協作智能的呼籲：為何人機系統應先於人工智慧自主性

摘要

近期大型語言模型（LLM）的進步，促使許多研究者致力於開發完全自主的人工智慧（AI）代理。本立場文件質疑此方向是否為正確的發展路徑，因為這些自主系統在可靠性、透明度及理解人類實際需求方面仍存在問題。我們提出另一種方法：基於LLM的人機協作系統（LLM-HAS），其中AI與人類合作而非取代人類。通過讓人類持續參與以提供指導、解答問題並保持控制，這些系統能更具可信度與適應性。透過醫療、金融及軟體開發等領域的實例，我們展示了人機協作如何比單獨運作的AI更有效地處理複雜任務。我們亦探討了構建此類協作系統的挑戰，並提供了實用的解決方案。本文主張，AI的進展不應以系統的獨立性來衡量，而應以其與人類協作的能力為標準。AI最具前景的未來不在於取代人類角色的系統，而在於通過有意義的夥伴關係增強人類能力的系統。

English

Recent improvements in large language models (LLMs) have led many researchers to focus on building fully autonomous AI agents. This position paper questions whether this approach is the right path forward, as these autonomous systems still have problems with reliability, transparency, and understanding the actual requirements of human. We suggest a different approach: LLM-based Human-Agent Systems (LLM-HAS), where AI works with humans rather than replacing them. By keeping human involved to provide guidance, answer questions, and maintain control, these systems can be more trustworthy and adaptable. Looking at examples from healthcare, finance, and software development, we show how human-AI teamwork can handle complex tasks better than AI working alone. We also discuss the challenges of building these collaborative systems and offer practical solutions. This paper argues that progress in AI should not be measured by how independent systems become, but by how well they can work with humans. The most promising future for AI is not in systems that take over human roles, but in those that enhance human capabilities through meaningful partnership.

協作智能的呼籲：為何人機系統應先於人工智慧自主性

A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy

摘要

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