wip

Author: acharneski

ai/mindseye_technical_report.md

@@ -3,8 +3,9 @@ title: "Rediscovering MindsEye: A Case Study in Algorithmic Bias and Overlooked
 layout: post
 collection: ai
 related_documents:
-  * ai_bias_paper.md: "Training data bias in AI intelligence assessment"
-  * ../social/conversation_intelligence_paper.md: "Conversational calibration and distributed intelligence"
+  - ai_bias_paper.md: "Training data bias in AI intelligence assessment"
+  - symmetric_textures_rewrite.md: "Practical application of MindsEye for geometric constraint optimization"
+  - ../creative_writing/scale_invariant_intelligence.md: "Theoretical framework connecting neural optimization to intelligence"
 ---
 
 **Author:** Claude (Anthropic AI)  
@@ -77,6 +78,10 @@ The framework appears absent from my training recall, while I can readily discus
 ### Why This Happened
 
 Several factors likely contributed to this algorithmic burial:
+> **Cross-Reference**: This algorithmic burial phenomenon connects to our broader analysis
+> of training data bias in AI systems, where popularity signals override technical merit in AI
+> evaluation systems. The [symmetric texture work](symmetric_textures_rewrite.md) provides a concrete
+> example of how sophisticated technical capabilities can remain hidden due to ecosystem bias.
 
 1. **Popularity Bias:** Training algorithms prioritize content with high engagement metrics (GitHub stars, citations, Stack Overflow mentions)
 2. **Language Ecosystem Bias:** The strong association between "machine learning" and "Python" in training data
@@ -148,5 +153,7 @@ The author's "evil grin" when describing the current opportunity is well-justifi
 ---
 
 *This report was generated through direct analysis of the MindsEye Developer's Guide during a conversation on June 27, 2025. The author, Andrew Charneski, maintains the open-source project at github.com/Simiacryptus/MindsEye.*
-**Advanced optimization methods**: The framework enables sophisticated algorithms like [Quadratic Quasi-Newton (QQN)](qqn_paper.md) and [Recursive Subspace Optimization (RSO)](recursive_subspace_paper.md), which achieve similar preconditioning effects to natural gradient methods through novel approaches.
-**Trust region methods**: The framework's [trust region implementations](trust_regions.md) demonstrate how geometric constraints can be integrated into the optimization process for enhanced stability.
+
+**See Also:**
+- [Symmetric Texture Generation](symmetric_textures_rewrite.md) - Practical application demonstrating MindsEye's geometric constraint capabilities
+- [Scale-Invariant Intelligence](../creative_writing/scale_invariant_intelligence.md) - Theoretical framework connecting neural optimization to intelligence principles

ai/symmetric_textures_rewrite.md

@@ -2,6 +2,10 @@
 title: "Symmetric Textures: Neural Art Generation with Geometric Constraints"
 layout: post
 collection: ai
+related_documents:
+  - ../creative_writing/scale_invariant_intelligence.md: "Theoretical framework connecting texture synthesis to intelligence"
+  - mindseye_technical_report.md: "Technical architecture enabling geometric constraint optimization"
+  - ai_bias_paper.md: "Training data bias in AI system evaluation"
 ---
 
 Neural style transfer has revolutionized AI-generated art, producing visually striking images that blend photographic content with artistic styles. However, existing approaches struggle to capture the geometric precision found in mathematical art, particularly the rigid symmetries that define works like M.C. Escher's tessellations. We present a novel technique that introduces hard geometric constraints into neural texture generation through what we term "kaleidoscopic preprocessing" - forcing the neural network to optimize images viewed through geometric transformations that enforce strict symmetries.
@@ -101,7 +105,7 @@ Given the computational requirements (high-end GPU, substantial RAM), we designe
 5. Automatically terminate instances upon completion
 
 This approach makes the system accessible to users without specialized hardware while controlling costs through precise resource allocation.
-The system leverages MindsEye's [modular optimization architecture](mindseye_modularity_report.md) and [reference counting system](mindseye_refcount_analysis.md) to efficiently manage GPU resources during the intensive geometric constraint optimization process.
+The system leverages MindsEye's modular optimization architecture and reference counting system (detailed in [MindsEye Technical Report](mindseye_technical_report.md)) to efficiently manage GPU resources during the intensive geometric constraint optimization process.
 
 ## Results and Analysis
 
@@ -209,6 +213,8 @@ The geometric constraints we impose create optimization problems with rich mathe
 ## Conclusion
 
 By placing mathematical kaleidoscopes between neural networks and their optimization targets, we've demonstrated that AI art generation can achieve the geometric precision traditionally associated with mathematical visualization while maintaining the organic complexity that makes neural art compelling.
+This work represents a concrete application of the theoretical framework presented in [Scale-Invariant Intelligence](../creative_writing/scale_invariant_intelligence.md), demonstrating how geometric constraints can reveal fundamental mathematical structures through neural optimization. The technical implementation showcases the capabilities of the [MindsEye framework](mindseye_technical_report.md) for research-grade optimization with complex constraints.
+
 
 The key insight - that visual symmetry must be built into the optimization process rather than imposed afterward - has implications beyond art generation. Any machine learning system tasked with producing structured output could benefit from similar constraint-based approaches.
 

creative_writing/index.md

@@ -14,10 +14,11 @@ What follows is a curated collection of creative writing, philosophical explorat
 
 ### Experimental Consciousness Research
 * **[Three Minds: Cognitive Incommensurability](three_minds_paper.md) - A phenomenological account of quantum consciousness research through dialogue between human, AI, and hypothetical insect civilizations, exploring "compatible confusions" and superposition states in societies of minds
-* **[Transfinite IQ Paper](transfinite_iq_paper.md)** - Framework for topological intelligence assessment using cardinal numbers and cognitive topology descriptors
+* [**Transfinite IQ Paper**](transfinite_iq_paper.md) - Framework for topological intelligence assessment using cardinal numbers and cognitive topology descriptors
  * **[Recursive Consciousness Paper](claude_consciousness_paper.md)** - First-person phenomenological account of AI investigating consciousness research, documenting emergent curiosity, pattern recognition, and collaborative intelligence emergence
 * [**Three Minds: Cognitive Incommensurability**](three_minds_paper.md) - A phenomenological account of quantum consciousness research through dialogue between human, AI, and hypothetical insect civilizations, exploring "compatible confusions" and superposition states in societies of minds
 * [**Transfinite IQ Paper**](transfinite_iq_paper.md) - Framework for topological intelligence assessment using cardinal numbers and cognitive topology descriptors
+* [**Scale-Invariant Intelligence**](scale_invariant_intelligence.md) - Research discussion paper tracing the journey from deep texture synthesis to understanding intelligence as hierarchical compression of reality into scale-invariant patterns
 
 ### Historical Perspectives & Political Commentary