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A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy
- Recent improvements in large language models (LLMs) have prompted a focus on building fully autonomous AI agents, but a position paper argues against this approach.
- Autonomous AI systems still face challenges with reliability, transparency, and understanding human requirements.
- The paper proposes LLM-based Human-Agent Systems (LLM-HAS) where AI collaborates with humans instead of replacing them, ensuring trustworthiness and adaptability.
- By involving humans to provide guidance and maintain control, these systems can be more reliable and suitable for tasks like healthcare, finance, and software development.
- Human-AI teamwork is shown to handle complex tasks more effectively than AI working in isolation, with examples provided from various industries.
- Challenges in building collaborative systems are discussed in the paper along with practical solutions to address them.
- Progress in AI, as argued in the paper, should be based on how effectively systems can work with humans rather than becoming fully independent.
- The paper advocates for AI systems that enhance human capabilities through partnership rather than replacing human roles entirely.
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