It seems like this is a transcript of a conversation between two individuals, likely experts in AI or machine learning. The discussion revolves around the potential of AI to solve complex problems and the challenges associated with it.

Some key points from the conversation are:

  1. Recurrent Neural Networks: One speaker mentions that recurrent neural networks will become more prevalent as computing power increases, allowing for more efficient problem-solving.
  2. Expertise in LLMs (Large Language Models): Another speaker highlights the importance of domain-specific knowledge in AI models and suggests that incorporating human expertise can help overcome limitations.
  3. Overcoming Expert Disagreements: The conversation touches on the challenge of reconciling disagreements between experts, even when they’re interacting with a model. The solution proposed involves using machine learning to identify better versus worse outcomes, rather than correct versus incorrect ones.
  4. Capturing Domain Knowledge: One speaker suggests leveraging existing Standard Operating Procedures (SOPs) and recipes in semiconductor manufacturing as a way to capture domain knowledge.
  5. The Role of Human Experience: Another speaker emphasizes the value of human experience and anecdotes in uncovering novel solutions, even when SOPs are available.

Overall, the conversation underscores the potential benefits and challenges of AI-driven problem-solving, as well as the importance of incorporating human expertise and experience to overcome limitations.

Translation

原文似乎是一场对话，两人很可能是专家，他们讨论的是人工智能解决复杂问题的潜力，以及与之相关的问题。 讨论中一些重要点： 1. **循环神经网络**：一人提到随着计算能力增加，循环神经网络将更加普遍，能够更高效地求解问题。 2. **LLMs（大型语言模型）专长**：另一人强调人工智能模型中域知識的重要性，并建议通过融合人类专长来克服限制。 3. **解决专家意见不一致的问题**：对话触及了专家即使与模型交互时，也存在意见不一致的问题。提议的解决方案是使用机器学习来识别更好还是更差的结果，而不是正确或错误的结果。 4. **捕捉领域知识**：一人建议利用半导体制造中的标准作业程序（SOP）和食谱作为一种方式来捕捉领域知识。 5. **人类经验在解决方案中的作用**：另一人强调了人类经验及其故事在揭示新颖解决方案方面的价值，即使存在着SOP。 总体而言，对话突出了人工智能驱动问题求解的潜力和挑战，以及融合人类专长和经验以克服限制的重要性。

Reference:

https://www.youtube.com/watch?v=AMyuvkwasY8

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