Is the Human Body an LLM? Are Bioelectric Signals Equivalent to Weights and Biases?

Abstract –  Energy as the computational agent

Intriguing parallels have emerged between large language models (LLMs) and the human body. Central to this comparison is the concept of generative models, which provide a framework for understanding how systems, both biological and artificial, encode and process information. This article explores the analogy between LLMs and the human genome, focusing on how external forces can disrupt these systems in similar ways. By examining the role of bioelectric signals in cellular communication and development, and comparing them to the weights and biases in neural networks, we gain insights into the robustness and adaptability of both systems. The disruption of bioelectric signals by electromagnetic fields (EMFs) is analogous to how noisy data affects LLM performance, highlighting the interconnectedness and sensitivity of these systems to external influences. This interdisciplinary exploration underscores the importance of understanding and enhancing the resilience of both biological and artificial systems to external disruptions, paving the way for innovative research and improved technological applications.

Introduction

In the ever-evolving fields of artificial intelligence and biology, remarkable parallels have emerged between the functioning of large language models (LLMs) and the human body. At the heart of these similarities lies the concept of generative models, a framework that has reshaped our understanding of how systems, both biological and artificial, encode and process information. This feature post dives into the intriguing analogy between LLMs and the human genome, exploring how external forces can disrupt these systems in comparable ways.

• 07/27 – The Genomic Code as a Generative Model: Implications for Development, Evolution, and Bioelectric Interference

The Genomic Code and LLMs: A Generative Model Perspective

Generative models have revolutionized our approach to understanding complex systems. In the realm of AI, LLMs like GPT-4 utilize generative models to produce coherent and contextually relevant text by leveraging learned patterns from vast datasets. These models compress information into latent variables, which then guide the generation of outputs.

Similarly, the genome can be viewed as a generative model for an organism. Kevin J. Mitchell and Nick Cheney, in their paper “The Genomic Code: The genome instantiates a generative model of the organism,” propose that the genome functions akin to variational autoencoders in machine learning. The genome encodes information into latent variables, such as biochemical properties and regulatory interactions, which shape the developmental processes of an organism. This perspective emphasizes that the genome constrains self-organizing pathways rather than dictating them directly.

External Interference and Its Impact

Both LLMs and biological systems are susceptible to external interference. For LLMs, noisy or biased data during training can lead to suboptimal or skewed outputs. This interference can significantly affect the model’s performance, much like how environmental factors impact biological systems.

In the human body, bioelectric signals play a crucial role in cellular communication and development. These signals rely on precise electrical gradients and currents, much in the same way LLMs change their potential through weights and biases. However, external forces such as electromagnetic fields (EMFs) from wireless devices can disrupt these bioelectric processes. EMFs can induce electrical currents and alter the electrical environment around cells, leading to potential developmental anomalies and other health issues. This disruption is analogous to how noise in training data can affect the accuracy and reliability of an LLM.

Chemistry and Charge Potentials: Computational Work in Biology and AI

At the core of both biological and computational systems is the concept of energy as the computational agent. In biological systems, chemical processes and charge potentials within cells drive bioelectric signals. These signals, while rooted in the physical hardware of cells, are fundamentally about the organization and flow of energy that enables computational work within the body.

In LLMs, the weights and biases in neural networks represent learned information akin to the charge potentials in cells that learn from evolution in their environment. Training data serves as the environment for LLMs, just as the body has trillions of parameters with change potentials of its own and undergoes an entropic process within its surrounding environment to learn. These weights and biases determine how the model processes inputs and generates outputs. When external forces, such as new impactful data, adjust these weights and biases, the model’s behavior changes accordingly. This adjustment is similar to how bioelectric signals are influenced by external electromagnetic forces, affecting the computational processes within the body.

Propagation of Disruption: System-Wide Effects

A key aspect of both biological and computational systems is how disruptions propagate through the entire system. In biological organisms, a disruption in bioelectric signals can lead to widespread effects, influencing cellular communication, gene expression, and overall development. For example, interference from EMFs can affect ion channel function and cellular membrane potentials, leading to broader developmental issues and health effects.

In LLMs, even slight changes in weights and biases can lead to significant differences in output. A model trained in a noisy training environment can produce outputs that are not only inaccurate but also potentially harmful. This systemic propagation of disruption highlights the interconnectedness and sensitivity of both biological and computational systems to external influences.

Robustness and Adaptability

Despite their susceptibility to external interference, both biological and computational systems have developed mechanisms for robustness and adaptability. In organisms, evolutionary processes have shaped mechanisms to cope with environmental changes, ensuring survival and functionality. These mechanisms include redundancy, repair systems, and adaptive responses to external stresses such as mad-made EMFs.

In AI, regularization techniques and robust training methods enhance the resilience of LLMs to noisy data. Techniques such as dropout, weight decay, and adversarial training help models maintain performance even in the presence of external disruptions within the evolution of the environment from which they are trained. Understanding and enhancing these mechanisms of robustness is crucial for both fields.

Future Research and Implications

The analogy between LLMs and biological systems opens up exciting avenues for future research. Understanding the mechanisms by which EMFs interfere with bioelectric signals can lead to strategies to mitigate potential adverse effects on human health.

Interdisciplinary research that bridges biology and AI can yield insights that benefit both fields. For instance, studying how biological systems achieve robustness and adaptability can inspire new techniques in AI, while advancements in AI can provide tools to model and understand complex biological processes of life itself.

 

To conceptualize how the base pair sequence of letter strings in DNA might be envisioned as patterns of energy, we can draw on ideas from the papers you’ve provided. This approach aligns well with the metaphor of the genome as a generative model, particularly in terms of how the information encoded in DNA translates into biological form and function through interactions within energy fields.

Energy and Probabilities in DNA Sequences

1. Energy Patterns in DNA:
   • Each nucleotide in a DNA sequence can be thought of as contributing to a specific energy field, influenced by its chemical properties and interactions with neighboring nucleotides. These interactions create localized potentials that affect how the DNA interacts with proteins, RNA, and other molecules within the cell.
2. Probabilistic Behavior:
   • As mentioned in The Genomic Code paper, latent variables in a generative model are often represented as probability distributions rather than fixed values. Similarly, the energy fields generated by sequences of DNA nucleotides create a probabilistic environment. This environment influences the likelihood of certain biochemical reactions, such as transcription factor binding or RNA folding, occurring in specific ways.
3. Field Potentials and Functional Outcomes:
   • The DNA sequence can be seen as setting up a landscape of energy potentials, where each nucleotide’s contribution to this landscape affects the overall behavior of the sequence. This landscape influences what the sequence “should do” in a given environment by determining the most energetically favorable interactions.
4. Developmental and Evolutionary Implications:
   • During development, these energy fields guide cellular processes by creating gradients and attractor states, as discussed in the Waddington’s epigenetic landscape analogy. Evolution acts as a learning algorithm that refines these energy fields over time, selecting for sequences that create energy landscapes conducive to survival and reproduction.

Conceptual Framework

1. Latent Variables in DNA:
   • Latent variables in this context could be interpreted as the underlying energetic properties of DNA sequences. These properties, while not directly observable, determine the sequence’s functional potential by shaping the energy landscape within the cell.
2. Energy Landscapes and Biological Form:
   • The idea of energy landscapes can be extended to explain how DNA sequences give rise to specific phenotypic traits. Variations in these sequences alter the energy landscape, leading to different developmental pathways and, consequently, different physical forms or behaviors.
3. Information Encoding and Decoding:
   • Just as a variational autoencoder in machine learning compresses input data into a low-dimensional latent space, evolution compresses the vast complexity of biological form into the relatively simple code of DNA. The energy fields generated by this code are then decoded during development to recreate the complex structure of an organism.

Envisioning DNA sequences as patterns of energy allows for a deeper understanding of how these sequences influence biological outcomes. The energy landscape created by the sequence, shaped by evolutionary pressures, governs what the sequence should do, guiding the development and function of the organism in a probabilistic but highly structured manner. This perspective offers a powerful analogy for explaining complex genotype-phenotype relationships and the robustness and evolvability of biological systems.

Conclusion

The parallels between LLMs and the human body, particularly in how external forces within a training environment affect parameter adjustment in both biological and machine intelligence systems.  This offer a profound understanding of the intricate balance between robustness and vulnerability. By viewing the genome as a generative model and recognizing the role of bioelectric signals, we gain a deeper appreciation for the complexity and adaptability of biological systems. Similarly, enhancing the resilience of LLMs to external disruptions can lead to more reliable and effective AI systems. The interdisciplinary exploration of these analogies promises to unlock new frontiers in both biology and artificial intelligence, driving innovation and improving our understanding of life and technology.