Computational Philosophy: Building on the Implicate Order to Surface New Knowledge and Insights
Originally published on LinkedIn, September 1, 2023
Today we explore a fascinating parallel between prompt engineering in AI models and discovering new information from SQL databases. At their core, both aim to surface new knowledge from vast stores of information.
The Implicate Order
The term “implicate order” refers to the intricate, intertwined network of knowledge—information enfolded within a vast, interconnected web of collective human experiences. AI models like ChatGPT represent the distillate of years of Internet training data, converging the knowledge state of millions.
When the Web first emerged in the early 1990s, it was described as the Global Brain. In reality, it was just a global filing cabinet—lots of facts, but no wisdom, cognition, or insights.
Prompt Engineering vs. SQL Queries
Prompt engineering is the art of crafting input prompts for AI models to generate specific, high-quality responses. Similarly, SQL uses structured queries to extract meaning from relational databases.
Both systems don’t “know” things conventionally. They access vast amounts of data or patterns to retrieve or generate output. The precision and quality depend largely on how you formulate your input.
Surfacing Implicate Knowledge
With SQL, we must know what to ask and have deep understanding of the model’s domain. We create specific graphs of connection through predicate joins between related fields.
With AI models, we can ask vague, ambiguous questions with little domain understanding. The AI creates inferential output based on token patterns and model weighting, bridging gaps between seemingly unrelated information.
From Galileo to AI
In 1589, Galileo challenged Aristotle’s belief by dropping spheres from the Leaning Tower of Pisa. His experiments were made possible by precision timepieces—the technology of his era—which also transformed navigation and enabled global exploration.
Today, AI models act as our new compass, our contemporary precision instruments. They accumulate vast knowledge and infer, predict, and generate. When we query these models, they sift through implicate knowledge, unfolding intricate patterns to reveal new insights.
Just as precision timepieces empowered Renaissance thinkers, AI models empower modern professionals and scholars. These tools allow us to slice through vast data with precision and create new knowledge by engaging with AI.
The Cognition Economy
In today’s Cognition Economy, deeply cultivated domain expertise is critical to being a Knowledge Engineer. Whatever your HR department titles you as, if you work in the Cognition Economy, you are fundamentally a Knowledge Engineer.
Every query you make, every insight you draw, continues a centuries-long tradition of inquiry. Just as Galileo stood on the shoulders of giants, so too do we, as we gaze into the future empowered by AI.
The narrative of human progress is intertwined with our tools and our drive to unearth implicate knowledge—to make the implicit, explicit.