Systems design 3: LLMs and the semantic revolution (apenwarr.ca)

A recent discussion highlights the significance of large language models (LLMs) in driving a semantic revolution in systems design, akin to the foundational shifts brought by the Internet. The article reflects on the complexities of establishing universal standards for communication and interoperability among diverse systems. Drawing parallels to historical challenges of connecting computers globally, it illustrates how effective communication, as encapsulated in Postel's Law—“be conservative in what you send, liberal in what you accept”—has facilitated smoother interactions in digital environments. This conversation is crucial for the AI and machine learning (ML) community as it emphasizes the need for adaptable and robust frameworks capable of handling the inherent imperfections of human communication. The text delves into the evolution of technologies like XML and HTML, demonstrating that while syntax can be standardized, the true challenge lies in managing semantics. By recognizing the critical role that LLMs play in interpreting and generating human-like text, the discussion posits that a similar semantic understanding could guide the development of more resilient AI systems that can operate effectively across varied contexts and datasets, ultimately enhancing cross-platform communication and interoperability in the AI landscape.