Synthetic data is artificially generated data created to train, test, or evaluate AI systems, as opposed to data collected from real-world observations. It's becoming critical to AI development as real-world data becomes scarce, expensive, or sensitive. LLMs trained on internet data have largely exhausted high-quality human-written text. The next scaling frontier involves models generating their own training data. Models like GPT-4 generate reasoning traces, code solutions, and conversations at scale, which then train smaller models. This is how distillation works. Synthetic data also solves privacy problems. Medical AI systems need patient data, but privacy regulations restrict access. Synthetic patient records that statistically mirror real patients allow model training without privacy exposure. In computer vision, synthetic environments allow training perception systems on perfectly labeled data. Every pixel is labeled and every scenario is controllable. The risk of synthetic data is distributional mismatch: if synthetic data doesn't capture the real-world distribution accurately, models trained on it fail when deployed. And data generated by models can amplify existing biases if the generator is already biased.
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