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What is Synthetic Data?

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Engineering Notes · AI Systems

Facing a shortage of fresh human text, the lab trained its latest model largely on synthetic data.

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Overview

Synthetic data is training data generated by a computer rather than collected from the real world, and in AI it usually means data written by another AI model. It gets used when real data is scarce, private, or too expensive to label. It works well enough to be standard practice and badly enough to be argued about.
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Overview

Synthetic data is training data a machine made up instead of data anyone collected, usually one AI writing lessons for another. It gets used when real data is scarce, private, or too pricey to label. It works, and it is argued about, because a model taught by a model inherits that model's mistakes. 😎

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Detail

Synthetic data exists because real data runs out, costs too much to label, or cannot legally be used. Start with the privacy case: a hospital that cannot share patient records can generate artificial patients with similar statistics and share those instead, since no real person is in the file. The scarcity case is bigger, because labs training on human text have been working through a finite supply, so they increasingly have models write training material for the next model. Done carefully this is genuinely useful, as generated examples can be aimed squarely at a model's weak spots, which beats scraping another mountain of what it already knows. Done carelessly it degrades, because flaws and blind spots in the generator get inherited and amplified by whatever learns from it, a failure researchers call model collapse. The safeguards follow directly from that risk: keep real data in the blend, filter the generated batch, and test the result against reality rather than against its teacher.
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Detail

Real data has three problems: it runs out, it costs a fortune to label, and a lot of the good stuff is legally radioactive. So labs manufacture their own. Self-driving teams do the vivid version, because nobody has ten thousand real clips of a mattress bouncing off a truck at night, so they generate them and train on those. At the frontier the scale is different, because the supply of human-written text is running out, so labs now have models write fresh training material for the next model. The upside is aim, because you can generate exactly the cases your model keeps failing instead of scraping another mountain of what it already knows. The downside is inbreeding, because every flaw in the generator gets passed down and amplified, which researchers politely call model collapse. Hence the house rules: keep real data in the mix, filter hard, and grade the student against reality instead of against its teacher. 😎

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Analogy

Flight simulators for training pilots. Real emergencies are rare, dangerous and impossible to schedule, so airlines manufacture them: engine failures on demand, any storm you like, a hundred times a week. Pilots trained this way fly better, because they have met situations a normal career never supplies. But a simulator only contains what its builders thought to put in it, which is why nobody lets pilots skip real flying hours.
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Analogy

Practicing tennis against a ball machine instead of a person. The machine gives you a thousand perfect reps nobody could schedule, and you genuinely get better. It also never disguises a serve, never rushes the net, never does the weird thing a real opponent does. Train only against the machine and you become excellent at beating the machine.
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Formal definition — The same term, explained the usual way

Synthetic data is algorithmically generated data used in place of, or alongside, empirically collected data, commonly produced by generative models for training or evaluation. It addresses scarcity, labeling cost and privacy constraints and permits targeted coverage of underrepresented cases; recursive training on generated output risks distributional degradation, termed model collapse, motivating retention of authentic data, filtering and external validation.

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