AI不该取代人，而该与人协作提升能力。
A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy
- 让AI与人共同完成任务，而非追求完全自治
- 在医疗、金融等领域验证协作优于独立运行
- 适合关注可信AI、人机协同的研究者
大型语言模型（LLMs）的进展促使许多研究者致力于构建完全自主的AI代理。本文质疑这一路径是否正确，指出当前自主系统仍存在可靠性差、透明度低及难以理解人类真实需求等问题。我们提出另一种思路：基于大语言模型的人机系统（LLM-HAS），让AI与人类协同工作，而非取代人类。通过保留人在关键环节中提供指导、解答问题和维持控制，这类系统更具可信度与适应性。文章以医疗、金融、软件开发为例，证明人机协作可更有效地处理复杂任务。同时探讨了构建此类系统的挑战并提出可行方案。本文主张，衡量AI进步不应看其独立程度，而应看其与人类协作的能力。未来最有前景的方向不是替代人类的角色，而是通过有意义的合作增强人类能力。
原文摘要 · Abstract (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.
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