are tough to achieve in real data. This also helps to increase the labeling model’s accuracy. Automated labeling tools like ADVIT eases the process.
Applications of Synthetic data
Synthetic data has a huge number of applications in training machine learning models. Here are some of the most prominent uses of synthetic data.
Autonomous vehicles – Synthetic data has been a pioneer in training autonomous vehicles. ML models collecting real-time information on the road can be dangerous and still not be enough to cover all aspects of vehicle training. On the contrary, synthetic data can be created according to the required training for the ML model of the autonomous vehicle. Specific scenarios that are not possible or rarely collected in real data, can be synthetically created to train the model faster
Marketing – Individual data is necessary for ML models designed for marketing purposes. Marketing teams use data to run individual level simulations to improve their overall spend. This becomes impossible with real data without the individual’s consent, due to GDPR. However, synthetic data replicating the properties of real data can be used for these purposes.
Robotics – Robotics has grown fast over the past two decades. The use of real data can take too long to train the robotic model. Synthetic datasets created specifically to train the ML model for its robotic purpose are way faster. Many companies are spearheading the robotics industry by training their robotics models through simulations of the real scenario created with synthetic data.
Surveillance – Surveillance ML models need to be trained using synthetic data as real data of criminal acts or unusual incidents is scarce. ML models can be trained to perform security and surveillance tasks with the use of synthetic data containing incidents of a criminal act. The ML model uses computer vision and understands a security hazard.
Fraud Detection – Real data of cases related to fraud is hard to get your hands on. However, this data is essential for training an ML model for fraud detection. Synthetic data can train AI algorithms to detect fraud, especially in sectors like finance & insurance. The algorithm is fed synthetic data containing instances of fraudulent activities along with non-fraudulent activities.
Healthcare – Medical records containing real patient data have certain limitations of use as they contain private information of the patients. Partially synthetic data can be created using medical diagnoses and test results of patients without their personal details. This data helps new models learn the process without infringing on patients’ privacy.
Synthetic Data Creation
Synthetic data created can be of two types – Fully Synthetic & Partially Synthetic.
Fully synthetic data does not need any original data for its creation and re-identification of single units is almost impossible.
While partially synthetic data is generated from existing real data. The unique identifiers in real data are tweaked to protect individuals’ identities in the real data.
Synthetic data can be built using methods like drawing numbers from a description, agent-based modeling & deep learning models. Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) are two main types of synthetic data generation techniques that improve with more data.
Challenges in Synthetic Data
Lack of Outliers – Synthetic data has an array of advantages, but it does come along with some challenges. For instance, Synthetic data often lacks individual outliers that are present in raw data. Some of these outliers are necessary for the higher accuracy of the model.
Quality of input data – Synthetic data is created from an input, and the quality of synthetic data depends on the quality of the input data. Any bias in the input data results in a bias in the output of the ML model’s processing.
Output quality – Synthetic data also needs to have an output quality control to authenticate the process. A single error in the input data can make the output vary by huge margins.
In conclusion, we have understood the basics of real data as well as synthetic data. While synthetic data is essential for the training of various ML models, it cannot be the only training source as the model will not function correctly in real-world scenarios.
To tackle this, we must use a mix of real and synthetic data. This synergy helps in raising the accuracy of the ML model to the maximum. Synthetic data is here to stay and support artificial intelligence growth, and as data scientists and AI model developers, we should be ready to embrace it.