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Why Synthetic Data Is Key to Paving the Way for Smart Cities

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Illustration of a futuristic smart city.

The potential for uniting synthetic and real-world data in the pursuit of smart cities is highly promising for the future of smarter AI.

Credit: Getty Images

Gartner predicts synthetic data will overshadow real-world data in artificial intelligence (AI) models by 2030. It saves valuable time and energy while also providing the opportunity to counteract incumbent bias in the base data.

Of course, model training should not be limited to one data source or the other. Synthetic and real-world data can be complementary in efficiently progressing training at a lower cost, with greater applicability and without privacy or bias issues. But, it stands that synthetic data is essential for developing smarter AIs capable of recognizing and preventing corner cases in smart cities.

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