AI Is Eating Its Own Tail, According to a New Study

A recent study indicated that AI runs the risk of “eating its own tail” and was released on the pre-print arXiv server. This is the practice of training artificial intelligence language models on data that was produced by those same models. As a result, the quality of the output from these models can deteriorate as they start to learn from their mistakes and repeat them.

In the study, text regarding English architecture was generated using a language model called OPT-125m. After that, the model was trained using both this material and a corpus of human-written text. The researchers discovered that the model’s output got worse over time as it started to pick up and repeat mistakes from the AI-generated text.

The researchers issue a dire warning about the potential consequences of this occurrence for the development of AI. AI language models run the risk of becoming more unreliable and erroneous if they are not adequately trained on high-quality data. This could cause issues in a variety of applications, including chatbot creation, news generating, and machine translation.

The results of the study emphasize how crucial good data curation is for AI language models. It is crucial to make sure that training data is precise, impartial, and representational of the real world in order to avoid model collapse.

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