GPT-4 has become so lazy that people are faking disabilities to try to make it perform as it used to.

The article compares the process of developing neural networks, particularly large language models (LLMs) like GPT-4, to directed evolution in a lab. The author explains that these models have fixed capabilities once trained, with any changes or enhancements requiring manual intervention. This results in new 'checkpoints' or snapshots of the network's abilities. GPT-4 is seen as a system of specialized LLMs, which may exhibit emergent behaviors but are still limited by their last update.

The article also discusses the potential for a self-tuning LLM capable of autonomous learning, which could bring us closer to Artificial General Intelligence (AGI). This would necessitate an AI with access to long-term memory and the ability to self-modify. However, such rapid and potentially unpredictable evolution could be hazardous, something OpenAI aims to avoid. The author humorously suggests that this is why we sometimes see 'brainfarts' in GPT-4 checkpoints.

Key takeaways:

Neural networks, like GPT-4, operate on fixed capabilities after training, with any changes requiring manual intervention and resulting in new checkpoints.
GPT-4 is thought to be a system of various specialized large language models (LLMs), which may lead to emergent behaviors but are still constrained by their last update.
The idea of a self-tuning LLM, capable of autonomous learning, could bring us closer to Artificial General Intelligence (AGI), but could also lead to unpredictable or hazardous evolution.
OpenAI is cautious about the potential risks of rapid and unpredictable AI evolution, which could sometimes result in GPT-4 checkpoints with errors or 'brainfarts'.

GPT-4 has become so lazy that people are faking disabilities to try to make it perform as it used to.

Key takeaways:

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