Beyond Facts: Designing AI for Relevance, Uncertainty, and Meaning

Humans are not detached rational minds operating in a vacuum. We are rationalizing pack members whose sense-making is shaped by culture. Our behaviour is informed by a culturally constructed worldview that tells us what to pay attention to and what to ignore. The way we see the world is the way we show up in it, so worldview is not cosmetic; it is cognitive infrastructure.

Defining Intelligence and Knowledge

Within that frame, we can define intelligence and knowledge in operational terms:

• Intelligence is how efficiently a system acquires and uses knowledge under constraints of time, data, attention, and risk
• Knowledge is what you have acquired that reliably helps you predict, explain, and decide

On this view, the central bottleneck is not storage but relevance.

The Role of Relevance

Relevance is not an intrinsic property of facts; it is realized in context, a relation between topics that makes one thing useful for dealing with another. Relevance realization is the capacity to identify what matters in a given situation by ignoring what does not, so a finite agent can navigate a combinatorial explosion of possible signals and actions.

Implications for AI Design

If both humans and AI systems must manage relevance and uncertainty over time, our data and AI stack should reflect that. We should:

• Treat model outputs as answer distributions
• Preserve ambiguity as signal
• Track provenance: what the system knew, from where, and how well it was calibrated

This approach helps us improve our grip on a changing world rather than pretending to have certainty we do not possess.

Impact

RISE work helps organisations design AI and knowledge structures that treat relevance, uncertainty, and provenance as first-class, essential for deploying trustworthy AI in complex, data-sensitive environments.