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  • Mal McCallion

ChatGPT's Performance: A Case of AI Burnout?

Updated: Dec 11, 2023

Is OpenAI's ChatGPT running out of steam? Recent performance fluctuations in this AI chatbot have sparked concerns about potential burnout, raising broader questions about the sustainability of AI models.

A study conducted by Stanford University researchers revealed significant performance variations, or 'drift', in ChatGPT's ability to perform diverse tasks, ranging from solving maths problems to generating software code. This unexpected inconsistency has left many in the tech world scratching their heads.

The study compared two versions of OpenAI's platform: GPT-3.5 and GPT-4. Interestingly, GPT-4's ability to solve maths problems showed a significant decline over just three months, from March to June. In contrast, GPT-3.5 demonstrated an almost opposite trajectory, improving its accuracy over the same period.

Adding to the mystery, ChatGPT's ability to explain its reasoning has also become less apparent over time. In March, the chatbot provided step-by-step reasoning for specific questions, but by June, it ceased to do so without any clear explanation.

The 'black box' dilemma further complicates the situation. Since OpenAI has not made its code open source, researchers and the public have limited visibility into the changes made to the neural architectures or the training data. This lack of transparency makes it challenging to understand the hidden complexities behind these fluctuations.

The potential burnout of ChatGPT could have significant implications. Just last month, it was reported that the chatbot experienced a surprising 9.7% decrease in website traffic in June compared to May. Unique visitors also dropped by 5.7%, and time spent on the site declined by 8.5%, hinting at a possible decline in user engagement.

While some experts suggest that the initial novelty of ChatGPT may be wearing off, others point to the launch of the iOS app in May as a potential reason for the traffic diversion. Regardless of the cause, these developments underscore the importance of understanding and addressing the complexities of AI models to ensure their long-term sustainability.

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