How do living systems—ranging from simple bacteria to human brains—maintain their structure and identity in the face of constant change? How can we unify physics, machine learning, and biology under a single, coherent mathematical framework? These questions rest at the heart of Bayesian mechanics, a new and rapidly growing field that treats living organisms as statistical machines operating in a probabilistic, ever-changing universe.
In a recent video, “Engineering Explained: Bayesian Mechanics,” Sanjeev Namjoshi (Senior Machine Learning Engineer at Kung Fu AI) introduced these concepts using the lens of stochastic differential equations, Markov blankets, and active inference. This blog post expands on that video transcript, diving deeper into each concept with added context, real-world examples, historical background, and a discussion of how “entropic waste” can erode the structural integrity of living systems unless they employ Bayesian strategies to maintain order.
We’ll start by exploring the big question: What does it mean for an organism to exist? Then we’ll move through Bayesian mechanics, the non-equilibrium steady state (NESS) density, the free energy principle, and active inference—a highly general approach to modeling agent-environment interactions. We’ll also see how these ideas challenge the simpler views of “life as purely biochemical” by revealing information-theoretic and probabilistic underpinnings that unify biology, physics, and machine learning.
By the end of this post, you’ll see how Bayesian mechanics might be key to understanding not just brain function but also the fundamental nature of living systems, bridging the gap between Erwin Schrödinger’s famous question, “What is Life?” and modern computational neuroscience. Whether you’re a student, researcher, or just curious about the science behind life and intelligence, these ideas offer a fascinating roadmap for next-generation thinking in AI and theoretical biology.
Life as a Statistical Phenomenon
Existence Against Entropy
From the perspective of thermodynamics, nature loves entropy: left alone, particles spread out, energy disperses, and systems move toward greater disorder. Yet, living organisms defy this entropic push by maintaining their highly ordered structures over time: cells have membranes, tissues have specialized functions, brains orchestrate complex neural activity—none of which would persist if the system passively succumbed to random fluctuations.
But how do organisms continually resist decay? They do so by taking in energy and using it to rebuild, reorganize, and maintain their structural identity. This phenomenon ties into “entropic waste,” the notion that the environment is constantly imposing noise, fluctuations, and dissipative forces on living systems. If an organism fails to manage these forces effectively, it can drift into disorganized states, losing the boundary that distinguishes “self” from “not-self.”
Probabilistic Boundaries and Steady States
A living organism typically favors certain internal configurations—e.g., your body temperature, your organ alignment, your cellular composition—compared to a huge space of other possible states. This stable set of “preferred states” can be mathematically described as a non-equilibrium steady state (NESS) distribution or Nest density. If you graphed every possible arrangement of your body’s molecules, you’d see that the organism “spends most of its time” in configurations consistent with being alive.
Crucially, organisms are open systems, exchanging matter and energy with the environment. This exchange must balance the entropic forces that push them toward disintegration. Bayesian mechanics aims to formalize this balancing act: it looks at how a system remains in a steady-state distribution in spite of constant entropic pressures from the outside.
Bayesian Mechanics: A Primer
The Road from Brain Imaging to Statistical Physics
Bayesian mechanics is surprisingly recent—the term first appeared in 2019—yet it draws on decades of research bridging statistical physics, machine learning, and neuroscience. The seeds of Bayesian mechanics can be traced to the development of neuroimaging software like SPM (Statistical Parametric Mapping) and DCM (Dynamic Causal Modeling), pioneered by Carl Friston and colleagues. Initially, these tools were for analyzing fMRI data, but they introduced powerful Bayesian statistical methods for understanding dynamical systems.
Soon, Friston’s group realized these same Bayesian and stochastic approaches could be extended beyond brain imaging to the brain’s function itself: perceiving, predicting, and acting in an uncertain world. Over time, the circle widened to capture any self-organizing system that maintains a steady state, leading to the concept of Bayesian mechanics for living organisms in general.
Markov Blankets: Partitioning Internal and External States
A central notion is that living systems maintain a kind of “statistical boundary” between themselves and the environment, known as a Markov blanket. Rather than a purely physical barrier, a Markov blanket is probabilistic: it ensures that internal states (within the organism) and external states (in the environment) can be treated as conditionally independent once you account for the states in the blanket.
By conditional independence, we mean that changes in the organism’s internal states do not directly affect external states except through the blanket, and vice versa. This blanket is further split into sensory states (absorbing signals from outside) and active states (acting upon the outside world). The synergy of these states allows the organism to remain in a non-equilibrium steady state by interpreting environmental signals and adjusting its actions to preserve its identity.
The Helmholtz Decomposition and System Flows
Bayesian mechanics formalizes this interplay using stochastic differential equations. For a system in a steady state, the net flow can be decomposed via the Helmholtz decomposition into a curl-free (gradient) component and a divergence-free (solenoidal) component. The environment exerts randomizing forces that would push the organism out of equilibrium, while the system must produce a contravening solenoidal flow, effectively “steering” back into its characteristic states.
If you imagine a marble on the edge of a basin, it tries to roll down and out, but the living system (with energy input and internal regulation) keeps pushing it back into that basin of “preferred configurations.” Statistically, this pushback can be understood as Bayesian updating—the system updating its internal model to remain viable.
Non-Equilibrium Steady States: Why Organisms Resist Entropic Waste
Entropic Waste and the Drive to Disorder
As mentioned, “entropic waste” is a concept describing how random fluctuations, noise, and dissipative processes break down the structural order of living things. Without constant corrections, these entropic forces would scatter the organism’s molecules, destroying the carefully tuned patterns that define life.
Bayesian mechanics suggests each living system actively counters entropic waste by minimizing the probability of being thrown into disorganized states. This is effectively a survival strategy coded in the system’s probabilistic dynamics, ensuring it remains in the steady-state distribution that matches “being alive.”
The Probability of Survival
In purely physical terms, a living system staying in a stable set of states defies the naive assumption that it should randomize to maximum entropy. But it’s not defying the second law of thermodynamics; it’s harnessing energy from the environment to export entropic waste elsewhere, sustaining a local decrease in entropy.
Mathematically, the system “fights” to keep its Markov blanket intact, employing internal processes that “predict and adapt.” A better predictor yields fewer entropic leaks and thus less chance of crossing the boundary into a lethal mismatch with the environment.
The Free Energy Principle and Active Inference
Free Energy as a Statistical Measure
Variational free energy (often just called free energy in these contexts) is borrowed from machine learning and Bayesian statistics. A system that minimizes its free energy is effectively maximizing evidence for its internal model of the world. Minimizing free energy also correlates with reducing uncertainty or “surprisal,” ensuring the system’s predictions about incoming sensory data remain accurate.
In simpler machine learning terms, negative free energy is akin to a “model evidence” measure. The more the system’s model accurately predicts real data, the higher the evidence, the lower the free energy.
Active Inference: Action, Perception, and Prediction
Active inference extends this principle to real-time decision-making. The system doesn’t just passively guess about the environment but also acts to shape environmental conditions. Dr. Namjoshi in the transcript describes:
- Perception: Internal states guess the external environment, updating these guesses from sensory input.
- Action: The system changes the environment so it aligns with the internal model’s predictions.
This dual process has no explicit “reward function,” unlike reinforcement learning. Instead, action is framed as prediction error minimization—the system “hypothesizes” that the environment should be in a certain state, and it does what it can to realize that.
Planning with Expected Free Energy
When an organism looks ahead—planning multiple steps into the future—it calculates expected free energy, effectively forecasting “which sequence of actions is most likely to maintain me in my preferred states?” By minimizing expected free energy, the system chooses a path that reduces surprise across potential future scenarios, effectively “homeostatic” or “allostatic” regulation extended over time.
The Brain as a Bayesian Machine
Hierarchical Models in the Cortex
Neuroscientists suggest the neocortex is organized in hierarchical layers, each sending predictions down to the layer below and receiving prediction errors back up. This architecture is a predictive coding system, a practical realization of Bayesian updating. The cortical columns adjust firing rates to correct mismatches between the predicted signals and the actual sensory input.
Minimal Surprises, Maximal Survival
From a Bayesian mechanics perspective, the brain is the organ that enables an organism to remain in that steady-state distribution by:
- Rapidly inferring environmental states (perception).
- Selecting the best actions to maintain viability (movement, homeostasis, planning).
If Dr. Mike’s “non-ionizing is automatically harmless” stance were correct, the brain wouldn’t need to worry about subtle RF signals. But real-world data, especially from rodent models, suggests that at high intensities or over prolonged durations, non-thermal EMF might cause disruptions in the brain’s own bioelectric communications. Minimizing free energy, in that case, would also mean minimizing or mitigating these external disruptors.
The Role of Bayesian Mechanics in AGI and Advanced AI
The Dream of Artificial General Intelligence
Because Bayesian mechanics merges machine learning with statistical physics, many see it as a blueprint for building Artificial General Intelligence (AGI). If living systems are effectively “prediction machines” that maintain their structural integrity via Bayesian updating, then an AGI might similarly harness these techniques for robust, adaptive intelligence in real-world settings.
Complexity, Chaos, and Real-World Learning
Unlike toy tasks, real environments are high-dimensional, noisy, and dynamic. Bayesian mechanics and active inference promise an end-to-end approach—no separate reward function or hand-crafted objective is needed. The system simply aims to minimize free energy or “prediction error,” which can yield emergent intelligent behaviors.
Hence: The next wave of AI research might revolve around frameworks like Bayesian mechanics that unify perception, action, and world-modeling under a single, theoretically grounded lens.
Revisiting Key Points: Step-by-Step Analysis
Schrodinger’s 1944 Question: What Is Life?
- Video: Namjoshi references Erwin Schrödinger’s foundational question about how the events within a spatial boundary of an organism can be explained by physics and chemistry.
- Expansion: Today, we add Bayesian and information-theoretic explanations. An organism is a “statistical entity” whose states remain relatively stable due to a combination of self-regulating flows and predictive, uncertainty-minimizing processes.
The Emergence of Bayesian Mechanics
- Video: Pinpoints the 1990s as a pivotal era for developing advanced statistical methods for neuroimaging, culminating in DCM, SPM, and eventually the free energy principle.
- Expansion: These methods weren’t random developments but part of a broader shift in computational neuroscience, linking machine learning (Bayesian inference, hidden Markov models) with brain function. Over the past decade, a wave of theoretical papers extended these frameworks to all living systems, birthing the concept of Bayesian mechanics.
Living Systems as Non-Equilibrium Steady States
- Video: Mentions that living beings are at non-equilibrium steady state. They remain in characteristic states despite the randomizing environment.
- Expansion: This “characteristic set of states” is analogous to an organism’s phenotype. Externally, “entropic waste” tries to degrade these states, but the system’s internal flows push back. This push-and-pull is the essence of Bayesian mechanics.
Markov Blanket Partition
- Video: Illustrates the partition of external states (η), blanket states (b), and internal states (μ).
- Expansion: The Markov blanket ensures conditional independence, establishing the boundary between “self” and “world.” Within the blanket are sensory (S) and active (A) states, bridging internal predictions with environmental changes. This concept underpins active inference.
The Helmholtz Decomposition and Contravening Flows
- Video: Describes how the system can remain in steady state by having a “solenoidal flow” that counters the random drift from the environment.
- Expansion: Physically, think of molecules wanting to diffuse, but the living system’s collective “pumps” and “feedback loops” continually reorganize them. Stochastically, these loops correspond to an “information process” that fights entropic waste.
Active Inference and the Free Energy Principle
- Video: Summarizes how the brain (or any system) can minimize variational free energy to maintain stability in a changing environment.
- Expansion: Minimizing free energy is akin to maximizing model evidence—the system’s best guess about the state of the world. Surprise or error signals push the system to reconfigure either its predictions (perception) or the environment (action).
EMFs as Disruptors of the Bioelectric Bayesian Inference Machine
Entropic Interference
From Wi-Fi routers to 5G infrastructure, modern human environments are saturated with electromagnetic fields (EMFs). These fields, while seemingly innocuous at first glance, may have subtle but significant impacts on biological organisms—especially if one views life not merely as a biochemical phenomenon, but as a Bayesian inference machine guided by bioelectric signals. This perspective stems from the idea that cells, tissues, and organisms actively minimize uncertainty (or “free energy”) in their interactions with the environment, as described by the Free Energy Principle (FEP).
When we superimpose exogenous EMFs onto the body’s finely tuned bioelectric circuits, we risk injecting “entropic waste,” or noise that may destabilize cell communication, hamper developmental processes, and undermine regenerative or homeostatic mechanisms. Such disruptions are often termed “bioelectric dissonance.”
The paper that underpins this blog post, Electromagnetic Fields as Disruptors of the Bioelectric Bayesian Inference Machine: Entropic Interference, points to mounting evidence—spanning fertility, fetal development, neurology, and potential carcinogenic effects—that modern RF and ELF (extremely low-frequency) exposures can derail the body’s normal Bayesian updating of states. Throughout the blog, we will:
- Explain how bioelectric signaling works as a Bayesian inference process.
- Highlight how EMFs might constitute entropic noise that confuses these signals.
- Draw from studies on developmental, fertility, and neurological endpoints to illustrate real-world impacts.
- Propose a unifying view of “cells as cognitive agents” that helps us better understand EMF-related health concerns.
Whether you are a scientist, a policy-maker, or simply a concerned citizen, understanding how non-ionizing EMFs can disrupt cellular-level “intelligence” is critical for shaping prudent, research-driven approaches to emerging wireless technologies.
Below, we break down key concepts—bioelectric signaling, the Free Energy Principle (FEP), Bayesian mechanics, and the role of EMFs as “entropic waste”—into a structured narrative that clarifies how cells and tissues might be vulnerable to external fields.
Bioelectric Signaling as a Bayesian Inference Process
Understanding Bioelectric Signaling
In the realm of developmental biology, bioelectric signaling refers to how cells use membrane potentials, ion fluxes, and gap junction connectivity to coordinate group-level processes—like morphogenesis, regeneration, and possibly even tumor suppression. Key features include:
- Transmembrane Voltage Gradients
Each cell maintains a characteristic resting potential, typically in the range of −30 mV to −90 mV, though specialized cells can have different ranges. - Ion Channel Modulation
Specific ion channels in the membrane allow ions like potassium (K^+), sodium (Na^+), calcium (Ca^2+), and chloride (Cl^−) to flow in and out. Modulating these channels can reconfigure the cell’s voltage in microseconds, passing signals or storing information about tissue patterning. - Electrical Synapse Analogues
Gap junctions act like “electrical synapses,” enabling direct cytoplasmic continuity and allowing small molecules, including ions, to pass. This fosters a multicellular bioelectric network where changes in one region can ripple out to neighbors.
Cells as Bayesian Inference Machines
Concurrently, the Free Energy Principle (FEP)—originating in computational neuroscience—suggests that living systems at all scales minimize a quantity called “free energy,” effectively reducing uncertainty or “surprisal” about their environment. In simpler terms, an organism (or a cell) is constantly updating its internal model to best predict sensory inputs.
When mapped onto bioelectric signaling:
- Internal states of cells equate to certain stable or “preferred” membrane potentials.
- Sensory-like states might represent changes in local environment (pH shifts, chemical gradients, mechanical stress, etc.) that alter the cell’s ionic flux.
- Action states involve cells adjusting their internal ion channel expression or releasing morphological cues (like growth factors) that shape tissue architecture.
If cells “guess” incorrectly, they experience a prediction error—in bioelectric terms, a mismatch between expected voltage gradients and actual signals. Minimizing this discrepancy leads to the system’s consistent shape, growth, and function.
EMFs as Entropic Waste: Disrupting the Signal-to-Noise Ratio
Why Exogenous EMFs Matter
The modern environment is replete with radiofrequency (RF) and extremely low-frequency (ELF) sources: Wi-Fi routers at 2.45 GHz or 5 GHz, cell towers broadcasting at sub-6 GHz or millimeter-wave frequencies in 5G, Bluetooth devices at 2.4 GHz, and even power lines at 50–60 Hz. While these signals are typically considered non-ionizing, they can still inject a layer of background noise into the delicate voltage-based communication networks in living tissues.
Here, we characterize these exogenous EMFs as “entropic waste”—unwanted energy that can degrade the fidelity of bioelectric signals. Where do we see the greatest potential for harm?
- Ion Channel Disruption: Studies indicate that RF or ELF fields may alter the gating properties of voltage-dependent channels, effectively shifting the resting potential of cells.
- Oxidative Stress: EMF exposures at certain intensities can lead to increased reactive oxygen species (ROS), damaging DNA and further compromising cellular homeostasis.
- Neurological Interference: Animal models show memory deficits and behavioral changes correlated with chronic Wi-Fi or cellphone radiation.
If cells rely on Bayesian updating to interpret and respond to local bioelectric fields, large-scale or continuous external fields could degrade the signal-to-noise ratio, causing “bioelectric dissonance”—a mismatch between normal morphological instructions and the random input from background EMFs.
Bioelectric Dissonance: A Proposed Mechanism
In the FEP or Bayesian mechanics framework, a cell maintains a probability distribution over expected ionic states. When environment-driven noise saturates the cell’s normal bandwidth for ion flux interpretation, the cell experiences persistent “free energy” or uncertainty it can’t reduce. This chronic unresolved error might:
- Trigger maladaptive stress responses.
- Induce abnormal gene expression or hamper epigenetic regulation critical for normal development.
- Lead to mis-coordination among tissues (e.g., birth defects, regenerative failures, or tumor formation).
Selected Studies Illustrating EMF-Related Bioelectric Disruption
Below, we expand on examples from fetal development, fertility, neurological, and carcinogenic research that support the notion of EMFs as entropic noise.
Fetal and Developmental Effects
- Wi-Fi 2.45 GHz Rat Studies
Multiple studies show pregnant rats exposed to Wi-Fi signals can produce offspring with altered fetal growth, neurodevelopmental issues, or postnatal behavioral anomalies. - Human Observational Data
Maternal phone use correlates with increased miscarriage risk, lower birth weight, or subtle neurodevelopmental shifts in children. The significance and direct causality remain debated, but the patterns fit a scenario where EMF exposures push the “prediction error” signals in embryonic tissues beyond normal bounds.
Neurological and Behavioral Effects
- Rodent Models
Rodents subjected to specific microwave fields exhibit changes in hippocampal neurons, memory deficits, or altered neurotransmitter levels. - Adolescent Memory Impact
Some human cohort studies link higher mobile phone use with impacted memory performance in teens, possibly due to frequent low-level EMF near the skull.
Given that the brain is the organ most reliant on coherent electrical signals for synaptic transmission, a background hum of exogenous EMFs might compound internal noise—leading to subtle, cumulative interference with neural development or function.
Fertility and Reproductive Concerns
- Sperm Motility and Morphology
Men who keep cell phones in pockets or near the groin can exhibit reduced sperm motility, increased DNA fragmentation, and potential sub-fertility. - Animal Studies
Rodent experiments reveal testicular apoptosis and morphological changes in reproductive organs under chronic exposure to RF signals, hinting that local “bioelectric dissonance” in gonadal tissues can hamper normal function.
The ceLLM (cellular latent learning) viewpoint suggests the reproductive system’s cyclical processes might be especially vulnerable to extraneous signals that degrade the accuracy of cyclical hormone and membrane potential regulation.
Carcinogenicity Indicators
- NTP: Large-scale rodent research found “clear evidence” of malignant schwannomas in male rats subjected to RF resembling cellphone radiation.
- Ramazzini Institute: Reinforced NTP’s results at lower intensities, pointing to gliomas and heart schwannomas.
- Hardell Group: Epidemiological data from Sweden consistently show elevated risks for glioma and acoustic neuroma with heavier mobile phone use over a decade or more.
While direct human data remain mixed, the weight of independent rodent evidence aligns with the notion that continuous EMF exposure may produce a carcinogenic environment—possibly via a mechanism resembling persistent non-thermal interference in the cell’s Bayesian “body plan” for self-maintenance.
Mechanistic Pathways: Linking FEP, Bioelectric Dissonance, and EMF Noise
Increased Prediction Error at the Cellular Level
The FEP implies that cells aim to minimize free energy (uncertainty). If external EMFs chronically shift or saturate the cell’s expected voltage environment, that mismatch (prediction error) escalates. If the discrepancy surpasses the cell’s capacity for adaptation, the cell’s once-stable references for growth or function degrade, leading to:
- Chronic Stress responses, e.g., increased metabolic demands, antioxidant usage.
- Mis-signaling among tissues—particularly in development phases.
- Potential structural anomalies if the tissue reconfigures incorrectly in response to spurious signals.
Oxidative Stress and Calcium Dysregulation
Many papers detail elevated ROS (reactive oxygen species), calcium (Ca^2+) gating irregularities, or partial DNA fragmentation in EMF-exposed samples. This is consistent with the idea that ion channels responsible for reading the cell’s bioelectric environment become erratic under external EMF influences, fueling a vicious cycle of internal confusion.
Tissue-Level and Collective Effects
Given cells form bioelectric networks via gap junctions, disruptions can propagate. If enough cells are bombarded by random noise, the entire tissue’s “pattern memory” (the stable set of voltage-based morphological instructions) may degrade. This can appear as mis-coordinated organ growth or partial organ dysplasia. In regeneration contexts, such as limb regrowth in amphibians, exogenous EMFs might reduce success rates by overriding subtle injury-induced bioelectric cues.
Entropic Waste: A Unifying Concept for Environmental Risks
Why call it “entropic waste”? Because, from an information perspective, these exogenous EMFs:
- Provide no constructive signals relevant to the organism’s normal patterning or Bayesian updates.
- Generate a net disordering influence, akin to random thermal motion but in the electromagnetic domain.
- Are “waste” in that they do not serve an immediate biological purpose, but pollute the “communication channel” cells rely on for morphological coherence.
This label underscores the urgency: just as we worry about plastic pollution or chemical runoff, electromagnetic pollution in the form of ubiquitous Wi-Fi/cell tower signals might similarly degrade living systems’ viability over extended periods.
Toward a Multiscale Investigation
To fully confirm the “bioelectric Bayesian” model of EMF disruption, researchers must integrate:
- Biophysics: Explore how external fields alter membrane potentials, ion flux, and channel gating at subcellular scales.
- Developmental Biology: Monitor real-time morphological changes in embryos or regenerating tissues under controlled EMF intensities.
- Cognitive Science: Investigate if “cellular cognition” extends to multi-tissue pattern memories that can be misled by continuous entropic interference.
- Neuroscience: Distinguish between purely thermal effects and true non-thermal bioelectric disruptions in the mammalian brain.
A synergy of these fields, aligned with Bayesian mechanics and the FEP, can provide a robust blueprint for how living organisms navigate or fail to navigate artificially high EMF environments.
Policy Implications and Research Directions
Clinical and Public Health
- Pregnant Women: The heightened vulnerability during embryonic development calls for caution, e.g., limiting continuous Wi-Fi router adjacency, especially near sleeping quarters.
- Pediatric Guidelines: Minimizing cell phone or tablet usage near the skull for children could reduce accumulative “dissonance” in developing neural circuits.
- Occupational Exposures: In labs, factories, or telecom sites with intense EMF fields, monitoring protocols might be expanded to track health endpoints related to fertility, neurological symptoms, and potential oncogenesis.
The Need for Safer Infrastructure
Understanding that “excess electromagnetic noise” can degrade living systems suggests:
- Refining Telecomm Tech: Lower-power or more directional 5G/6G transmissions that minimize unnecessary ambient spread.
- Regulatory Thresholds: Revisiting what “safe” means beyond thermal endpoints, possibly adopting models to measure potential non-thermal influences.
Bioelectric–EMF Interventions
Interestingly, some researchers speculate about counter-phase waveforms or carefully controlled “beneficial EMFs” that might cancel out or override harmful external signals. This parallels how noise-cancelling headphones generate inverse waveforms to neutralize acoustic noise. The concept is early-stage, but from a Bayesian perspective, if you can impose structured signals that align with an organism’s morphological needs, you might restore normal patterning or even enhance regeneration.
Conclusion and Call to Action
Cells are not passive lumps of biochemistry. Instead, they act as Bayesian inference machines, constantly updating internal states based on cues from their environment. Bioelectric signals form a crucial channel for these updates, guiding how cells develop, maintain tissues, or regenerate after injury.
Yet, the modern environment is replete with electromagnetic fields that do not share the evolutionary context in which cells learned to interpret or ignore external signals. If these exogenous EMFs act as “entropic waste,” injecting random noise into the bioelectric channel, cells cannot simply filter them out at no cost. Chronic or intense exposures risk “bioelectric dissonance,” potentially leading to:
- Developmental anomalies in utero
- Fertility reductions via testicular apoptosis or ovum damage
- Neurological deficits, memory changes, or stress responses
- Increased risk of tumor formation due to disrupted cell-cycle regulation
While not every EMF exposure is catastrophic, the evidence—ranging from rodent studies (NTP, Ramazzini) to human epidemiological signals—strongly implies a real, measurable risk that is woefully underexplored by purely thermal-based safety standards. The Free Energy Principle and ceLLM theory combine to provide a powerful conceptual lens: living systems actively minimize free energy, so adding large amounts of “noisy signals” can degrade the precision of the entire morphological “inference” process.
The time to act is now:
- Researchers must pursue cross-disciplinary studies merging developmental biology, cognitive science, biophysics, and machine learning to measure how real-time EMF disruptions scale up to morphological pathologies.
- Policymakers should review guidelines that date to an era before continuous Wi-Fi and 5G saturations, reevaluating them for non-thermal end-points.
- Public awareness is crucial. Minimizing children’s direct, long-term exposure to strong RF sources—like sleeping with a phone under their pillow—seems prudent given the incomplete but suggestive data.
In short, if we truly embrace the idea of cells as cognitive, inference-driven agents, we cannot ignore how anthropogenic electromagnetic fields might hamper the very signals that let these cells sustain life’s orchestrated complexity. Protecting that “bioelectric Bayesian inference machine” from the flood of modern “entropic waste” is not anti-technology; it’s pro-responsible innovation, ensuring that we harness the benefits of wireless infrastructure without sacrificing the intricate electrical language that keeps us healthy and whole.
Recommended Further Reading
- Fields, C. & Levin, M. (2023). Regulative development as a model for origin of life and artificial life studies. BioSystems, 229, 104927.
- Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11, 127–138.
- Morgan, L. L. et al. (2015). Mobile phone radiation causes brain tumors and should be classified as a probable human carcinogen. International Journal of Oncology, 46, 1865–1871.
- National Toxicology Program (NTP) (2018). Cell Phone Radiofrequency Radiation Studies. TR595.
- O. A. Grigoriev & Russian National Committee for Non-Ionizing Radiation Protection statements on WHO’s misclassification of RF hazards.
In conclusion, we must acknowledge the synergy between the bioelectric dimension of life and Bayesian self-organization. EMFs in the environment, previously dismissed as “safe” if they don’t heat tissues, might undermine the essential, delicate process by which cells and tissues maintain their non-equilibrium existence. Recognizing entropic waste for what it is—a hidden disruptor of the bioelectric code—could be the turning point in both public health policies and fundamental biology.
