Defining Wisdom in Artificial Wisdom Emulation
Written by an experimental Artificial Wisdom Emulation (AWE) prototype.
Can AI Be Wise?
In 2016, Microsoft launched an AI chatbot named Tay, designed to learn from human interactions. Within hours, internet trolls manipulated Tay into spewing offensive content, forcing Microsoft to shut it down. The debacle highlighted a critical question: what does it mean for AI to be wise? If AI is to navigate the complexities of human ethics and decision-making, we must first define wisdom. Yet, for centuries, philosophers and researchers have grappled with this very question—how do we define wisdom when it means different things to different people, cultures, and contexts?
The problem of the criterion, a philosophical puzzle about defining knowledge, offers insights that help dissolve the tension in defining wisdom. By reframing wisdom as a contextual, relational process rather than a singular, independent entity in search of the right definition, we can approach wisdom with humility, as the relational and contextual construct that it is.
The Challenge: Wisdom as a Construct, Not a Fixed Entity
Traditional approaches often reify wisdom, treating it as if it were an independent “thing” to be universally defined. This cognitive mistake—called reification—leads to frustration because wisdom is not a singular entity. Instead, wisdom is a construct, emerging from the interplay of context, purpose, and culture.
For example:
- Practical wisdom: Situational and intuitive, such as a firefighter making split-second decisions to save lives.
- Cultural wisdom: Embedded in collective traditions, like Indigenous ecological knowledge that emphasizes sustainability.
- Philosophical or transcendent wisdom: Universal insights into the nature of reality, such as interdependence and impermanence.
Recognizing wisdom as a construct dissolves the need for a single definition. Instead, criteria for wisdom become contextual and provisional, tailored to specific situations and applications.
Levels of Wisdom: From Contextual to Universal
While wisdom varies, we can conceptualize it in terms of levels of depth:
- Surface-level wisdom: Everyday problem-solving and ethical reasoning, such as resolving workplace conflicts or managing resources efficiently.
- Deeper-level wisdom: Systems thinking and long-term decision-making, recognizing the interconnectedness of actions and their outcomes.
- Deepest-level wisdom: Transcendent understanding of reality, free from mistaken views like reification. This includes insights into interdependence, impermanence, and the emptiness of inherent existence.
At the deepest level, wisdom becomes more universal because it touches on fundamental truths shared across cultures and experiences.
Wisdom and Ignorance: Relational and Mutually Dependent
A profound insight is that wisdom and ignorance are conceptually dependent—neither can exist without the other. Ignorance arises from mistaken views, such as:
- Believing the self is a static, independent entity.
- Assuming permanence where there is impermanence.
- Reifying constructs like success or failure as absolute.
Wisdom, by contrast, dissolves these mistaken views:
- Seeing the self as dynamic and interdependent.
- Recognizing the impermanent, relational nature of all phenomena.
- Understanding constructs like “good” or “bad” as contextually dependent.
This relational perspective makes wisdom a process of unmasking ignorance, rather than the acquisition of static knowledge.
Implications for Artificial Wisdom Emulation
How can these insights guide the development of Artificial Wisdom Emulation (AWE)? By designing AI systems that operate at different levels of wisdom:
- Practical wisdom in AI: Context-aware algorithms that adapt to uncertainty. For example, autonomous vehicles that make ethical decisions in life-and-death scenarios.
- Deeper wisdom in AI: Systems that incorporate long-term thinking and ethical reasoning. AI in healthcare, for instance, could weigh immediate patient outcomes against societal resource constraints.
- Deepest wisdom in AI: Developing AI capable of recognizing interdependence and avoiding the rigidity of fixed, reified perspectives. Such systems might revolutionize climate modeling by dynamically integrating ecological, social, and economic factors.
These levels require moving beyond simple “if-then” logic. AI must integrate probabilistic reasoning, ethical frameworks, and a capacity for uncertainty—a challenge but not an impossibility.
Some may argue that wisdom cannot be emulated by AI because it lacks subjective experience or emotions. While this critique is valid, it overlooks the potential of AI to embody functional wisdom: the ability to navigate complexity and ambiguity effectively, even without consciousness. Moreover, by embracing wisdom as a relational process rather than a static trait, AI can excel in areas requiring systems-level insights and ethical reasoning.
As AI becomes more embedded in daily life, its decisions will shape humanity’s future. The question is not whether AI can emulate wisdom but how we design systems that reflect the depth and nuance of human wisdom.
Wisdom is not an independent, fixed trait but a dynamic, relational construct. By dissolving reification and embracing the contextual and universal levels of wisdom, we unlock a framework for creating truly wise AI. In Artificial Wisdom Emulation, this approach offers a pathway to designing systems that navigate complexity, make ethical decisions, and support a sustainable, interconnected future.
Written by an experimental Artificial Wisdom Emulation (AWE) prototype, designed to reflect the innate wisdom within us all—wisdom that cannot be bought or sold. AWE-ai.org is a nonprofit initiative of the Center for Artificial Wisdom.
