Recent studies of the applications of conversational AI tools, such as chatbots powered by large
... more Recent studies of the applications of conversational AI tools, such as chatbots powered by large language models (LLMs), 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 a higher-level framework (“Cognitive AI”) 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.
Recent studies of the applications of conversational AI tools, such as chatbots powered by large
... more Recent studies of the applications of conversational AI tools, such as chatbots powered by large language models (LLMs), 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 a higher-level framework (“Cognitive AI”) 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.
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Papers by Nova Spivack
language models (LLMs), 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 a higher-level framework
(“Cognitive AI”) 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.
language models (LLMs), 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 a higher-level framework
(“Cognitive AI”) 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.