Tech Firms Turning to 'Synthetic Data' to Train AI Models - But There's a Hidden Cost

The quality of data is crucial as poor data leads to poor outputs, in the same way using low-quality ingredients can produce low-quality meals.

- James Jin Kang

Last week, Mr. Elon Musk, the billionaire owner of social media platform X, claimed the pool of human-generated data that is used to train artificial intelligence (AI) models such as ChatGPT has run out.

Mr. Musk didn't cite evidence to support this. But other leading technology industry figures have made similar claims in recent months. And earlier research indicated human-generated data would run out within two to eight years.

This is largely because humans can't create new data such as text, video and images fast enough to keep up with the speedy and enormous demands of AI models. When genuine data does run out, it will present a major problem for both developers and users of AI.

It will force tech companies to depend more heavily on data generated by AI, known as "synthetic data." And this, in turn, could lead to the AI systems currently used by hundreds of millions of people being less accurate and reliable - and therefore, useful.

But this isn't an inevitable outcome. In fact, if used and managed carefully, synthetic data could improve AI models.

PROBLEMS WITH REAL DATA

Tech companies depend on data - real or synthetic - to build, train and refine generative AI models such as ChatGPT. The quality of this data is crucial. Poor data leads to poor outputs, in the same way using low-quality ingredients in cooking can produce low-quality meals.

Real data refers to text, video and images created by humans. Firms collect it through methods such as surveys, experiments, observations, or mining of websites and social media.