AI is trained on existing data. Since tools like ChatGPT began publishing sometimes erroneous data, AI has been trained on that too. Now it seems that AI is generating data specifically to train AI. What is the probability that errors will produce errors more frequently?
https://www.theregister.com/2023/06/16/crowd_workers_bots_ai_training/
AI is going to eat itself: Experiment shows people training bots are using bots
Workers hired via crowdsource services like Amazon Mechanical Turk are using large language models to complete their tasks – which could have negative knock-on effects on AI models in the future.
Data is critical to AI. Developers need clean, high-quality datasets to build machine learning systems that are accurate and reliable. Compiling valuable, top-notch data, however, can be tedious. Companies often turn to third party platforms such as Amazon Mechanical Turk to instruct pools of cheap workers to perform repetitive tasks – such as labeling objects, describing situations, transcribing passages, and annotating text.
… But an experiment conducted by researchers at the École polytechnique fédérale de Lausanne (EPFL) in Switzerland has concluded that these crowdsourced workers are using AI systems – such as OpenAI's chatbot ChatGPT – to perform odd jobs online.
Training a model on its own output is not recommended. We could see AI models being trained on data generated not by people, but by other AI models – perhaps even the same models. That could lead to disastrous output quality, more bias, and other unwanted effects.
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