In the defense industry, synthetic data has emerged as a transformative tool for training AI systems.

Yet, despite its growing adoption, there are still many myths surrounding synthetic data for AI in defense. 

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Myth 1: "AI can’t train AI.”

One of the most common synthetic data myths for AI is the belief that ‘AI can’t train AI.’ Early in the generative AI craze, a prevailing concern was that using synthetic data to train other AI models would lead to catastrophic results. However, this is a gross oversimplification. The issue isn’t synthetic data itself but the quality of data being used.

That’s because not all synthetic data is created equal. Due to significant advancements in generative models, creating synthetic data now is far more viable and effective than it was a few years ago. Moreover, synthetic data offers an important advantage: the ability to generate a wide variety of scenarios that are difficult or expensive to capture in the real world. Using synthetic data allows you to patch these gaps in your data distribution. 

According to Nvidia, when properly designed to reflect the shapes and patterns of real-world data, synthetic data can be effective or even better for training an AI model than data based on actual objects, events, or people. This leads us to the next myth: that synthetic data must perfectly replicate the real world to be effective. 

Myth 2: "Synthetic data needs to be 100% accurate to the real world.”

Another prevalent myth about synthetic data for AI in defense is that it must perfectly replicate the real world to be effective. What’s crucial is that the synthetic data mathematically or statistically reflects real-world data. In fact, the features that visual AI systems learn to detect, like shapes, colors, and patterns, are often extremely similar—if not identical—between synthetic real-world data. This means the synthetic data doesn’t need to capture fine details unless they are critical to the specific use case. 

Consider, for example, training an AI system to detect enemy assets in a densely-wooded forest. Synthetic data can simulate the forest environment, but it doesn’t need to replicate every leaf, branch, or shadow with pixel-perfect accuracy. Instead, it needs to provide sufficient visual and contextual information, such as realistic lighting, textures, and object interactions, to enable the AI to learn and apply its knowledge to real-world scenarios. 

In fact, one of the key advantages of using synthetic data is that it doesn’t need to be completely accurate. This is particularly valuable in the defense sector, where synthetic data can mimic real-world scenarios without exposing sensitive or personally identifiable information (PII).

Myth 3: "Models can’t be trained solely on synthetic data.”

A third myth surrounding synthetic data for AI in defense is that models can’t be trained solely on synthetic data. There is some truth to this claim, as having real data in the mix is beneficial. However, nowadays, synthetic data can be made to look highly realistic, which may be sufficient for certain use cases. 

At Younite, we use NVIDIA Cosmos to adjust synthetic data to appear more realistic. This platform accelerates physical AI development by leveraging state-of-the-art models trained on millions of hours of driving and robotics videos, democratizing physical AI development under an open model license.

Our process starts with synthetic data, progresses through AI training with real-world data, and culminates in real-world production use. This allows us to continuously iterate on and refine our AI systems while overcoming data bottlenecks that can hold up the training, testing, and validation of data to develop AI models. 

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Partner with Younite

Younite is an active member of two major alliances and ecosystems in the defense industry, Patria-led eALLIANCE and The Digital Defense Ecosystem. Our experience in producing photorealistic indoor and outdoor environments allows us to generate diverse, realistic scenarios for synthetic data generation. These environments ensure high-quality datasets that meet the rigorous demands for effective AI development and testing. Our solutions drive readiness, improve situational awareness, and deliver a strategic edge in both peacetime and high-stakes defense applications.

Ready to accelerate your AI development? Let’s start a conversation today.

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