Recent studies of the applications of conversational AI tools, such as chatbots powered by large language models, to complex real-world knowledge work have shown limitations related to reasoning and multi-step problem solving. Specifically, while existing chatbots simulate shallow reasoning and understanding they are prone to errors as problem complexity increases. The failure of these systems to address complex knowledge work is due to the fact that they do not perform any actual cognition. In this position paper, we present Cognitive AI, a higher-level framework for implementing programmatically defined neuro-symbolic cognition above and outside of large language models. Specifically, we propose a dual-layer functional architecture for Cognitive AI that serves as a roadmap for AI systems that can perform complex multi-step knowledge work. We propose that Cognitive AI is a necessary precursor for the evolution of higher forms of AI, such as AGI, and specifically claim that AGI cannot be achieved by probabilistic approaches on their own. We conclude with a discussion of the implications for large language models, adoption cycles in AI, and commercial Cognitive AI development.

翻译：近期研究表明，以大型语言模型驱动的聊天机器人等对话式人工智能工具在复杂真实世界知识工作中的应用存在推理与多步骤问题解决能力的局限性。具体而言，现有聊天机器人虽能模拟浅层推理与理解，但随着问题复杂度增加，其错误率显著上升。这些系统无法应对复杂知识工作的根本原因在于它们并未执行任何实质性的认知过程。在本立场论文中，我们提出认知人工智能（Cognitive AI）这一高阶框架，旨在大型语言模型之外通过程序化方式实现神经符号认知。具体而言，我们为认知人工智能设计了双层功能架构，作为能够执行复杂多步骤知识工作的人工智能系统的路线图。我们认为认知人工智能是更高级人工智能形态（如通用人工智能）演进的必要前提，并特别指出仅靠概率方法无法实现通用人工智能。最后，我们讨论了该研究对大型语言模型、人工智能技术采纳周期以及商业级认知人工智能开发的影响。