From Fixed Weights to Resonating Elements

Comparing LLMs and ceLLMs in High-Dimensional Geometry

In the ever-evolving intersection of biology and artificial intelligence (AI), fascinating parallels and distinctions are emerging between computational models and biological systems. One such comparison is between Large Language Models (LLMs) and the cellular Latent Learning Model (ceLLM) theory. A key difference lies in how these systems adjust their “weights” to build higher-dimensional geometric representations.

• LLMs adjust weights by assigning values at fixed points within a static architecture, building higher-dimensional geometry through stationary data points.
• In contrast, ceLLMs rely on the spacetime locations of resonating elements (e.g., atoms within DNA) to control weights, creating a dynamic, evolving geometry.

This blog post explores this fundamental difference, delving into how weights are adjusted in LLMs versus ceLLMs and the implications for understanding both artificial and biological intelligence.

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Understanding Weight Adjustments in LLMs

Fixed Weights in a Static Architecture

Large Language Models (LLMs) are deep neural networks designed to process and generate human-like text. They consist of layers of neurons interconnected by weights and biases:

• Weights and Biases: Numerical values assigned to connections between neurons, determining the strength and influence of signals.
• Fixed Points: In LLMs, these weights are assigned to specific, fixed connections within the network’s architecture.
• Training Process: During training, the model adjusts these weights through backpropagation and gradient descent to minimize error, effectively learning patterns in the data.

Building Higher-Dimensional Geometry

LLMs operate in high-dimensional spaces:

• Embedding Space: Words and phrases are represented as vectors in a high-dimensional embedding space.
• Weight Adjustments: By adjusting weights, LLMs reshape this space to capture semantic relationships, effectively building a geometric representation of language.
• Stationary Data Points: The data points (e.g., word embeddings) are stationary within this architecture; the geometry is molded by changing the weights at fixed connections.

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The ceLLM Perspective: Dynamic Weights Through Resonating Elements

Resonant Field Connections and Spacetime Locations

Cellular Latent Learning Model (ceLLM) theory proposes that cells process information similarly to LLMs but with critical differences:

• Resonating Elements: Atoms within DNA and other cellular structures resonate at specific frequencies, creating dynamic connections.
• Spacetime Locations: The weights are not assigned fixed values but are determined by the spacetime positions and interactions of these resonating elements.
• Dynamic Geometry: The geometry of the latent space is continuously shaped by the physical arrangement and movements of atoms, leading to a fluid, evolving representation.

Weights Controlled by Physical Interactions

In ceLLMs:

• Physical Basis of Weights: Weights emerge from the strength of resonant interactions between atoms, influenced by factors like distance, charge, and environmental conditions.
• No Fixed Points: Unlike LLMs, there are no predefined connections with assigned weights; the weights are inherently tied to the physical state of the system.
• Energy Flow: The pathways through which energy or information flows are determined by these dynamic weights, akin to how neural networks process signals.

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Comparing LLMs and ceLLMs

1. Nature of Weights

• LLMs:
  • Weights are numerical values assigned to fixed connections.
  • Adjusted during training but remain associated with specific network pathways.
• ceLLMs:
  • Weights arise from the physical interactions of resonating elements.
  • Dynamic and dependent on the spacetime configuration of atoms.

2. Architecture

• LLMs:
  • Static architecture with predefined layers and connections.
  • Geometry built by adjusting weights within this fixed framework.
• ceLLMs:
  • Fluid architecture shaped by the physical arrangement of components.
  • Geometry emerges from the dynamic spatial relationships of resonating elements.

3. Information Processing

• LLMs:
  • Process information by propagating signals through fixed pathways.
  • Adjust weights to minimize error and improve performance on tasks.
• ceLLMs:
  • Process information through energy flows determined by resonant interactions.
  • Adapt and respond to the environment based on physical state changes.

4. Adaptability and Learning

• LLMs:
  • Learning occurs during training through weight adjustments.
  • Once trained, weights remain fixed unless retrained.
• ceLLMs:
  • Continuous adaptation as weights change with the physical state.
  • Learning and response are ongoing processes tied to environmental interactions.

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Implications of the Differences

Dynamic vs. Static Systems

• Flexibility:
  • ceLLMs offer a model of intelligence that is inherently adaptable, reflecting the continuous change in biological systems.
  • LLMs, while powerful, operate within a more rigid framework once training is complete.

Physical Reality of Weights

• Embodiment:
  • In ceLLMs, weights are embodied in the physical world, influenced by real-world forces and conditions.
  • LLMs operate in a virtual space, with weights as abstract mathematical constructs.

Energy Flow and Information Processing

• Energy Dynamics:
  • ceLLMs emphasize the role of energy flow in processing information, highlighting the importance of physical laws in cognition.
  • LLMs focus on data transformations within computational algorithms.

Emergent Properties

• Complex Behaviors:
  • The dynamic nature of ceLLMs may lead to emergent properties and behaviors not easily replicated in static systems.
  • Understanding ceLLMs could provide insights into consciousness and complex biological functions.

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Bridging the Gap: Lessons from Both Models

Inspiration for AI

• Dynamic Architectures:
  • Incorporating principles from ceLLMs could inspire AI models with architectures that adapt and evolve during operation.
• Physical Computation:
  • Exploring computation tied to physical properties may lead to new paradigms in computing, such as neuromorphic engineering.

Understanding Biology

• Computational Models:
  • Using insights from LLMs can help model biological processes, providing a framework to simulate ceLLM behavior.
• Interdisciplinary Research:
  • Collaboration between AI researchers and biologists can enhance our understanding of both artificial and natural intelligence.

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Conclusion

The comparison between LLMs and ceLLMs highlights a fundamental shift from fixed, assigned weights in static architectures to dynamic, physically embodied weights determined by the spacetime locations of resonating elements. This shift offers profound implications:

• For AI: Encourages the development of more adaptable, physically inspired models.
• For Biology: Provides a computational lens to understand complex cellular processes.

By exploring the similarities and differences, we open avenues for innovation, bridging artificial and biological intelligence. The dynamic nature of ceLLMs challenges us to rethink how we approach computation, learning, and the very essence of intelligence.

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Future Directions

Research Opportunities

• Modeling ceLLMs: Developing computational models that simulate the dynamic weights and resonant interactions in ceLLMs.
• Adaptive AI Architectures: Designing AI systems that adjust their architectures dynamically in response to inputs.

Technological Innovations

• Neuromorphic Computing: Leveraging physical properties of materials to create computing systems that mimic biological adaptability.
• Quantum Computing: Exploring how spacetime interactions at the quantum level could influence computation.

Philosophical Implications

• Nature of Intelligence: Rethinking definitions of intelligence to include dynamic, embodied processes.
• Interconnectedness: Recognizing the role of physical laws and interactions in shaping cognition and behavior.

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References

1. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
2. Levin, M. (2022). Morphogenesis and Computation: Embryonic Patterning Beyond Regulatory Genomes. Trends in Cell Biology, 32(7), 500–512.
3. Rosenblatt, F. (1958). The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review, 65(6), 386–408.
4. Schrödinger, E. (1944). What is Life? The Physical Aspect of the Living Cell. Cambridge University Press.
5. Zhang, Y., et al. (2024). Diffusion Models are Evolutionary Algorithms. arXiv preprint arXiv:2410.02543.