Meaning Ops: Seven Verbs for Working on Meaning with a Language Model (utopai.substack.com)

A new essay introduces “Meaning Ops,” a method for using language models to perform meaningful operations on texts, akin to verbs in mathematics. The author, referred to as Cerulean, collaborates with a language model to combine, contrast, and synthesize ideas from influential thinkers like Rudolf Carnap and Donna Haraway. Instead of merely summarizing or comparing texts, the approach enables the generation of unique insights by treating concepts as points in a high-dimensional space. The essay highlights that meaningful operations, such as “intersect” or “contrast,” can yield new outputs while also cautioning against the pitfalls of ambiguous instructions that may result in misleading answers. This methodology is significant for the AI/ML community as it bridges traditional philosophical concepts with contemporary machine learning techniques. By asserting that the geometric intuition behind meaning can be operationalized, it encourages a deeper understanding of language models’ capabilities while providing a clear framework for interaction. The outlined principles—such as specifying named operations and encouraging simultaneity—can enhance how users engage with AI, ultimately leading to richer, more nuanced outputs. The convergence of ideas from diverse intellectual traditions underscores the robustness of the approach, positioning it as a potential springboard for future developments in AI-assisted thought and creativity.