A leading entrepreneur claims: “All human-generated data for AI model training has been exhausted.” Recent research has indicated that human-generated data is likely to run out within the next two to eight years.

Going by these claims, data is a core component of AI training requirements. That said, more technology companies are shifting away from real-world data to train their AI models. 28% of companies adopting AI technology identified “limited data access” as the reason behind failed deployments.

As an alternative, organizations are shifting to synthetic data to train sophisticated AI models. Generated by AI-powered algorithms, synthetic data is a more accessible and cost-effective solution useful for training various types of AI models – apart from ensuring both data security and integrity. Gartner predicts that by 2030, synthetic data will replace real-world data as the primary form of data used for AI model training.

Here’s a deep dive into how a synthetic data generator can transform AI model training.

Role of synthetic data in the AI domain

Synthetic data generation tools are crucial in the AI space to overcome challenges like data scarcity, inaccessibility, and bias. While real-world data may contain sensitive information, synthetic data is “artificially-generated” and does not pose any privacy issues.

Here are some of the benefits of using a synthetic data generator in AI model development as opposed to real-world data:

1. Generates massive data volumes.
   AI models are more dependent on rich and diverse datasets for delivering an optimum result. Synthetic data generation tools can create massive data volumes and diverse datasets for AI models. Here’s an example of how Waymo is leveraging an AI data generator to simulate camera data before using it in self-driving vehicles.
2. Ideal for rare event simulation.
   As compared to real-world data, a synthetic data generator can simulate rare events more accurately for AI models. It can automatically label or “annotate” any object in a rare event scenario – thus making it more cost-efficient than manual data labeling. Here’s an example of how JP Morgan Chase generated sample payment data for fraud detection.
3. Useful for non-visible data.
   Visual data such as infrared images or radar captures are not visible to human users, thus making them difficult to annotate manually. With synthetic data, AI models can be trained on such non-visible data. Here’s an example of implementing sensor fusion using synthetic data in Simulink.
4. Eliminates data bias.
   In traditional AI training, data bias is a concern that can distort the model output and present an inaccurate picture. Synthetic data eliminates data bias by diversifying the input datasets to be more representative. Through regular audits of AI models, enterprises can rectify any bias in synthetic datasets.

How Kingfisher is democratizing synthetic data generation

Onix’s Kingfisher synthetic data generator tool is designed for enterprises with limited access to real-world data or privacy challenges. This AI-powered tool can utilize production data and code to generate synthetic datasets identical to real-world datasets. For example, healthcare companies with limited clinical data can use Kingfisher to generate medical records.

Effectively, Kingfisher provides a low-risk environment for enterprises to create, test, and train AI models for their specific requirements without compromising privacy. Here’s what makes Kingfisher ideal for widespread usage in the business domain:

• High data availability – By mimicking real data, Kingfisher ensures high data availability for AI model training (even in situations of data scarcity).
• Zero privacy violation – With Kingfisher, data-dependent companies can still comply with regulations like GDPR and CCPA – while reducing the risk of data breaches.
• Massive scalability – Kingfisher can scale up data volumes (from thousands to millions), thus ensuring AI models have the right data whenever needed.
• No data gaps – The Kingfisher tool supports both data-driven and logic-driven techniques to generate synthetic data, thus enabling companies to overcome any form of data gap.

Conclusion

As more enterprises rely on AI model development, they cannot simply depend on real-world data to train their models for the best results. Synthetic data generators are a viable alternative for effective AI model training purposes.

With the recent launch of its Kingfisher tool, Onix is democratizing the process of synthetic data generation. This AI-powered tool can help enterprises generate high-quality datasets to accelerate their AI development.

Our latest eBook, “Democratizing data access with synthetic data” details the real-world applications of synthetic data – and how Kingfisher can overcome the challenges in synthetic data generation.

Do you want to explore how Kingfisher can overcome your data accessibility challenges? Download this eBook today.

Reference links: 

Tech companies are turning to ‘synthetic data’ to train AI models – but there’s a hidden cost 

Training AI Models with Synthetic Data: Best Practices

Exploring Synthetic Data: Advantages and Use Cases

Kingfisher: The Synthetic Data Generator