A Practical Guide for ML Engineers and Researchers.
The Data Bottleneck in AI
If you’ve been following the AI landscape, you’ve probably noticed a paradox. While large language models are getting bigger and more capable, the high-quality data needed to train them is becoming increasingly scarce.
Consider this: GPT-3 was trained on roughly 500 billion tokens. GPT-4’s training data is estimated to be even larger. But here’s the problem. The internet, despite its vastness, is finite. Researchers at Epoch AI predict we could exhaust high-quality language data for AI training between 2026 and 2032.
Beyond scarcity, there are other challenges. Privacy regulations like GDPR restrict what data can be used. Copyright concerns have led to lawsuits against AI companies. And even when data is available, it’s often biased, outdated, or simply not diverse enough for specialized tasks.
Enter synthetic data: artificially generated information that mimics the patterns and characteristics of real-world data. It’s not just a workaround. For many AI applications, synthetic data is becoming the primary solution to the data bottleneck.