The author also raises the question of whether the mistakes made by the model are distinct from those made by junior programmers. They provide an example where they asked the model to translate a Perl script into Lua, and the model returned a nonsensical response. The author suggests that the model's ability to understand the original code, combined with its inability to write a competent translation, might be a red flag indicating the output of an LLM.
Key takeaways:
- The author discusses the potential issue of distinguishing between human programmers and AI models like GPT-4, especially those who might use these models to avoid work.
- GPT-4, while able to provide clear explanations in English, made significant mistakes when asked to write code, particularly when translating from Perl to Lua.
- The author suggests that the pattern of mistakes made by AI models like GPT-4 could be different from those made by junior programmers, and could potentially be used to identify AI-generated code.
- The author also raises the question of whether the AI's ability to understand complex code but inability to accurately translate it could be a red flag for identifying AI-generated code.