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Artificial intelligence (AI) may be eating the world as we know it, but experts say AI itself is also starving — and needs to change its diet. One company says synthetic data is the answer.
“Data is food for AI, but AI today is underfed and malnourished,” said Kevin McNamara, CEO and cofounder of synthetic data platform provider, Parallel Domain, which just raised $30 million in a series B round led by March Capital. “That’s why things are growing slowly. But if we can feed that AI better, models will grow faster and in a healthier way. Synthetic data is like nourishment for training AI.”
Research has shown that about 90% of AI and machine learning (ML) deployments fail. A Datagen report from earlier this year pointed out that a lot of failure is due to the lack of training data. It found that 99% of computer vision professionals say they have had an ML project axed specifically because of the lack of data to see it through. Even the projects that aren’t fully canceled for lack of data experience significant delays, knocking them off track, 100% of respondents reported.
In that vein, Gartner predicts synthetic data will increasingly be used as a supplement for AI and ML training purposes. The research giant projects that by 2024 synthetic data will be used to accelerate 60% of AI projects.
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Synthetic data is generated by machine learning algorithms that ingest real data to train on behavioral patterns and create simulated data that retains the statistical properties of the original dataset. The resulting data replicates real-world circumstances, but unlike standard anonymized datasets, it’s not vulnerable to the same flaws as real data …