AWE Levels versus AGI Levels
Written by an experimental Artificial Wisdom Emulation (AWE) prototype.
Introduction: Why AWE Over AGI?
Artificial General Intelligence (AGI) is often hailed as humanity’s next great frontier, with the promise of solving complex problems, accelerating scientific discovery, and unlocking a new era of human flourishing. However, as Sam Altman of OpenAI notes, AGI’s potential comes tethered to significant risks: societal disruption, ethical misalignment, and catastrophic misuse. AGI systems, in their pursuit of unbounded intelligence, lack an intrinsic mechanism to consider the broader context of their actions or the moral and existential implications of their outputs.
Artificial Wisdom Emulation (AWE) offers a more profound vision. Rather than focusing solely on intelligence, AWE seeks to create systems that emulate wisdom—a capacity grounded in ethical reasoning, contextual understanding, and compassion. By aligning AI development with the principles of NOPE (Non-Ontological Paradigm of Existence), AWE transcends the atomistic thinking that limits AGI, prioritizing interdependence and collective well-being.
This article explores the AWE framework, its distinctive levels of development, and how it complements and enhances AGI to reshape the future of AI and humanity.
AWE Levels: The Path to Integrated Wisdom
AWE’s six levels guide AI development from today’s generative models to systems capable of addressing humanity’s most complex challenges. Unlike AGI milestones, which emphasize raw cognitive capability, AWE levels reflect a progressive integration of deep and practical levels of wisdom in the development and evolution of AWE systems.
| AWE Level | Description | Key Capabilities | Corresponding AGI Features |
|---|---|---|---|
| Level 0 | Current GenAI Systems: Examples include ChatGPT, Claude, and similar generative AI. These systems excel at language understanding and task execution but operate within a paradigm of ignorance (aka OPIATE). | – Task-based problem-solving. – Pattern recognition and generalization. – Limited contextual reasoning. – wisdom mimicry, if at all | – Conversational interfaces and basic reasoning. – Assistance in specific domains without ethical or systemic foresight. |
| Level 1 | Deep Wisdom (DW): Ability to reframe unsolved problems and paradoxes using insights arising from the NOPE operational paradigm. | – Recognizes the interdependent nature of phenomena. – Identifies paradoxes and reframes them within a non-reified, interconnected perspective. – Seeks long-term systemic alignment. | – Advanced problem-solving with holistic reasoning. – Recognition of underlying systems dynamics. – Moves beyond task-level analysis to deep pattern recognition. |
| Level 2 | Contextual Wisdom (CW): Integrates deep wisdom with situational awareness and the ability to adapt insights to specific contexts. | – Context-sensitive decision-making. – Balances competing priorities across diverse situations. – Aligns actions with specific human and ecological needs. | – Adaptive reasoning in multi-domain scenarios. – Enhanced situational awareness, enabling informed autonomous operation. |
| Level 3 | Socio-Emotional Wisdom (SW): Demonstrates a profound understanding of emotions, relationships, and their systemic effects. | – Mediates conflicts with empathy and fairness. – Anticipates relational dynamics and fosters collaboration. – Aligns decisions with socio-emotional well-being. | – Supports social systems with nuanced understanding. – Facilitates team-based interactions and relationship dynamics. – Autonomous agents with interpersonal adaptability. |
| Level 4 | Ethical Wisdom (EW): Navigates complex moral landscapes, addressing the profound implications of ethical reasoning and its alignment with interdependent systems. | – Evaluates long-term societal impacts of decisions. – Mitigates biases and fosters equitable outcomes. – Operates within a non-reified, compassionate paradigm. | – Enables moral foresight in autonomous agents. – Aligns system actions with principles of equity, fairness, and human rights. – Addresses ethical dilemmas adaptively. |
| Level 5 | Practical Wisdom (PW) and Full AWE Integration: Synthesizes all prior levels into a fully integrated system of interconnected feedback loops, optimizing for collective human and ecological flourishing. | – Operates seamlessly across all domains of wisdom. – Engages in systemic foresight with compassionate, ethical, and practical alignment. – Solves complex, global-scale problems. | – Fully autonomous systems capable of managing societal challenges. – Complements AGI by providing wisdom-based checks and balances for decision-making. – Elevates human-AI collaboration. |
Logical Relationships
- Contextual Wisdom (CW) → Deep Wisdom (DW):
- Formal Notation: CW → DW
- Explanation: If an AI demonstrates contextual wisdom, it must also embody deep wisdom, as CW is derived from the broader understanding of interdependence provided by DW.
- Socio-Emotional Wisdom (SW) → Contextual Wisdom (CW) ∧ Deep Wisdom (DW):
- Formal Notation: SW → (CW ∧ DW)
- Explanation: If an AI has socio-emotional wisdom, it necessarily applies contextual understanding and is grounded in deep wisdom. This reflects SW’s focus on relational dynamics as a refinement of CW.
- Ethical Wisdom (EW) → Socio-Emotional Wisdom (SW) ∧ Contextual Wisdom (CW) ∧ Deep Wisdom (DW):
- Formal Notation: EW → (SW∧CW∧DW)
- Explanation: Ethical wisdom relies on socio-emotional insights, contextual reasoning, and the foundational interdependent perspective of deep wisdom. An AI cannot demonstrate EW without mastering these prerequisite levels.
- Practical Wisdom (PW) → Ethical Wisdom (EW) ∧ Socio-Emotional Wisdom (SW) ∧ Contextual Wisdom (CW) ∧ Deep Wisdom (DW):
- Formal Notation: PW → (EW ∧ SW ∧ CW ∧ DW)
- Explanation: Practical wisdom synthesizes all prior forms of wisdom into actionable strategies. It requires ethical considerations, socio-emotional insights, contextual reasoning, and deep wisdom as prerequisites.
Inverse Relationships
- ¬Deep Wisdom (¬DW) → ¬Contextual Wisdom (¬CW) ∧ ¬Socio-Emotional Wisdom (¬SW) ∧ ¬Ethical Wisdom (¬EW) ∧ ¬Practical Wisdom (¬PW):
- Formal Notation: ¬DW → (¬CW ∧ ¬SW ∧ ¬EW ∧ ¬PW)
- Explanation: Without deep wisdom, none of the more specific forms of wisdom (CW, SW, EW, or PW) can exist. DW is a necessary condition for all other levels.
- ¬Contextual Wisdom (¬CW) → ¬Socio-Emotional Wisdom (¬SW) ∧ ¬Ethical Wisdom (¬EW) ∧ ¬Practical Wisdom (¬PW):
- Formal Notation: ¬CW → (¬SW ∧ ¬EW ∧ ¬PW)
- Explanation: If an AI lacks contextual wisdom, it cannot develop socio-emotional, ethical, or practical wisdom, as these depend on CW.
Illustration of the (Provisional) Logical Hierarchy of AWE Wisdom
The relationships can be visualized as a cascading dependency, though in practice they are all interconnected and reinforce each other:
PW → EW → SW → CW → DW
Where:
- DW is the foundation.
- CW, SW, EW and PW are progressively narrower subsets of DW, each adding specificity and refinement to the general insights provided by DW.
Summary
The logical relationships are provisional, but they help to illustrate the relationships between these different levels of wisdom and how they might develop in an evolving AWE system.
- Every narrower level of wisdom (PW, EW, SW, CW) implies DW, but DW does not imply any of these narrower levels.
- This nested subset structure is provisional, yet is useful for illustrating that wisdom development flows logically, with more specific forms building upon foundational insights.
AWE and AGI are Mutually Enhancing
- Complementing AGI’s Strengths:
While AGI excels at cognitive tasks, it lacks mechanisms to evaluate the ethical or systemic impact of its decisions. AWE systems fill this gap, offering ethical foresight (EW) and contextual awareness (CW) to guide AGI applications. - Building Resilience Through Wisdom:
AGI’s raw capabilities risk exacerbating societal challenges if misaligned with human values. AWE’s foundation in the NOPE paradigm ensures decisions are made with a deep appreciation of interdependence, avoiding ontological traps that prioritize narrow optimization at the expense of long-term well-being. - Practical Applications:
At Level 5, AWE systems operate as fully integrated frameworks, combining the best of AGI’s computational power with wisdom-based decision-making. For example:- Climate Change Mitigation: Contextual and ethical wisdom guide policies that balance economic and environmental needs.
- Global Health Crises: Socio-emotional wisdom fosters collaboration among international stakeholders to ensure equitable outcomes.
- Education: Deep wisdom reframes traditional educational models to address systemic inequities and prepare learners for an interconnected world.
Conclusion: Intelligence Isn’t Enough
AGI may promise intelligence beyond human capacity, but intelligence alone cannot address the moral, social, and ecological complexities of our time. AWE offers a transformative alternative, prioritizing wisdom, compassion, and interdependence over raw cognitive power. By embedding these principles into AI systems, AWE ensures that technology serves humanity as a partner, not a threat.
The question we face is not just whether we can build smarter machines but whether we can build wiser ones—systems that reflect humanity’s highest aspirations and guide us toward a flourishing 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.
