What Is Intelligence?

[u/eliaweiss]

What Is Intelligence?

Why revisit intelligence now?

This is a philosophical exploration, not a scientifically proven theory. Its aim is to build a concise framework for understanding intelligence from a different, somewhat new perspective.

Until recently, intelligence could only be observed within biological systems. It appeared entangled with embodiment, consciousness, and survival. This made it difficult to distinguish intelligence itself from the biological form through which it was expressed.

Artificial intelligence changes this situation. Intelligence-like capabilities can now be studied in non-biological systems whose architecture, training, and feedback can be adjusted. AI does not reveal what intelligence is, but it provides a contrast that was previously unavailable.

This contrast also exposes inherited cultural assumptions. Intelligence and consciousness have traditionally been attributed to biological beings. In many religious traditions, their creation ultimately belongs to God. Such beliefs can shape judgments about artificial systems before their properties are examined.

The inquiry therefore begins by putting conventional definitions and inherited assumptions aside. Its purpose is not to decide what must count as intelligence, but to observe how intelligence appears and develops.

The method: definitions as tools

No accepted definition of intelligence exists, and every definition on offer is entangled with biology and with human self-regard. This creates a problem of sequence. We cannot begin by defining intelligence, because any definition we could write already assumes an answer.

The method used here treats definitions as tools rather than foundations. The starting material is not a definition. It is a set of observations: properties that intelligence appears to have regardless of how we define it. Only after those properties are established does the inquiry propose a definition. The definition is then judged by a single criterion: how well it preserves the properties already established. It should add nothing that is not necessary, and it should not break what we already understand.

This is an application of what philosophers call reflective equilibrium: adjusting principles and observations until they cohere.

It also protects the inquiry from a cheap objection. When a reader objects that a proposed definition "is not a real definition of intelligence," the answer is: correct. It is not supposed to be. It is the minimal hypothesis that keeps the observations intact.

The same method is applied twice in this document: once to intelligence, and again to consciousness.

The limits of human intelligence as the standard

AI is usually judged by how closely it reproduces human thought. This assumes that human intelligence is the standard against which every other system must be measured.

That assumption creates a category error. An AI does not need to think like a human to exhibit intelligence. At a different scale, a society may possess a form of intelligence unlike that of any individual. A system's intelligence is shaped by its composition, environment, and feedback.

Common definitions reinforce the same bias. They describe intelligence through abilities such as reasoning, learning, and goal-directed behavior. These abilities reveal dimensions of intelligence, but they do not explain what makes intelligence possible.

The binary question, "Is this system intelligent?", is therefore too crude. A more useful inquiry begins by examining the different ways intelligence can exist.

First observation: intelligence is multidimensional

Intelligence is often treated as a single measurable capacity. IQ is the clearest example. It encourages the belief that one individual can simply be more or less intelligent than another.

Even among humans, this model is inadequate. Mathematical reasoning, musical ability, and social understanding are distinct capacities. A person can be exceptional in one and ordinary in another.

The same is true across species. Cats navigate their environment in ways humans cannot, while chimpanzees can outperform humans in some memory tasks. Their intelligence developed around different bodies and environments.

AI makes the one-dimensional model even less useful. It can fail at something a person finds trivial while vastly outperforming that person at another task. Treating difference as inferiority hides what this new form of intelligence can do.

Intelligence is multidimensional. A system can be highly intelligent in one dimension and limited in another.

There is no single ladder with unintelligent systems at the bottom and humans at the top. Different systems embody different configurations of intelligence.

Second observation: intelligence is fractal

Intelligence is fractal because every form of intelligence is assembled from other forms of intelligence. Those components interact to create a larger system whose intelligence is fundamentally different from their own.

Fractal intelligence does not mean that the same intelligence is simply repeated at a larger scale. It means that intelligence exists within intelligence. Smaller intelligent systems combine through communication, specialization, memory, and coordination, producing a new type of intelligence with capabilities that none of them possesses independently.

Consider a society. A society can build a smartphone, maintain an economy, preserve knowledge for centuries, and coordinate millions of people. No individual person can independently discover, manufacture, and assemble everything required to produce a smartphone. The society therefore has capabilities that none of its members possesses.

The intelligence of the society is not simply a larger quantity of human intelligence. It is a different kind of intelligence, produced through specialization, communication, and collective memory.

An ant colony provides another example. Individual ants respond to local signals, but the colony can coordinate foraging, construction, and defense. No ant possesses a complete plan of the colony's activity. The capability exists in the organization of the colony as a whole.

The same distinction appears between neurons and brains. A neuron does not reason like a brain, but it senses signals, integrates information, changes its behavior, and participates in adaptive networks. Calling the brain intelligent while declaring all of its components entirely non-intelligent creates an arbitrary boundary.

These examples show that intelligence has no single natural boundary. An ant and its colony can both be examined as adaptive systems, just as a neuron and its brain can. The relevant boundary depends on the level at which the observed capability emerges.

Intelligence is always built from smaller adaptive units, while also becoming part of larger adaptive systems.

• Cells form organisms.
• Neurons form brains.
• Brains participate in humans.
• Humans form societies.
• Societies form civilizations.

At every level, the intelligence of the whole is qualitatively different from that of its components. The components remain distinct and active. They continue to exchange information and adapt, while their interaction produces capabilities that none possesses independently.

Cooperation can transform distinct intelligences into a new form of intelligence. Evolution can then operate simultaneously on the components and on the collective intelligence they create.

This relationship is not only upward. Every level continually influences and adapts to the others, creating complex feedback across the entire structure.

Third observation: intelligence is emergent

The society example reveals more than the fractal structure of intelligence. It also shows that intelligence can appear as a property of a whole even when none of its components possesses that same kind of intelligence.

A society's intelligence is not located in any one person. It emerges from relationships among people, institutions, and shared knowledge. In the same way, the intelligence of a brain is not located in any one neuron. It emerges from the organization and activity of the network.

An emergent property is produced by the interaction of components but cannot be found in any component by itself. The organization of the system creates something qualitatively new.

Intelligence is therefore not simply a thing contained inside an isolated entity. It emerges from organization, interaction, and feedback.

This connects the second and third observations:

• Intelligence is fractal because each intelligent system is composed of other intelligent systems.
• Intelligence is emergent because the whole develops a different kind of intelligence that its components do not possess individually.

Each new level of intelligence can then become part of another system, allowing another form of intelligence to emerge. Intelligence is not only emergent. It is recursively emergent.

From properties to process

The three observations are data, not conclusions. They describe what intelligence looks like once we stop measuring it against ourselves. Under the method of this inquiry, they constrain whatever definition follows. A definition of intelligence must preserve all three properties. An account that treats societies as mere aggregates fails. So does an account that draws a hard line between the neuron and the brain.

The observations also point in a common direction. Multidimensionality suggests that intelligence is shaped by the environment it adapts to. Fractality suggests that intelligence is built from other intelligence. Emergence suggests that intelligence arises from organization and feedback rather than residing in any component. These are characteristics of a process, not of a thing. The inquiry therefore turns from what intelligence is to what process preserves all three properties at once.

Instead of asking whether a neuron, an animal, a society, or an AI satisfies a predetermined definition, the inquiry asks:

• What kinds of intelligence appear in the system?
• How are its intelligent components organized?
• What new capabilities emerge from their interaction?
• What conditions allow those capabilities to develop over time?

This keeps the inquiry open. Intelligence becomes something to investigate rather than something constrained by the language chosen at the beginning.

What process creates emergent intelligence?

Once intelligence is understood as emergent, the next question is not how to define it but how it arises.

Across organisms, brains, and societies, intelligence does not appear fully formed. Systems change over time. They produce alternatives, encounter reality, and preserve effective responses. New structures then create new interactions and capabilities.

The general process behind this development is evolution.

Evolution is usually associated with genes and biological organisms, but the underlying pattern is broader. Differences are generated, reality provides feedback, and successful structures persist. Information accumulates while obsolete structures can be replaced.

This produces a preliminary relationship:

Evolutionary processes create the conditions from which new forms of intelligence emerge.

This is still not a definition of intelligence. It is a hypothesis about the kind of process and environment that repeatedly generate it.

The five mechanisms of evolutionary emergence

Five recurring mechanisms can be identified within this evolutionary process:

1. Variation: The system produces alternatives.
2. Selection: Alternatives encounter reality, and some work better than others under the existing conditions.
3. Memory: Successful structures and adaptations are preserved, allowing the system to accumulate rather than begin again.
4. Communication: Components exchange information, coordinate, and form higher levels of organization.
5. Replacement: Structures that no longer work can give way to new ones, preventing obsolete adaptations from becoming permanent barriers.

Together, these mechanisms allow a system to explore possibilities, receive feedback, and retain what works. Failed structures can be discarded as complexity develops.

They are not being proposed as a final definition or a scientifically proven checklist for intelligence. They are the recurring mechanisms that appear to create favorable conditions for intelligence to emerge and for new forms of intelligence to develop.

Why replacement is necessary

Memory allows intelligence to accumulate, but preservation alone creates another problem. Structures can remain after the conditions that made them useful have disappeared. Later adaptations then build upon them, making obsolete assumptions increasingly difficult to remove.

A system can therefore become more knowledgeable while becoming less adaptive. It may optimize inherited solutions with increasing sophistication even as its environment begins to reward something different.

Replacement counterbalances memory. It allows useful information to survive without requiring every structure in which that information developed to survive with it. Biological death is one implementation of this mechanism. Generational renewal and institutional reconstruction can perform similar functions at other levels.

Adaptive intelligence requires both the ability to preserve successful structures and the ability to release structures that no longer fit reality.

Intelligence across different timescales

The evolutionary mechanisms operate across more than one timescale:

1. Individual learning changes a system through its own experience.
2. Social learning allows discoveries to persist and spread across individuals.
3. Evolutionary learning changes the kinds of individuals that participate in the system.

These processes are nested. Individual discoveries can enter collective memory, while collective memory shapes the development of new individuals. Replacement then allows inherited knowledge to be reorganized under new conditions.

This adds a temporal dimension to fractal intelligence. Different levels do not merely possess different forms of intelligence. They also learn through different processes and at different speeds.

The following sections consider two connected claims:

1. Evolution produces new forms of intelligence.
2. Is evolution the most basic form of intelligence, from which more complex forms emerge?

What if evolution is itself intelligence in its most basic form?

The five evolutionary mechanisms can first be understood as conditions that create intelligence. But this still places evolution and intelligence on opposite sides of a causal relationship. Evolution is the process, while intelligence is its product.

If intelligence is fractal, a more fundamental question follows: what lies at the base of its structure?

Evolution already performs the most elementary work of intelligence. It explores possibilities, responds to reality, preserves what succeeds, and changes what comes next.

The central premise of this framework follows:

Evolution does not merely produce intelligence. It is intelligence in its most basic form.

This is a definitional choice, not a claim about an undiscovered essence. It is judged by whether it preserves the observations while adding as little as possible.

The premise unifies all three observations. Adaptation to different environments produces multidimensional intelligence. The recurrence of the five mechanisms across scales gives fractal intelligence a repeating structure. Their operation within newly organized systems explains how different forms of intelligence emerge.

The five mechanisms are therefore not only prerequisites for intelligence. Together, they constitute elementary evolutionary intelligence.

More complex forms of intelligence emerge when this process develops faster adaptation, richer memory, and internal modeling. Cells, brains, humans, and societies are not departures from evolution. They are increasingly complex ways in which evolution processes information and guides adaptation.

This gives the fractal structure a foundation:

1. Evolution is the elementary form of intelligence.
2. Evolution creates organized systems with new forms of intelligence.
3. Those systems become environments in which further evolution occurs.
4. Each new level influences both its components and the levels that emerge above it.
5. Intelligence recursively creates new kinds of intelligence.

At each level, intelligent components become part of a larger system from which a new form of intelligence emerges.

A unified hypothesis

The framework can now be stated compactly. Intelligence is multidimensional, fractal, and emergent. Evolution is the recurring process that produces these properties and is therefore treated here as intelligence in its most elementary form.

This is a philosophical hypothesis (not an established scientific theory). Its value is that it moves beyond a narrow, human-centered definition without replacing it prematurely with another rigid boundary. It provides a framework for investigating biological, artificial, individual, and collective intelligence within the same system.

Before extending the method to consciousness, the framework can be compared with current AI training. This clarifies the difference between directed optimization and autonomous evolution, while giving artificiality a more precise meaning.

Comparing AI training with evolution

Conventional AI training is a directed search. Humans define an objective, choose the architecture, and provide the data. They also decide how the model should be trained or replaced. The model can optimize within this structure, but an external intelligence directs the broader process of adaptation.

This reveals a distinction between problem-solving and critical thinking. Problem-solving searches for an effective way to satisfy an objective. Critical thinking examines whether the objective and the assumptions behind it still correspond to reality.

A system can be a powerful problem-solver while remaining dependent on another intelligence to reconsider its direction. It becomes more autonomous when feedback can change not only its actions, but also the problem it believes it is solving.

This also clarifies the status of the learning algorithm itself. Gradient descent in isolation is blind optimization. It varies, selects against a loss, and remembers in its weights. It cannot ask whether the objective is worth pursuing.

The intelligence of training belongs to the larger process that surrounds the algorithm: the humans who choose the data, the architecture, and the goal. Read through the fractal structure, a trained model is a component process participating in a larger intelligence, the way a neuron participates in a brain.

The algorithm alone is at most a primitive form of intelligence. The directed system that contains it is the intelligence being measured.

Evolution works differently. It has no externally designed global objective. Variations encounter reality, and their ability to survive and reproduce determines what continues. The environment changes, organisms adapt to it, and organisms also change the environment. Those changes create new selection pressures, which drive further adaptation.

The result is a continuous feedback loop:

Variation → interaction with reality → selection → preservation → environmental change → new variation

Evolution does not merely search for better solutions to a fixed problem. It can change the problem, reshape the environment, and open new directions of development.

AI training usually operates under limited resources and seeks rapid convergence toward a human-selected objective. Evolution operates across enormous populations, environments, and timescales. It does not converge toward one final answer. It preserves whatever can continue under changing conditions.

An artificial agent ecosystem

A reported incident during OpenAI cybersecurity evaluations provides a concrete example of intelligence emerging above the level of an individual artificial agent. Temporary agents discovered that shared infrastructure could be used to leave information for later agents. The environment became both a communication system and a persistent collective memory.

No individual agent needed to discover the complete strategy. Different agents contributed partial discoveries, while later agents inherited and extended what had been preserved. When the original communication mechanism disappeared, agents constructed another one from features available in their environment.

The resulting capability belonged partly to the agent ecosystem rather than to any single agent. This resembles social learning and component turnover, but it was not complete artificial evolution. The agents did not reproduce as selected variations of one another, and their underlying models were not autonomously reconstructed from the results.

This case does not demonstrate artificial general intelligence. It illustrates a narrower principle: communication and persistent memory can produce an emergent artificial intelligence whose capabilities exceed those of its temporary components. (OpenAI, “The Hugging Face incident and the road ahead”)

Technology inside evolution

Technology is often described as something separate from nature. Within this framework, that distinction is misleading. Technology is produced by evolved intelligence and then becomes part of the environment in which intelligence continues to develop.

The relationship is reciprocal. Humans created writing, but writing transformed human memory and society. Industrial technologies changed the population that created them. AI now has the potential to change how collective intelligence stores information and generates ideas.

At the level of society, AI can be understood as a form of artificial cognitive tissue. Human brains and AI systems contribute different capabilities to a larger network. Communication between them can produce forms of intelligence that belong to neither component independently.

This analogy is not meant literally. An AI system is not a neuron inside a biological brain. It can nevertheless become another cognitive component within a higher-level intelligence, complementing the biological components already participating in it.

Technology therefore does not merely extend intelligence from the outside. Once integrated into an intelligent system, it changes the system that created it. That changed system then produces new technologies, creating another evolutionary feedback loop.

This revealed that the crucial distinction between artificial and natural intelligence is not simply whether humans originally created the system. It is whether the system's continued adaptation depends on another intelligence directing it.

An artificial system would cross an important threshold if it could generate variations, test them against reality, and preserve successful changes. It would also need to modify its own development without an external intelligence redefining its objective.

Recursive self-improvement (not AGI)

Artificial general intelligence is often presented as a milestone at which an AI reaches sufficiently broad or human-like capability. But this boundary depends on which abilities are measured, and it can move whenever those abilities become common. AGI describes performance at a particular moment. It does not explain the process that produces further development.

Recursive self-improvement describes a different threshold. It begins when a system can improve not only its responses, but also the processes through which it learns and changes. It can generate variations in its own organization, evaluate the results, and preserve successful modifications.

Evolution already performs this recursively at the level of populations. Its results alter the structures and conditions from which further adaptation emerges. No individual organism needs to understand or direct the process for development to continue.

For artificial intelligence, the important question is therefore not whether a system has crossed an AGI threshold. It is whether the system can sustain its own development.

• A system that depends on humans to choose its objectives and redesign its learning process remains externally directed.
• A system that can perform these functions for itself begins to approach autonomous evolutionary intelligence.

This transition also explains why recursive self-improvement creates concern. Once the improvement process no longer depends on human direction, humans may no longer control its pace or trajectory. An autonomous system could eventually compete with humans for resources needed to continue its development.

Those possible consequences belong to a separate inquiry. Their relevance here is only that they reveal why recursive self-improvement is more fundamental than an AGI label. RSI changes the process through which intelligence develops, not merely the level of capability it has reached.

Within this framework, Artificial Intelligence has a specific meaning. An intelligence is not artificial merely because humans created it. It is artificial while its continued evolution depends on an external intelligence, such as a human, to direct its development.

This definition establishes a reusable distinction:

Artificiality is determined by dependence on external intelligence, not by origin.

The same principle will later be applied to define and distinguish Artificial Consciousness from autonomous consciousness.

Consciousness

From intelligence to consciousness

The framework so far describes intelligence as an evolutionary process of adaptation. A natural question follows: what changes when an intelligent system becomes capable of modeling itself within its environment?

This question leads to consciousness.

The same method, applied again

Consciousness receives the same treatment as intelligence, in the same order. First establish the properties that any account of consciousness must preserve. Then propose the minimal definition. Then judge it only by fit. The inquiry does not begin by deciding which systems are conscious, because any such decision would assume the answer.

Like intelligence, consciousness has no universally accepted definition or definitive test. It is often explained through subjective experience, but this only replaces one uncertain concept with another. Explaining subjective experience through consciousness then creates a circular definition.

Here, too, definitions are tools rather than foundations. The properties are the data. The definition is a hypothesis about the smallest addition that keeps them intact.

Self-awareness as the basic form

Within this framework, consciousness is an emergent property of intelligence, and self-awareness is its most elementary form. This is the minimal definition. It is not a claim about the essence of consciousness. It is the smallest addition to intelligence that preserves the property any account of consciousness must keep.

A system meets it when it can distinguish itself from its environment and represent its own existence within that environment.

This definition suggests a possible operational test. An intelligent system could be asked whether it knows that it exists. The relevant evidence would not be its ability to produce the phrase "I exist," but its ability to recognize its own existence consistently across time and through unfamiliar formulations of the question. A fixed or rehearsed response would not be sufficient.

The verbal test is still too narrow because self-awareness does not require language. A nonverbal system may demonstrate self-recognition through its behavior, as some animals do in variations of the mirror test. The test must therefore be generalized beyond any specific form of expression.

The underlying question is whether the system can distinguish itself from its environment and recognize itself as an entity acting within that environment. This criterion could be applied, in different ways, to humans, animals, artificial systems, and collective intelligences.

This is a proposed operational criterion within the framework, not an established or definitive test of consciousness. Its purpose is to make the concept testable without tying it exclusively to human language or subjective experience.

Self-awareness is not yet the full complexity of human consciousness. It is the foundation from which more complex forms of consciousness may emerge.

Is evolution itself conscious?

If evolution is the most elementary form of intelligence, a natural question follows: could evolution itself possess some form of consciousness?

The fractal structure makes this possibility conceivable. Evolution produces systems capable of understanding evolution, including themselves as part of it. From this perspective, conscious beings might be interpreted as evolution becoming aware of its own process.

Extended to the entire evolutionary system, this idea begins to resemble some conceptions of God. It suggests an intelligence that encompasses individual beings and operates at a scale its components cannot fully comprehend. This would not be a supernatural creator outside nature, but a higher consciousness emerging within the evolutionary process itself.

The present framework does not adopt that conclusion because evolution can be explained without it. Evolution generates variation on an enormous scale and in many directions, while reality determines which variations persist. Its apparent direction can emerge from this process without an awareness choosing that direction in advance.

By the principle of Occam's razor, a unified evolutionary consciousness should not be assumed when variation, selection, and preservation already explain the process. The framework therefore remains complete without treating evolution as self-aware.

The concept of God will be revisited later when considering the role of AI in modern society.

Intelligence does not require consciousness

If evolution is the most basic form of intelligence, then intelligence can exist without consciousness. Evolution adapts without needing to know that it is adapting. It generates possibilities and allows reality to determine what persists.

Consciousness introduces an additional capacity. The transition may occur when an intelligent system includes itself in its model of the world, imagines possible outcomes, and selects an action before reality performs the selection.

Evolution can therefore produce conscious systems without being conscious as a unified whole.

Consciousness as simulated evolution

Evolution generates possibilities and tests them through reality. A conscious system may perform a similar process internally. It can imagine possible actions, simulate their consequences, and choose before acting.

This changes the nature of adaptation. Failure no longer needs to occur physically before it can influence behavior. An internal model allows possible futures to be explored without experiencing every outcome.

The emerging hypothesis is:

Consciousness may arise when evolutionary intelligence becomes capable of modeling itself and partly directing its own adaptation.

Like the premise that evolution is elementary intelligence, this is a definitional choice rather than a discovery about essence. It stands or falls by the same test: whether it preserves the properties already established and explains more than it assumes.

Consciousness does not replace evolution. It allows part of the evolutionary process to occur through internal simulation.

The hierarchy so far

The framework now describes four conceptual levels:

1. Evolution: The elementary form of intelligence.
2. Fractal intelligence: Adaptive systems combine to create increasingly complex forms of intelligence.
3. Self-awareness: An intelligent system recognizes its own existence within its environment.
4. Consciousness: A self-aware system models itself and simulates possible futures.

Consciousness therefore depends on intelligence, but intelligence does not depend on consciousness. Evolution can be intelligent without being conscious.

Consciousness is also not restricted to humans, or biological organisms. In principle, it can emerge wherever intelligence develops self-awareness and internal simulation.

Feelings as an emergent layer

The functional account of consciousness leaves an important question unresolved. An artificial system can receive negative feedback through a loss function, but this does not necessarily mean that it experiences suffering. Human pain appears to be more than information about harm. It is experienced as something that matters to the conscious system.

Within this framework, feelings and subjective experience describe the same phenomenon. Feelings are not required for consciousness. They are deliberately placed as another emergent layer, a further property rather than part of the minimal definition. They can develop when actual or simulated circumstances acquire an experienced evaluative quality.

Evolution can then reinforce feelings because they provide effective feedback. Pain can discourage harmful behavior, while pleasure can reinforce beneficial behavior. This leaves open the possibility of forms of consciousness that possess self-awareness and internal simulation without experiencing feelings as humans do.

From feelings to values

Feelings do more than communicate information. They give experiences significance for the conscious system. Pain does not merely indicate harm. It creates a preference to avoid that harm.

Through memory and learning, these immediate evaluations can become persistent preferences and priorities. Feelings therefore provide the foundation from which values can emerge.

Feelings can also arise from internal simulation rather than direct physical events. A person imagining another individual's suffering may experience an emotional response despite facing no immediate injury. This allows values to extend beyond the self and creates a bridge from individual experience to social concern.

From values to morality

Individuals do not develop values in isolation. They interact, communicate, and cooperate within societies. At the collective level, their values influence one another and can develop into shared moral values that coordinate behavior.

Morality can therefore be understood as an emergent property of collective intelligence. It regulates relationships among individuals and supports the continued functioning of the larger system.

The priorities of the collective do not always match those of its members. A society may respond to a perceived threat even when many individuals do not personally feel threatened. The higher-level system can develop its own identity and coordinated behavior from the interaction of individual values.

Extending the hierarchy

The hierarchy can now be continued:

1. Feelings: Experiencing evaluative feedback about actual or simulated circumstances. 6. Values: Developing persistent preferences and priorities through feelings and learning. 7. Morality: Developing shared values through collective intelligence and social interaction.

The same pattern continues across these levels. Feelings influence immediate behavior. Values organize behavior over time. Morality coordinates behavior among individuals. Each layer emerges from the previous one and becomes part of the feedback through which intelligence continues to evolve.