AI is stepping up its game and it should. With the evolution of AI, every sector of science, technology, and fashion is rapidly booming!

Before getting into this, let’s first understand: What is Machine Learning?

Machine learning is a subset of artificial intelligence (AI), defined as a machine’s ability to mimic intelligent human behavior. AI systems are designed to process information, learn from data, and make decisions that parallel human cognition. In simple words, they are mostly mimicking human patterns.

Machine learning begins with data, which can be in numbers, photos, text, code, or other forms.

To get started with a machine learning model, data is a necessary tool! It’s like fuel to your car. In a data-driven technology,top-quality data will optimize your performance.

According to Melody Chien, “Data quality is directly linked to the quality of decision-making,” However, sometimes real-world datacannot be up to the expectations. Collecting and labeling data is not only time-consuming but sometimes it’s inaccurate and poses a safety risk!

Some common data quality indicators are:

• Real data is so vast that it cannot be fathom and scalable
• Real data sometimes it’s inaccurate and has implications such as security concerns, not programmed, and inconsistent format.
• Capturing and storing irrelevant data increases an organization’s security and privacy risks.
• Manually data registering is a tedious task!

The Rise of Synthetic Data in AI

Gartner predicted by 2024, 60% of the data used for developing AI and analytics will be artificially produced and with that, the mighty invention of synthetic data occurred.  As the world grappled with the limitations of real-world data, innovators and scientists turned to machine learning models to generate artificial data that could mimic the real thing.

Introducing “The Protagonist”: Synthetic Data

Synthetic data is no less than a hero. It has been extensively utilized in various sectors due to its ability to bridge gaps, especially when real data is either unavailable or must be kept private due to privacy or compliance risks. Synthetic data has numerous applications across various fields, including machine learning, data analysis, and software testing.

In machine learning, synthetic data is particularly useful when obtaining sufficient real-world data is challenging or impractical, enabling the training of models while ensuring the privacy of individuals and compliance with data protection regulations.

The generation of synthetic data involves using algorithms to create datasets that mirror the patterns, structures, and relationships found in authentic datasets, commonly employing techniques such as statistical modeling, generative adversarial networks (GANs), and differential privacy methods.

The resulting synthetic data is invaluable in training machine learning models, allowing them to learn and generalize from the artificial data before being deployed in real-world environments.

How does Generative AI create Synthetic Data?

GPT: Generative Pre-trained Transformer

We all have been familiar with GPT! Well, this language model is trained on extensive amounts of tabular data. GPT understands and replicates patterns present in the data. As a result, GPT-based synthetic data generation tools can create realistic synthetic tabular data that is valuable for:

• Augmenting existing tabular datasets.
• Creating realistic tabular data for machine learning tasks.

GPT has an amazing ability to generate realistic synthetic data as it can learn from the patterns and relationships present in the training data. It also allows GPT to produce synthetic data that is similar in structure making it ideal for AI-powered data solutions.

VAEs: Variational Auto-Encoders

VAEs employ an encoder and a decoder to generate synthetic data. The encoder summarizes the patterns and characteristics present in real-world data, meanwhile, the decoder transforms this summary into a lifelike synthetic dataset.

VAEs are particularly useful for generating fabricated rows of tabular data that reflect the same rules and patterns as their real counterparts. This is because the encoder and decoder work together to capture the underlying structure of the real data and reproduce it in the synthetic data.

Key Features and Benefits of Synthetic Data

1. Preventing Bias and Ensuring Fairness

Synthetic data helps to prevent discriminatory outcomes and foster fairness in decision-making. For example, banks can use synthetic data to develop a more equitable credit scoring model, including a wider range of features that reduce bias against historically marginalized groups.

Synthetic data also helps organizations maintain data security by replicating the characteristics and patterns of real-world data. For example, a healthcare organization can use synthetic data for disease diagnosis models, making it fully discreet of actual patient data while achieving accurate results.

2. Promoting Collaboration and Knowledge Sharing

Now since, it eliminates the fact of exposing confidential information, synthetic datacan be used in teams and organizations, providing greater collaboration and promoting knowledge sharing. This helps organizations to collaborate on data in a completely anonymous and secure manner.

Synthetic data is used to create a virtual replica of the database, which is then exploded tested shared documented with the shareholders. This way teams can experiment securely plus, there would be control over the actual data.

3. Cost Effective and Resource Efficient

As we see, real data collection is usually costly, takes a lot of time, and requires intensive resources. By leveraging synthetic datasmall businesses or even start-ups can perform complex analyses that would be extremely expensive or time-consuming.

Synthetic Data also eliminates the need for expensive hardware or software, allowing organizations to redirect the resources toward another critical business area.

The advantages of synthetic data extend far beyond privacy concerns. Synthetic data is poised to have a profound impact on data management, governance, and strategic decision-making at the C-level.

In an interview, Alexander Linden stated:

“Synthetic data can increase the accuracy of machine learning models. Real-world data is happenstance and does not contain all permutations of conditions or events possible in the real world. Synthetic data can counter this by generating data at the edges, or for conditions not yet seen.”

Gartner predicts that by 2030, synthetic data will be used much more than real-world data to train machine learning models and that would be revolutionizing!