The startling success of ChatGPT and other large language models (LLMs) using
transformer-based generative neural network architecture in applications such
as natural language processing and image synthesis has many researchers excited
about potential opportunities in process systems engineering (PSE). The almost
human-like performance of LLMs in these areas is indeed very impressive,
surprising, and a major breakthrough. Their capabilities are very useful in
certain tasks, such as writing first drafts of documents, code writing
assistance, text summarization, etc. However, their success is limited in
highly scientific domains as they cannot yet reason, plan, or explain due to
their lack of in-depth domain knowledge. This is a problem in domains such as
chemical engineering as they are governed by fundamental laws of physics and
chemistry (and biology), constitutive relations, and highly technical knowledge
about materials, processes, and systems. Although purely data-driven machine
learning has its immediate uses, the long-term success of AI in scientific and
engineering domains would depend on developing hybrid AI systems that use first
principles and technical knowledge effectively. We call these hybrid AI systems
Large Knowledge Models (LKMs), as they will not be limited to only NLP-based
techniques or NLP-like applications. In this paper, we discuss the challenges
and opportunities in developing such systems in chemical engineering.

通过使用基于 transformer 的生成神经网络架构的 ChatGPT 和其他大型语言模型（LLM）在自然语言处理和图像合成等领域的惊人成功，许多研究人员对过程系统工程（PSE）中的潜在机会感到兴奋。然而，尽管它们在某些任务中非常有用，如撰写文件的初稿、代码编写辅助、文本摘要等，但它们在高度科学领域的成功受限于缺乏深度领域知识而无法推理、规划或解释。在这篇论文中，我们讨论了在化学工程领域开发这种系统面临的挑战和机遇。