While there is a widespread belief that artificial general intelligence (AGI) -- or even superhuman AI -- is imminent, complex problems in expert domains are far from being solved. We argue that such problems require human-AI cooperation and that the current state of the art in generative AI is unable to play the role of a reliable partner due to a multitude of shortcomings, including inability to keep track of a complex solution artifact (e.g., a software program), limited support for versatile human preference expression and lack of adapting to human preference in an interactive setting. To address these challenges, we propose HAI-Co2, a novel human-AI co-construction framework. We formalize HAI-Co2 and discuss the difficult open research problems that it faces. Finally, we present a case study of HAI-Co2 and demonstrate its efficacy compared to monolithic generative AI models.

翻译：尽管普遍认为通用人工智能（AGI）——甚至超人类AI——即将到来，但专家领域的复杂问题远未得到解决。我们认为此类问题需要人机协同，而当前生成式AI的最新技术由于存在诸多缺陷，无法承担可靠合作伙伴的角色，这些缺陷包括：无法追踪复杂解决方案产物（例如软件程序）、对人类多样化偏好表达的支持有限，以及在交互环境中缺乏对人类偏好的适应性。为应对这些挑战，我们提出了HAI-Co2这一新型人机协同构建框架。我们形式化定义了HAI-Co2，并讨论了其面临的困难开放研究问题。最后，我们通过HAI-Co2的案例研究，论证了其相较于单体生成式AI模型的有效性。