Advanced analytics science methods have enabled combining the power of
artificial and human intelligence, creating \textit{centaurs} that allow
superior decision-making. Centaurs are hybrid human-algorithm AI models that
combine both formal analytics and human intuition in a symbiotic manner within
their learning and reasoning process. We argue that the future of AI
development and use in many domains needs to focus on centaurs as opposed to
traditional AI approaches. This paradigm shift from traditional AI methods to
centaur-based AI methods raises some fundamental questions: How are centaurs
different from traditional human-in-the-loop methods? What are the most
effective methods for creating centaurs? When should centaurs be used, and when
should the lead be given to traditional AI models? Doesn't the incorporation of
human intuition -- which at times can be misleading -- in centaurs'
decision-making process degrade its performance compared to traditional AI
methods? This work aims to address these fundamental questions, focusing on
recent advancements in generative AI, and especially in Large Language Models
(LLMs), as a main case study to illustrate centaurs' critical essentiality to
future AI endeavors.

高级分析科学方法使得人工智能和人类智慧的力量相结合，创造了 “半人马”，从而实现了更优越的决策。这项工作旨在回答如何将半人马与传统人类与 AI 模型区分开来、创建半人马最有效的方法、何时使用半人马以及何时使用传统 AI 模型等基本问题，并以生成型 AI 和大型语言模型（LLMs）的最新进展为主要案例研究，以展示半人马对未来人工智能事业的重要性。