Abstract: 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.

Lay summary (by Llama 3 70B Instruct): Imagine you're chatting with a computer program that's supposed to help you with complex tasks, like solving a tricky problem or understanding a complicated idea. These programs, called chatbots, are good at simple conversations but they're not very good at handling complex problems that require deep thinking and reasoning. They often make mistakes when the problem gets too hard. The reason for this is that these chatbots don't really "think" or understand things like humans do. They're processing words and phrases without any real understanding. We propose a new approach to building AI systems that can really think and understand complex ideas. We call it Cognitive AI. It's like a blueprint for building AI systems that can do complex tasks, like solving multi-step problems. We believe that this approach is necessary for building even more advanced AI systems in the future. In short, we're saying that current chatbots are not good enough, and we need a new way of building AI systems that can really think and understand complex ideas.