The Evolving Landscape of AI: Bridging the Gap Between Explicit, Tacit, and Tribal Knowledge

Artificial Intelligence (AI) has been making leaps and bounds, promising machines that think like humans—or even outperform us.

Yet, achieving a symbiotic relationship between human intellect and machine capabilities remains an ambitious goal.

The challenge often lies in capturing both explicit and tacit forms of human knowledge into AI systems.

The Rulebook of Explicit Knowledge

Explicit knowledge refers to codified rules and structured info that can be systematically programmed into AI.

Like medical diagnosis systems that incorporate textbooks, research papers, and clinical guidelines.

This allows them to suggest possible conditions from symptoms.

With me so far?

Cracking the Code of Experience (Tacit Knowledge)

Tacit knowledge is harder to capture - the intangible, experiential know-how from practice.

Like a firefighter's sixth sense for how a fire spreads based on subtle cues.

Or a chess grandmaster's intuitive grasp of the board. This slippery stuff is tough to codify but super important!

Surfacing Invisible Expertise

Emerging techniques help elicit tacit knowledge.

Like analyzing expert performance data for behavioral patterns and heuristics.

Cognitive interviews also aim to externalize implicit reasoning.

In chess, researchers track masters’ gaze to glean insights into their tactical intuition. Progress!

The Tribal Aspect: A Different Beast

But what about tribal knowledge?

This unique blend of articulable and inarticulable wisdom exists within specific groups and is neither fully explicit nor completely tacit.

Capturing tribal knowledge requires a different set of tools:

• Articulable Elements: Techniques such as interviews, documentation, and training programs can capture knowledge that can be openly discussed.

• Tacit Components: For the unwritten, experiential know-how, strategies like shadowing, heuristic analysis, and narrative capture can be invaluable. Observational techniques, akin to tracking a chess player's gaze, may also be employed to understand the group's collective expertise.

Hybrid AI - Combining Rules and Neural Nets

A multifaceted approach is required.

Hybrid systems combining rule-based AI and data-driven machine learning appear promising.

Human-in-the-loop training, with experts reviewing and retraining models, also offers potential. But it introduces complexities...

Testing the Limits of Explainability

Rigorous testing is vital to ensure safety and reveal when AI diverges from human logic.

Explainable AI tools that clarify how models reach conclusions help refinement.

For example, medical AI could explain its diagnoses by referencing guidelines used.

The Polanyi Paradox - Making the Tacit Explicit?

We're left grappling with the Polanyi Paradox, summarized by the phrase, "We know more than we can tell."

Can AI ever replicate the tacit and tribal forms of human expertise?

The answer remains elusive, but the ongoing dialog between machines and human knowledge is inching us closer to a future where the two can coexist and even complement each other.

Augmenting Humans Requires Humans in the Loop

By understanding and appreciating the complexities of capturing both explicit and tacit forms of knowledge, including the unique realm of tribal wisdom, we can strive for a future where AI truly augments human capabilities.

The magic happens when these different types of knowledge harmonize, pushing the boundaries of what both humans and machines can achieve.