  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.
