Bridging Hermeneutics and AI: The Quest for Meaning in a Digital Age

As someone with a foundation in Bible Theology and Hermeneutics, the recent discourse on Large Language Models (LLMs) and their interpretative capabilities has struck a chord with me. Hermeneutics, the art and science of interpretation, provides a fascinating lens through which to examine the burgeoning field of AI.

The challenge of interpretation—be it of ancient texts or digital data—lies in the dance between intention and understanding, a space where ambiguity and context are both obstacle and key. In AI, particularly with the advent of Azure CLU (Conversational Language Understanding), we see a promising convergence of these hermeneutic principles and computational technology.

Azure CLU's orchestration of Intents and a Custom DSL (Domain-Specific Language) for Entity construction, grounded in ontological frameworks, is akin to the hermeneutical process. It involves discerning the underlying structure and meaning of texts—now, however, the texts are data, and the interpretation is automated workflow.

In this light, the transformer models in LLMs could be seen as digital hermeneuts, compressing and encoding the "Shannon information" of web text into a vast, interconnected graph of model weights. Yet, as hermeneutics teaches us, information is not the same as meaning. Meaning is emergent—it arises from context, intention, and interaction. It is not merely captured in the static graph of a model but in the dynamic interplay between agents—whether human or AI.

By integrating formal ontologies into LLM prompts, we provide a "context header," guiding AI towards interpretations aligned with an organization's core semantics and values. This is not just technical optimization; it's a fundamental step toward ensuring that the AI we build and interact with can engage with us on terms that hold shared meaning and relevance.

In the grand tradition of hermeneutics, we must now apply these time-honored principles to the AI we create. Every organization must recognize the existential imperative to consolidate its ontological core. It is not just about survival but about shaping an AI future that understands us—and that we can understand in return.

As we stand on this digital frontier, let us bring the depth of hermeneutics to bear on our AI endeavours, striving for a future where technology not only processes information but engages with the profound tapestry of human meaning.

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