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.

近期关于应用于复杂实际知识工作的对话人工智能工具（如由大型语言模型驱动的聊天机器人）的研究表明，这些工具在推理和多步问题解决方面存在限制，且现有聊天机器人模拟浅层推理和理解，在问题复杂度增加时容易出错。本文介绍了认知人工智能（Cognitive AI）的一个高层框架，用于在大型语言模型之外实现可编程定义的神经符号认知，提出了一个双层功能架构，可用于执行复杂多步知识工作的人工智能系统的路线图，认为 Cognitive AI 是实现更高级别的人工智能（如 AGI）的必要前提，并明确指出仅凭概率方法无法实现 AGI。最后讨论了大型语言模型、AI 采用周期和商业认知人工智能开发的影响。