Gen AI Needs Synthetic Data. We Need to Be Able to Trust It

Artificial Intelligence (AI) today has come a long way, with models like ChatGPT and Gemini showcasing significant advancements. However, these AI systems have been trained primarily on real-world data available, and even with all the content on the internet, they still fall short in handling every possible situation.

To continue their growth and evolution, these AI models need supplementary training elements, such as synthetic data or simulated data. Synthetic data are computer-generated scenarios that are not real but plausible, allowing AI to learn and respond to various hypothetical situations it might encounter in the future.

One prominent example of the usage of synthetic data in AI training is the DeepSeek AI model, developed in China, which was primarily trained on synthetic data. By utilizing synthetic data more extensively, DeepSeek AI has saved on processing power and data collection costs.

However, the adoption of simulated data for training AI models is about more than just cost savings, as it permits AI to learn from scenarios that exist only within the synthetic data, but not in the real-world information they’ve been provided.

The key challenge lies in ensuring that AI models relying on synthetic data can be trusted to accurately respond to real-life situations. The problem with simulated data arises from the fact that unrealistic training scenarios could lead to surprising or undesirable AI responses when applied to real-world scenarios.

For instance, the reactions of a self-driving car trained only on simulated data to a swarm of bats, a rare incident not typically present in real-world data, could be unpredictable, potentially leading to accidents. A similar concern arises with AI systems that haven’t been properly grounded in real-world scenarios, being detached from the reality they’re meant to emulate.

Oji Udezue, who has led product teams at Twitter, Atlassian, Microsoft, and other companies, expressed that building reliance on AI models can be achieved by removing the notion of edge cases, under the assumption that the model can be trusted.

Trusting AI models necessitates several factors, such as verifying the AI’s ability to react accurately to real-world situations and providing feedback on any disparities between synthetic and real-world scenarios.

The issue of trustworthy AI training and models also highlights the potential risks and ethical considerations involved in the use of simulated data. As AI tools become increasingly popular and sophisticated, it opens the door for various potential misuses and impacts on both individuals and society.

To build reliable AI systems using simulated data, it is imperative to prioritize transparency, trust, ethics, and error correction. This includes making the underlying training models transparent, incorporating user evaluation of models, keeping ethics and risks in mind, and employing error-correction techniques.

Additionally, ensuring that AI training models remain up-to-date by reflecting real-world diversity and addressing any errors in synthetic data is essential for maintaining the accuracy of AI responses.

Ultimately, it is the collective responsibility of AI developers and users to activate best practices for training AI models, focusing on reliability, transparency, ethics, and error correction. In doing so, AI applications using simulated data can be developed with confidence, ensuring their success and safety for all users.