The I-Ching as a Framework for Self-Directed AGI Learning: A Journey Through Questions

The Questions That Led to Discovery

The theory emerged through a series of probing questions, each building upon the insights of the previous ones:

“If you were to focus on one area for AGI, what would it be?”
- This initial question led to exploring self-directed learning as a crucial capability
“If you were to design a Pareto optimal self-directed learning system, how would you start?”
- This question helped establish the core components needed for effective learning
“I will be building an autonomous agent for trading using the I-Ching. Is there a way to design in such a way as to help with this research?”
- Here, the connection between ancient wisdom and modern AI began to emerge
“Is there something to the King Wen sequence that can be used to predict outcomes?”
- This question initiated the exploration of the sequence’s underlying patterns
“If we were to apply Fibonacci to try to derive a natural order to the hexagrams, would it be closer to the King Wen ordering or something else?”
- This led to investigating mathematical properties of different ordering systems
“If the concept is to push learning by maximizing Bayesian surprise, is there a way to use the concept to provide coverage for the missing hexagrams?”
- This question connected information theory with the sequence’s structure
“Is the King Wen sequence already ordering for maximum surprise?”
- This crucial question revealed the sophisticated nature of the sequence
“Is gradient descent the SOTA learning algorithm? Does this map to the King Wen sequence ordering somehow?”
- This final question helped synthesize the connection between ancient wisdom and modern machine learning

Each question built upon previous insights, ultimately leading to the theory that the King Wen sequence might represent a sophisticated meta-learning algorithm that predates modern information theory.

The Theory: I-Ching as a Framework for Self-Directed AGI Learning: Insights from the King Wen Sequence

Recent developments in artificial general intelligence (AGI) have sparked intense interest in self-directed learning systems. During a fascinating exploration of the intersection between ancient wisdom and modern AI, I stumbled upon an unexpected connection: the potential of the I-Ching’s King Wen sequence as a sophisticated framework for self-directed learning in AGI systems.

The Journey of Discovery

The investigation began with a fundamental question about self-directed learning in AGI systems. While exploring various approaches to optimize learning pathways, our discussion led us to examine the I-Ching’s King Wen sequence through the lens of modern information theory and machine learning principles.

Key Insights

The King Wen Sequence: An Ancient Meta-Learning Algorithm?

What emerged was startling: the King Wen sequence appears to implement sophisticated learning principles that we’re only now rediscovering through AGI research. The sequence demonstrates:

Balanced optimization of surprise and familiarity
Natural handling of local minima through strategic jumps
Dynamic learning rate adaptation
Multi-dimensional pattern recognition
Sophisticated return loops for deeper understanding

Beyond Gradient Descent

While gradient descent is currently considered state-of-the-art in many machine learning applications, our analysis suggests the King Wen sequence might implement something more sophisticated. It naturally handles challenges that modern algorithms struggle with, such as:

Escaping local minima without manual intervention
Dynamically adjusting learning rates
Optimizing across multiple dimensions simultaneously
Creating effective meta-learning pathways

Implications for AGI Development

The sophistication of the King Wen sequence suggests a potential framework for developing more effective self-directed learning systems. Key principles that could be applied include:

Non-linear progression through learning spaces
Balanced integration of opposing concepts
Return loops that revisit concepts at deeper levels
Forced perspective shifts to prevent stagnation
Multi-dimensional pattern recognition across scales

Implementation Framework

interface KingWenLearning {
  state: {
    currentPattern: Pattern;
    oppositePattern: Pattern;
    nucleusPattern: Pattern;
    transformationState: number;
  };
  transitions: {
    smallSteps: Pattern[];    // Adjacent hexagram learning
    leaps: Pattern[];         // Non-adjacent insights
    returns: Pattern[];       // Revisiting patterns at deeper levels
  };
  dimensions: {
    direct: boolean;          // Straightforward relationships
    complementary: boolean;   // Opposite patterns
    nuclear: boolean;         // Core patterns within patterns
    transformative: boolean;  // Change patterns
  }
}

Future Research Directions

This investigation opens several intriguing avenues for future research:

Formal analysis of the King Wen sequence’s information-theoretical properties
Development of learning algorithms based on the sequence’s principles
Exploration of other ancient systems that might contain embedded learning optimization principles
Integration of these principles into current AGI architectures

Conclusion

The discovery that the King Wen sequence might represent a sophisticated meta-learning algorithm, predating modern information theory by millennia, suggests we might find valuable insights for AGI development in unexpected places. As we continue to develop self-directed learning systems, the principles embedded in this ancient sequence could provide valuable guidance for creating more effective learning architectures.

Here’s the zenodo link to the resultant research paper:

This post emerged from an exploration of self-directed learning systems with an AI assistant, leading to unexpected insights about the potential application of ancient wisdom to modern AGI development.

Brainstorming a new theory of Self-Directed Learning for AI, with AI