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The problem of ‘model collapse’: how a lack of human data limits AI progress

Research suggests use of computer-made ‘synthetic data’ to train top AI models could lead to nonsensical results in future

The use of computer-generated data to train artificial intelligence models risks accelerating their collapse into nonsensical results, according to new research that highlights looming challenges to the emerging technology. 

Leading AI companies, including OpenAI and Microsoft, have tested the use of “synthetic” data — information created by AI systems to then also train large language models (LLMs) — as they reach the limits of human-made material that can improve the cutting-edge technology.

Research published in Nature on Wednesday suggests the use of such data could lead to the rapid degradation of AI models. One trial using synthetic input text about medieval architecture descended into a discussion of jackrabbits after fewer than 10 generations of output. 

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