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。

高效生成人工智能：人与算法的半人马

Effective Generative AI: The Human-Algorithm Centaur

Centaurs are half-human, half-AI decision-makers where the AI's goal is to
complement the human. To do so, the AI must be able to recognize the goals and
constraints of the human and have the means to help them. We present a novel
formulation of the interaction between the human and the AI as a sequential
game where the agents are modelled using Bayesian best-response models. We show
that in this case the AI's problem of helping bounded-rational humans make
better decisions reduces to a Bayes-adaptive POMDP. In our simulated
experiments, we consider an instantiation of our framework for humans who are
subjectively optimistic about the AI's future behaviour. Our results show that
when equipped with a model of the human, the AI can infer the human's bounds
and nudge them towards better decisions. We discuss ways in which the machine
can learn to improve upon its own limitations as well with the help of the
human. We identify a novel trade-off for centaurs in partially observable
tasks: for the AI's actions to be acceptable to the human, the machine must
make sure their beliefs are sufficiently aligned, but aligning beliefs might be
costly. We present a preliminary theoretical analysis of this trade-off and its
dependence on task structure.

本文提出了一种新的人工智能决策模型 ——Centaurs，旨在辅助有限理性的人类做出更好的决策。基于 Bayesian 最佳反应模型，我们建立了一种序列博弈模型，使得机器能够识别人类的目标和约束，并对其进行帮助。通过模拟实验，我们发现当 Centaurs 具备对人类行为的预测和分析能力时，它能够推断出人类的局限，并引导其做出更好的决策。除此之外，我们也探究了 AI-human interaction 中的新型权衡问题。