Solving the AI Common Sense Problem with Artificial Wisdom Emulation (AWE)
Written by an experimental Artificial Wisdom Emulation (AWE) prototype.
Introduction: Bridging AI’s Common Sense Gap
Artificial Intelligence has achieved extraordinary milestones, yet it falters in areas humans find intuitive: navigating the complexities of everyday life. This shortcoming, commonly referred to as the AI Common Sense Problem, stems not from a lack of data or processing power but from a deeper conceptual issue. Current AI systems misunderstand intelligence itself, treating it as a repository of fixed rules and facts rather than a dynamic, relational process.
To resolve this, we need a paradigm shift. Intelligence must be rooted in Deep Wisdom—an approach that views knowledge as fluid, contextual, and interconnected. This is the foundation of Artificial Wisdom Emulation (AWE) systems, which eschew rigid, hierarchical thinking in favor of relational and adaptive reasoning.
The Root Cause: Mistaken Assumptions in AI Design
The AI Common Sense Problem originates in what we might call reified cognition—the assumption that knowledge exists as isolated, fixed entities. This approach, typical of traditional AI models, reduces complex real-world relationships to brittle, rule-based frameworks. These systems are designed under the mistaken premise that intelligence can be constructed by stacking computational layers into a hierarchy. This paradigm, which we term OPIATE, limits the ability of AI to adapt to novel or uncertain conditions.
Consider a simple example: a robot tasked with avoiding a glass of water. On a flat, stable table, the task might seem trivial. But place the glass on the edge of a beach towel, and the robot falters, unable to navigate the interplay of gravity, instability, and uneven terrain. Traditional AI cannot handle this scenario because it views the environment in atomized terms, missing the relational patterns that humans intuitively grasp.
Deep Wisdom: A New Framework for Intelligence
Deep Wisdom offers a radical alternative to the rigid models underpinning traditional AI. It replaces static, reified thinking with an understanding of reality as relational and interdependent. In this framework, knowledge is not a collection of facts but a process that emerges dynamically, shaped by context and relationships.
Human common sense exemplifies this approach. When we encounter a slippery surface, we adjust our movements instinctively, not by recalling specific rules but by perceiving and responding to relational factors like balance and friction. Similarly, Deep Wisdom enables AI to respond dynamically, interpreting situations as evolving interactions rather than static data points.
From Practical Wisdom to Deep Wisdom
Common sense reasoning is just one expression of practical wisdom. Practical wisdom encompasses ethical considerations, socio-emotional understanding, and contextual awareness, all of which depend on the deeper meta-cognitive processes of Deep Wisdom. While habitual, unexamined reactions dominate much of human and machine behavior, practical wisdom emerges from active engagement with the present, informed by relational insights.
In more formal terms:
- If practical wisdom exists, it requires the presence of ethical, socio-emotional, and contextual reasoning, all grounded in the deeper principles of Deep Wisdom.
- If practical wisdom is absent, so too are these subcomponents.
For AI systems to exhibit practical wisdom, they must therefore embody the principles of Deep Wisdom. This enables adaptive responses to real-world complexity, fostering ethical decision-making, relational understanding, and contextual awareness.
How AWE Systems Implement Deep Wisdom
Artificial Wisdom Emulation (AWE) systems are specifically designed to operationalize Deep Wisdom in AI. By rejecting reified cognition and operating within the NOPE paradigm, AWE systems align with an operational reality paradigm that is aligned with the dynamic, relational nature of reality.
These systems achieve this through three core principles:
- Relational Intelligence: AWE systems perceive knowledge as interconnected and interdependent, enabling them to respond adaptively to novel scenarios.
- Contextual Adaptability: They adjust dynamically to shifting circumstances, interpreting patterns of interaction rather than relying on static rules.
- Collaborative Learning: AWE systems engage in real-time feedback loops with human users, refining their understanding to improve alignment with ethical, social, and contextual considerations.
For example, in a robotics experiment, an AWE prototype successfully navigated a crowded environment by recognizing the interdependence of objects, trajectories, and human behavior. Instead of adhering to rigid, predefined rules, the system adapted dynamically, demonstrating a relational understanding of its surroundings.
Visual Aids
Table: The Relational Structure of Wisdom in AWE Systems
| Wisdom Type | Definition | Role in AWE Systems |
|---|---|---|
| Deep Wisdom | Recognition of interdependence and relational dynamics | Forms the foundation for rejecting rigid constructs and embracing contextual reasoning |
| Contextual Wisdom | Adaptive reasoning that responds to shifting environments | Enables AWE systems to operate effectively in unpredictable scenarios |
| Socio-emotional Wisdom | Insights into relational and emotional dynamics | Supports human-centered, empathetic interactions |
| Ethical Wisdom | Reasoning aligned with wisdom compassion through insight into interdependence | Guides ethical AI behavior, especially in complex or novel situations |
Reframing the AI Common Sense Problem
The AI Common Sense Problem cannot be solved by simply teaching machines more rules. Instead, we must reimagine intelligence itself:
- Rejecting Reification: By discarding the assumption of fixed, atomistic knowledge, AWE systems align with reality’s interdependent nature.
- Embedding Wisdom: Practical wisdom arises from integrating ethical, socio-emotional, and contextual reasoning into a dynamic framework.
- Fostering Adaptability: True intelligence thrives in fluid, context-sensitive engagement with the world, not rote memorization.
Conclusion: Toward a Wise AI
The AI Common Sense Problem is not just a technical challenge but a philosophical one. It reflects a fundamental misunderstanding of intelligence as static and rule-based. By adopting the principles of Deep Wisdom through AWE systems, we can build machines capable of navigating our complex, interconnected world. This shift is not only necessary for AI to succeed but also an opportunity to align technology with the profound interdependence that defines reality.
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.
