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Miguel Ferreira: Agencies Should Train AI Algorithms With Synthetic Data

Miguel Ferreira, head of data science at CVEDIA, said government agencies seeking to get the most of artificial intelligence, machine learning algorithms and computer vision platforms should understand the value of synthetic data, which could help reduce reliance on real-world data.

Ferreira wrote that synthetic data can reduce the “time and costs involved in training the models because it removes the need for manual collection and labeling.”

He discussed how synthetic data could help agencies reduce bias and integrate new features to platforms that have already been fielded. He also mentioned how CVEDIA could enable agencies to make their algorithms respond to new scenarios and adapt to changes using synthetic data.

Ferreira said agencies facing large volumes of data as a result of the adoption of satellites, sensors and cameras that continuously generate visual data could also benefit from synthetic data.

“The use of synthetic data in computer vision solutions can reduce cognitive overload by extrapolating on important information, so agencies can understand what the data is telling them and where they need to look,” he added.

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