Unlocking the Potential of Synthetic Data for AI Development

Data is the bane of existence for AI models, and the accuracy and effectiveness of the AI systems significantly depend upon the completeness of the data used during the training. Although real data undoubtedly makes AI systems more effective, there are certain challenges, as the real data can be imbalanced, biased, or incomplete.

Hence, to cope with the shortages in real data, data scientists have to source synthetic data. Synthetic data is considerably more inexpensive than real data, but there are still some challenges, like ensuring demographic diversity, reliability, and sufficient volume accumulation, which data scientists must mitigate.

Understanding Synthetic Data

As the name implies, synthetic data isn’t collected from real-life occurrences. Instead, it mimics the characteristics of the original data and is sourced from various data generation techniques, algorithms, and models. Although synthetic data closely resembles real data, it never contains actual values from the original datasets. More precisely, it’s made-up data.

A more qualitative definition is that it is a type of data statistically identical to real-world data but algorithmically generated. Experts prefer synthetic data for three: it poses fewer privacy risks for organizations, decreases the turnaround time for model training and validation, and can significantly prove helpful for testing new products (as using production data is illegal). It can also help increase model explainability by eliminating bias and facilitating the stress-testing phase.

Although synthetic data is a relatively new concept, its future importance can be estimated by Gartner’s quote, “The most valuable data will be the data we create, not the data we collect.” Furthermore, by 2030, most AI models will be trained on synthetic data.

The Comparative Merits of Synthetic Data Versus Real Data

Synthetic data is rapidly becoming an industrial inclination. According to current statistics, about 60% of models are trained and validated via synthetic data sources. However, to decide which one will fit your needs, it is important to know the pros and cons of both.

The Benefits Comparison of Real and Synthetic Data

The Drawbacks Comparison of Real Data VS Synthetic Data

Common Techniques Used for Synthetic Data Generation

Synthetic data is sourced from various techniques; however, the most fundamental ones are mentioned below.

Using Deep Learning

Generative AI is one of the most popular means of creating synthetic data. These deep neural networks consist of GPTs, GANs, and VAEs, comprehending the underlying distribution in real data and trying to mimic it in the synthetic substitute.

Using the Statistical Distribution

This approach is particularly useful when the real data for a specific domain is unavailable. However, the data analysts have a keen observation of real-world statistical distributions. By using their domain knowledge, they can produce a random sample of any distribution like Chi-square, t, lognormal uniform, etc. However, it is critical to note that the accuracy of the resultant data highly depends on the domain understanding of the expert directing the synthesis.

Fitting the Data to a Known Distribution

Unlike the prior case, if there is real data available for the desired task, then businesses can use the Monte Carlo method to fit it into a known distribution. Although this method can help organizations find the best-fitting distribution, its compatibility with the industrial requirements is not always guaranteed. In such situations, Machine Learning models can be used to find the best-fit distribution.

Challenges in the Current Methodologies and Their Solutions

The deployment of deep learning models, especially GANs, is necessary because the general-purpose large language models could not provide the needed data accuracy. Deep Learning models solve most problems previously portrayed in synthetic data generation techniques.

However, they are still prone to overfitting, require more computation overheads, and may lack the ability to create realistic patterns in the data. To mitigate these issues, experts recommend implementing regularization techniques to prevent overfitting and pre-training the model on a resembling dataset to improve generalization.

Creating synthetic data based on statistical distribution poses challenges like mimicking the precise distribution and maintaining the correlations. To attenuate these complications, it is suggested that diverse statistical models be employed to capture complex relations.

Monte Carlo is a popular method for finding the right statistical distributions, but since it uses ML algorithms, it is susceptible to overfitting. Here, the actionable strategy is to deploy hybrid synthetic data generation techniques. In this approach, one part of the data is generated from theoretical information, and the rest is derived from the available data.

Wrap Up

Synthetic data is rapidly becoming a viable source for training and testing AI models as it is more intelligent and scalable than real data. However, as this new discipline unfolds, many challenges and risks must be addressed.

These include the absence of standardized tools, discrepancies between synthetic and real data, and the extent to which machine learning algorithms can effectively utilize imperfect synthetic data. However, this doesn’t neutralize the importance of the real data because there always has to be a source that can be weaved into something reasonable and accurate.