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1. Fundamental science

This document proposes a fundamental—scientific—approach towards a profound understanding of natural intelligence and natural language based on the Laws of Nature.

1.1. Fundamental truth

There is only one truth in fundamental science: the way nature works.


Nature operates in a single, definite order, governed by natural laws. Those who investigate these laws and uncover how nature truly works will find their discoveries confirmed under controlled conditions — and, in time, applied to everyday life. In this way, taxpayers will have a Return on Investment in their funding of science.

1.2. Fundamental sciences are closing the circle

We perceive nothing other than natural phenomena obeying the Laws of Nature, and proceeding according to the Laws of Nature. And we perceive nothing other than natural phenomena closing the circle, as illustrated by the following example of electromagnetism.

The field of electromagnetism is a fundamental science because it closes the circle:

  • We can convert light to electricity, and we can convert electricity back to light;

  • We can convert motion—via magnetism—to electricity, and convert electricity—via magnetism—back to motion.

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If natural reasoning (or natural logic) is a fundamental property of nature, then it should be possible to translate natural language—i.e., readable sentences—into natural logic, and natural logic back into natural language, without any loss of information. Furthermore, it should be possible to integrate multiple reasoning mechanisms within a single system (*), including mechanisms that have been scientifically described as well as those that have yet to be discovered.

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(*) I have observed that many techniques in automated reasoning remain fragmented across different systems and have not yet been integrated into a unified framework.

1.2.1. Cognitive science

The field of Artificial Intelligence (AI) and Natural Language Processing (NLP)—in a broad sense—is mainly studied from the perspective of behavioural/cognitive science, resulting in mimicry of behaviour. However, mimicking the behaviour of a hen (chicken) does not produce eggs. AI is therefore not naturally intelligent, but the result of human ingenuity.

1.2.1.1. AI / NLP is unable to close the circle

As a consequence of being investigated from the perspective of cognitive science, the field of AI / NLP is unable to close the circle on natural intelligence and natural language:
    • From readable sentences,
    • through natural logic (natural intelligence),
    • with the result expressed in readable — word-by-word constructed — sentences again.

Illustrated by an example:

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Given:

  • “John has 3 apples.”

  • “Peter has 4 apples.”

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There is no algebraic notation for the compound sentence:

  • “John and Peter have 7 apples together.”


A schoolteacher would write:

  • 3 apples + 4 apples = 7 apples

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But this equation lacks the references to John and Peter, and is therefore insufficient to automatically generate the compound sentence mentioned above.

Hopefully, mathematicians will come to the rescue by solving it mathematically:

  • J = 3

  • P = 4

  • J + P = 7

 

Unfortunately, this algebraic notation lacks the reference to apples, and is therefore also insufficient to automatically generate the compound sentence mentioned above.

In other words, conventional mathematical notation is too limited to represent the full linguistic structure of a simple statement that a six-year-old child can readily understand.

A neuro-symbolic AI system—a hybrid combining neural language models with symbolic reasoning—could, in principle, bridge this gap by integrating linguistic references with symbolic relationships. However, such an approach would be considered a workaround rather than a general-purpose solution to the underlying problem.

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This is just one example of my scientific challenge. A generic solution to this particular example is described in Block 3.
 

Large Language Models (LLMs) can, by themselves, solve certain reasoning problems. However, their reasoning capabilities are limited by their underlying architecture and engineering. As reasoning problems become increasingly complex or are combined, LLMs may begin to lose track of context and produce less reliable results.

Furthermore, Large Language Models lack self-organising capabilities: once trained, they cannot autonomously integrate newly acquired knowledge with their existing knowledge.

In the absence of a generic solution, the problem would require either human intervention or an engineered, task-specific solution. Therefore, AI and NLP should be understood primarily as fields of engineering rather than as sciences in their own right.

1.3. Pitfalls (cows and milk)

I noticed a few instances of misunderstanding, which I will address in the following paragraphs. But first, I want to get to the root cause of the misunderstanding:
 

  • In science, the term methodological naturalism is often used to describe the principle that scientific inquiry is restricted to phenomena that can be observed or empirically investigated;

  • In philosophy, the term philosophical naturalism refers to the view that nothing beyond the natural or observable world should be accepted as real or explanatory.


Suppose no one has ever seen a cow, and we do not even know whether cows exist. Now suppose we examine a glass of milk (*):
 

  • A methodological naturalist would say: “I do not know where the milk comes from, but I can observe that it contains, for example, water, milk fat, proteins, lactose, minerals, vitamins, and bacteria. I can also observe that the bacteria cause the milk to deteriorate”;

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  • A philosophical naturalist, by contrast, would rule out the existence of cows as a possible explanation and will tend to defend this position by focussing on the origin of the milk without invoking a cow: “The only living organisms we can observe in the milk are bacteria, so the bacteria must have produced it”.


The only way to determine whether the philosophical naturalist is right or wrong is to demonstrate, through empirical investigation, that the origins of the Laws of Nature, energy, matter, the universe, life, taxonomic families, natural intelligence, consciousness, and human language can be reliably replicated—under controlled conditions and, where necessary, at an appropriate scale— in terms of purely natural processes, without any intelligent influence.

This is precisely where philosophical naturalism fails: it assumes that such phenomena can ultimately be explained without intelligent influence, while failing to demonstrate empirically that they can, in fact, arise through purely natural processes.

Moreover, if cows do exist and do produce milk, the entire philosophical framework of the philosophical naturalist may be fundamentally misguided, directing the search for the origin of milk away from the very entity that produces it and toward organisms that merely happen to be present in the milk. This may then lead to ambiguous definitions—see the following paragraphs—designed to obscure the fact that the starting hypothesis of philosophical naturalism—that purely natural processes are sufficient to explain the phenomenon—was fundamentally mistaken.

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(*) I “borrowed” the basic analogy of cows and milk from Peter Scheele.

1.3.1. Self-organisation (misunderstood)

Self-organisation is often misunderstood. The following ‘scientific’ paper states:

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“Self-organisation refers to a broad range of pattern-formation processes in both physical and biological systems”.


However, in this paper, no distinction is made between a static ‘organisation’—which is limited to pattern formation—and a dynamic organisation, which requires natural intelligence.

Distinction:

  • Natural pattern formation—such as fractals and the formation of snowflakes—is a static process, based on rules (fractals) or the Laws of Nature (the formation of snowflakes);

  • Swarming of birds is a dynamic, temporary process, based on the bird’s instinct. Instinct is an innate mechanism of survival. In case of no danger, swarming is practiced as an emergency drill, while it also improves bonding;

  • Self-organisation is a dynamic, continuous process. It is a result of natural intelligence;

  • Any other organisation—such as a company or a pack of wolves—is a dynamic, continuous process of multiple intelligent actors.


(Self-)organisation may be a property of natural intelligence rather than its origin.

1.3.2. Consciousness, neurons, learning, complexity, and intelligence

Consciousness

To many scientists, natural intelligence and consciousness are closely intertwined. Yet consciousness is more than intelligence alone; it is the capacity of an intelligent being to function as an aware participant within complex social and relational structures. While consciousness depends on natural intelligence, it cannot be reduced to it. Systems based on Controlled Natural Language, for example, can perform logical reasoning without possessing consciousness.


Neurons

Similarly, many scientists consider neurons essential to natural intelligence. However, Controlled Natural Language reasoners also demonstrate logical reasoning without (artificial) neurons. Apparently, neurons are not essential to intelligence—just as feathers and flapping wings are not essential to aviation.


Learning

Learning requires natural intelligence. Yet scientists still do not understand what natural intelligence is. By that logic, what we call “Machine Learning” is unable to learn.

Artificial neural networks (ANNs) reflect a simple piece of advice my father once gave: “Don't become a monkey trained to perform a trick”. ANNs resemble a trained monkey—capable of performing, but not of understanding.


Complexity
Philosophical naturalism confuses the term “complexity” with “order”. Natural processes may indeed produce complex patterns. But complexity does not necessarily imply order, let alone a functioning system. In fact, we have never observed complexity itself giving rise to order, much less to a functioning system.

Moreover, great thinkers like Albert Einstein have emphasized that:

  • True intelligence seeks clarity, not complexity;

  • Intelligence is not expressed in complexity, but in the ability to distil things to their essence;

  • Simplicity is the highest form of sophistication, and that complexity is often a sign of incomplete understanding.


Intelligence
If Large Language Models (LLMs) exhibit any form of intelligence, it did not arise from the complexity of their neural architectures, but from the inherent logic embedded in the human-written texts on which they are trained.

The development of a reasoning system such as mine presupposes the application of principles of intelligence that are intrinsically encoded in human language, which I call: the Laws of Intelligence naturally embedded in the Human Language.

1.3.3. Boundaries of evolution

According to evolutionary hypotheses of common descent, there is no fundamental distinction between evolution observed within taxonomic families and the hypothetical evolutionary processes proposed to extend beyond those boundaries. Both are understood as parts of a single, continuous historical process. From this perspective, fossils are interpreted as representing organisms that are related through common ancestry.

However, in the physical world, everything appears to operate within limits or boundaries. An electrical cable, for example, has a maximum current it can safely carry; a steel cable has a maximum tensile strength; and the universe has a fundamental speed limit: the speed of light.

 

Likewise, organisms evolve within the limits imposed by their biological capabilities. What we observe empirically are boundaries within which biological variation and diversification occur, corresponding broadly to established taxonomic families:

  • Viruses produce viruses;

  • Bacteria produce bacteria;

  • Fungi produce fungi;

  • Plants produce plants;

  • Insects produce insects;

  • Fish produce fish;

  • Birds produce birds;

  • Monkeys produce monkeys;

  • And humans produce humans.

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The same principle applies to genetic algorithms and evolutionary algorithms, which are computational models inspired by evolutionary processes. Such algorithms operate within the parameters, representations, and rules specified by their designers. They therefore do not, by themselves, constitute empirical evidence that biological evolution can cross major taxonomic boundaries.

Moreover, neither the evolutionary relationships inferred from the fossil record nor the complete historical sequence of evolutionary change has been directly observed. Nor has it been demonstrated through experimental replication or computational simulation that organisms can evolve into fundamentally different taxonomic families—for example, that bacteria can give rise to non-bacterial organisms.

 

Consequently, the claim that all organisms share a common ancestry—or that particular features evolved over millions of years—falls outside the domain of empirically testable science. Rather, such claims reflect a philosophical framework rooted in philosophical naturalism.

1.3.4. Scientific models

Science is concerned with natural phenomena that can be observed, tested, replicated, and potentially falsified. In that sense, scientific models should, in principle, be reproducible.

Fundamental science - A scientific model can be reproduced

If a model cannot be reproduced, we should regard it as a hypothesis rather than an empirically established scientific model. In that case, it falls outside the domain of empirically testable science.

Fundamental science - A hypothetical model can’t be reproduced

1.3.5. Is Christianity anti-science?

Some people believe that Christianity is anti-science or fundamentally opposed to scientific inquiry. Yet for centuries, Christian scientists made significant contributions to fundamental science. They sincerely observed the way nature—as God’s creation—operates. As a result, their findings could be reproduced under controlled conditions and subsequently applied to practical, everyday life:

 

By replicating and applying their findings, these Christian scientists provided a Return on Investment to taxpayers, which we still benefit from today. Their approach of using fundamental science might be useful to investigate natural intelligence and natural language too.

1.3.6. Fundamental choice: apes or Adam and Eve?

Evolutionary hypotheses of common descent are fundamentally at odds with Christian beliefs. And theistic evolutionism is just a variant of the theory of evolution, topped with a religious sauce. So, only one of both belief systems can be true:

  • If man shares a common ancestor with the ape, Adam and Eve never existed;

  • If Adam and Eve never existed, the Fall of man—high treason against God—never happened;

  • If the Fall of man never happened, the redemption through Jesus is meaningless;

  • If redemption through Jesus is meaningless, Christianity is nothing but an empty religion.

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So everyone has to make a choice: a common ancestor with the ape, or creation by God.

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©2026 Menno Mafait

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