The brain, prompt and question of intelligence: What is intelligence and are humans still as intelligent in the rise of artificial intelligence?

Imagine being the smartest, clever or intellectually great species in the world. Then you have AI. Who is actually the most intelligent?

For a good amount of time, humans, have been, seen as what would be called the “top of the food chain” when it comes to being intellectually superior. However, in a great twist of events, throughout history, humanity has repeatedly discovered that the universe is less centered on humans, than we once believed.

Let us take it back a bit, all the way, to the sixteenth century, where Nicolaus Copernicus displaced Earth from the centre of the cosmos, beginning a scientific revolution that transformed our understanding of the universe. Three centuries later, Charles Darwin demonstrated that humans are not separate from nature but one branch on the evolutionary tree, sharing common ancestry with every living organism. More recently, the study of animal behaviour, which, has steadily dismantled the notion that many cognitive traits, such as, tool use, culture, cooperation, communication, and even elements of self-awareness, belong exclusively to our species.

Now, artificial intelligence (AI) appears to be prompting another moment of reflection. Within just a few years, AI systems have become capable of writing essays, generating software code, translating languages, composing music, identifying proteins, and assisting scientific discovery. Tasks that many people considered defining expressions of human intelligence can now be performed by machines that neither evolved through natural selection nor possess nervous systems, emotions, or conscious experiences in the biological sense.

The public response has been understandable. Newspapers, podcasts, classrooms, and dinner-table conversations have all converged on asking, can machines become intelligent?

In another twist of events, this may not be the most interesting question. Long before artificial intelligence entered public consciousness, biologists, psychologists, philosophers, and neuroscientists had already spent decades debating a more fundamental problem, of asking, what is intelligence?

Remarkably, despite more than a century of research, there remains no universally accepted definition. Intelligence is one of science's most widely studied, and most persistently contested concepts (Legg & Hutter, 2007).

This uncertainty is not a weakness of science but a reflection of the complexity of the phenomenon itself. Intelligence is not a single substance waiting to be measured like temperature or mass. Rather, it is a collection of processes that include learning, memory, problem-solving, communication, planning, adaptation, and decision-making. Different disciplines emphasize different aspects, and different societies have historically valued different cognitive abilities.

Interestingly, biology has been pointing toward this conclusion for decades. Evolution has never attempted to produce a single, universally "smartest" organism. Instead, natural selection has generated an astonishing diversity of cognitive strategies, each adapted to the ecological challenges faced by different species. The intelligence of an octopus differs from that of a honeybee, which differs again from that of a wolf, a crow, or a human. None is universally superior, but each is shaped by the problems which evolution required it to solve.

Seen from this perspective, artificial intelligence does not stand outside biology as an entirely separate phenomenon. Rather, it joins a growing conversation about the many ways, in which, information can be acquired, processed, stored, and used to solve problems. In some respects, AI challenges long-held assumptions about human uniqueness. In others, it reinforces ideas that biology has gradually uncovered, which include, intelligence is diverse, context-dependent, and deeply shaped by function rather than prestige.

It may be that biology has been asking the right question all along.

1. What is intelligence?

At first glance, intelligence seems like a concept that should be easy to define. We use the word constantly in everyday life, describing people as intelligent, questioning whether animals are intelligent, and now debating whether machines deserve the same label. Yet despite its familiarity, intelligence remains one of the most elusive concepts in science. There is no single definition accepted by psychologists, biologists, neuroscientists, philosophers, or computer scientists alike. Each discipline views intelligence through a different lens, shaped by the questions it seeks to answer.

This diversity of perspectives is not a flaw but a reflection of the phenomenon itself. Intelligence is not a single ability. It encompasses a suite of processes, which range from learning from experience, recognizing patterns, solving problems, adapting to change, making decisions, communicating information, and responding flexibly to novel situations. Different organisms, and indeed different technologies, may exhibit some of these capacities while lacking others.

Intelligence as a psychological trait

The scientific study of intelligence emerged in the late nineteenth and early twentieth centuries, largely through psychology. Researchers sought ways to understand why individuals differed in their cognitive abilities and whether those differences could be measured.

Among the most influential was the British psychologist Charles Spearman, who proposed the existence of a general intelligence factor, commonly referred to as g. According to Spearman, people who performed well on one type of cognitive task tended to perform well across many others, suggesting an underlying general cognitive ability.

This idea profoundly shaped intelligence research and laid the foundations for modern IQ testing. Intelligence became associated with logical reasoning, mathematical ability, memory, verbal comprehension, and abstract thinking. These measures proved useful in many educational and clinical settings and continue to inform psychological assessment today.

However, they also raised an important question. Do intelligence tests measure intelligence itself, or only particular expressions of it?

This debate gained momentum in the late twentieth century. Developmental psychologist Howard Gardner argued that human cognition could not be reduced to a single general ability. In his theory of Multiple Intelligences, Gardner proposed that humans possess several relatively independent forms of intelligence, including linguistic, logical-mathematical, spatial, musical, bodily-kinesthetic, interpersonal, intrapersonal, and naturalistic intelligence. Although aspects of his theory remain debated within psychology, Gardner's work broadened public understanding of intelligence beyond academic performance.

Similarly, psychologist Robert Sternberg proposed the Triarchic Theory of Intelligence, distinguishing between analytical intelligence, creative intelligence, and practical intelligence. Success in life, he argued, depends not only on solving abstract problems but also on adapting to changing environments, generating novel ideas, and applying knowledge effectively.

These theories differed in important ways, yet they shared a common insight, which says, intelligence is unlikely to be a single, monolithic trait.

Intelligence as a biological adaptation

Psychology asks how intelligent individuals differ from one another, and biology asks a different question, why did intelligence evolve in the first place?

From an evolutionary perspective, intelligence is not valuable because it enables organisms to perform well on examinations or solve mathematical equations. It is valuable because it helps organisms survive, reproduce, and cope with uncertainty.

An organism that can learn from experience, remember the location of resources, recognize predators, communicate effectively, or modify its behaviour when conditions change is more likely to persist than one that cannot. In this sense, intelligence is best understood as an adaptive capacity rather than an abstract measure of intellectual ability.

This perspective fundamentally changes the conversation. Evolution does not strive to produce the "most intelligent" organism. Natural selection has no universal benchmark against which intelligence is measured. Instead, it favours traits that improve fitness within particular ecological contexts.

The intelligence of a honeybee navigating a landscape of flowers, a wolf coordinating with its pack during a hunt, or an octopus solving problems in a dynamic marine environment cannot be meaningfully compared using the same yardstick. Each species faces different environmental challenges, and each has evolved cognitive abilities suited to those challenges.

In biology, intelligence therefore becomes less about ranking species and more about understanding adaptation.

Intelligence as a cultural construct

If biology explains why intelligence evolved, when we turn to culture, it helps explain why certain forms of intelligence become highly valued.

Throughout history, societies have celebrated different cognitive abilities depending on their needs and environments. In societies where knowledge was transmitted orally, exceptional memory, storytelling, and social communication were indispensable. Skilled navigators memorized coastlines, stars, winds, and ocean currents with extraordinary precision. Farmers accumulated generations of ecological knowledge about seasons, soils, and wildlife. Craftspeople mastered intricate practical skills through observation and apprenticeship rather than formal education.

As societies industrialized, different abilities became increasingly valuable. Literacy, numeracy, scientific reasoning, and technical expertise rose in prominence alongside expanding systems of formal education. Intelligence became closely associated with academic achievement, standardized testing, and analytical reasoning. None of these abilities are inherently more "intelligent" than those valued in earlier societies. Rather, they reflect changing cultural priorities.

This distinction is particularly relevant in the age of artificial intelligence. Many tasks that modern societies have long associated with intelligence, such as, writing coherent essays, translating languages, generating software code, analyzing data, and answering examination questions, can now be performed with remarkable competence by AI systems.

This development does not necessarily diminish the value of human intelligence. Instead, it exposes the cultural assumptions embedded within our definitions. We often define intelligence according to the abilities our society happens to reward most highly.

Had AI emerged in a society that valued oral storytelling over written language, or ecological navigation over symbolic mathematics, public debates about machine intelligence might look very different.

A moving definition

Taken together, psychology, biology, and culture reveal that intelligence is not a fixed or universally agreed concept. From psychology, it helps us understand how individuals differ in their cognitive abilities. With biology, it explains why cognitive abilities evolved in the first place. Finally, with culture, it determines which of those abilities, are the ones, societies celebrate, reward, and often mistake for intelligence itself.

Artificial intelligence enters this conversation at a fascinating moment. By performing many of the tasks we once regarded as defining human intelligence, it challenges the assumption that these tasks constitute intelligence in its entirety.

Rather than providing a final answer, AI has exposed an older and deeper question, that biology has been exploring for decades.

If intelligence is not a single measurable trait, but a collection of adaptive strategies shaped by evolution and interpreted through culture, then perhaps asking "Who is the most intelligent?" is the wrong question. A more biologically meaningful question might be, what kinds of intelligence has evolution produced, and why?

That question takes us beyond definitions and into the extraordinary diversity of life itself.

2. Intelligence as an evolutionary adaptation

If psychologists ask how intelligence varies among individuals, evolutionary biology asks a more fundamental question, why did intelligence evolve at all?

The answer begins with one of the central principles of evolutionary theory, in which, natural selection does not optimize for perfection. It does not strive to produce the strongest, fastest, or smartest organism in any universal sense. Instead, it favours traits that improve an organism's ability to survive and reproduce within the environment it inhabits.

Seen from this perspective, intelligence is not a destination, but an adaptation. Like wings, camouflage, venom, or photosynthesis, intelligence is one of many evolutionary strategies for solving life's challenges. The form it takes depends entirely on the problems an organism must overcome.

This simple idea fundamentally changes how we compare minds. Instead of asking, "Which species is the most intelligent?", biology asks, "What problems did evolution require this species to solve?"

The end of the ladder

For centuries, intelligence was often imagined as a ladder. Simple organisms occupied the bottom, increasingly complex animals filled the middle, and humans stood alone at the top. This view reflected the broader historical belief that evolution itself was a progressive march toward humanity.

Modern evolutionary biology rejects this image. Evolution has no predetermined direction and no ultimate goal. Every living species has survived because it possesses traits suited to its own ecological niche, not because it is progressing toward a higher form of life.

The same is true of intelligence. A dolphin is not "more intelligent" than a crow simply because its brain is larger. Nor is a crow necessarily more intelligent than an octopus because it excels at one type of problem-solving. Each species has evolved a cognitive toolkit shaped by millions of years of natural selection under very different ecological pressures.

Intelligence is therefore better understood as a branching tree than a ladder. Just as evolution has produced many forms of locomotion, respiration, and reproduction, it has also produced many forms of cognition.

The rediscovery of animal minds

For much of the twentieth century, this perspective was surprisingly controversial. Behaviourism, one of psychology's dominant schools of thought, argued that scientists should focus exclusively on observable behaviour rather than speculate about the internal mental lives of animals. Discussions of animal thoughts, emotions, or intentions were often dismissed as unscientific.

This began to change through the work of zoologist Donald Griffin, whose pioneering research helped establish the field of cognitive ethology, which involved, the scientific study of animal minds. Griffin argued that if animals demonstrated flexible behaviour, learned from experience, solved novel problems, and adjusted their actions to changing circumstances, then it was reasonable to investigate the cognitive processes underlying those behaviours rather than treating them as mere instinct.

His work helped shift biology away from the simplistic view of animals as biological automatons. Today, animal cognition is one of the most active areas of behavioural science, revealing an extraordinary diversity of intelligence across the animal kingdom.

Evolution's many solutions

Perhaps the most remarkable lesson from modern biology is that intelligence has evolved independently in multiple evolutionary lineages.

Consider the octopus. Separated from humans by more than 500 million years of evolution, octopuses belong to the molluscs, which are a group whose ancestors diverged from ours long before vertebrates appeared. Yet octopuses display sophisticated problem-solving, rapid learning, exploratory behaviour, and remarkable behavioural flexibility. They manipulate objects, escape enclosures, navigate complex environments, and appear capable of learning through observation and experience.

Their intelligence evolved independently of vertebrate brains. If intelligence arose twice through entirely different evolutionary pathways, perhaps it is not a singular evolutionary miracle but a recurring solution to ecological complexity.

Birds tell a similarly surprising story. For decades, bird brains were considered comparatively simple because they lacked the layered neocortex found in mammals. Yet research over the past several decades has overturned this assumption.

New Caledonian crows manufacture hooks and probes from twigs and leaves, selecting and modifying materials to suit particular tasks. Ravens plan for future events, solve multi-step puzzles, and exhibit impressive behavioural flexibility. Studies suggest that some corvids perform comparably to great apes on specific problem-solving tasks despite following a completely different evolutionary trajectory.

As neuroscientists have increasingly recognized, similar cognitive outcomes need not arise from identical brain structures.

Evolution often discovers multiple ways of solving the same problem.

Redefining human exceptionalism

Perhaps no discovery challenged human exceptionalism more directly than Jane Goodall's observations of wild chimpanzees in Gombe Stream National Park during the 1960s.

At the time, tool manufacture was widely regarded as one of humanity's defining characteristics. Goodall observed chimpanzees carefully selecting branches, stripping away leaves, and using the modified sticks to extract termites from their nests.

These were not accidental behaviours. They involved selecting appropriate materials, modifying them, and using them to solve a practical problem.

The discovery was so unexpected that Louis Leakey famously remarked, "Now we must redefine tool, redefine Man, or accept chimpanzees as human."

While modern biology no longer frames the issue in such stark terms, the broader point remains profound. Human uniqueness became less about possessing entirely novel traits and more about the remarkable degree to which certain traits, including language, culture, cooperation, and technology, have been elaborated within our own lineage.

Intelligence is also social

Not all intelligence resides within individuals. Some of evolution's most sophisticated solutions emerge through cooperation.

Frans de Waal spent decades documenting the complex social lives of primates, demonstrating behaviours that challenged long-held assumptions about human exclusivity. His research revealed reconciliation after conflict, empathy toward injured companions, coalition building, social learning, and even rudimentary notions of fairness among chimpanzees and bonobos.

These behaviours suggest that intelligence is not merely about manipulating the physical world, but also about navigating the social world.

For highly social species, understanding relationships, anticipating the behaviour of others, resolving conflict, and maintaining cooperation may be every bit as evolutionarily important as solving physical puzzles.

The demands of social living may even have driven some of the most significant expansions in cognitive capacity across primates, including our own lineage.

Intelligence has many faces

One of the most compelling demonstrations of cognitive diversity comes from Alex, the African Grey Parrot studied by comparative psychologist Irene Pepperberg.

Over several decades, Alex learned to identify colours, shapes, materials, and quantities using spoken English labels. More importantly, experiments suggested that he was not merely mimicking sounds but understanding relationships between objects. He could answer questions about sameness and difference, identify absent objects, and correctly distinguish between categories in ways that challenged long-standing assumptions about avian cognition. Alex did not possess human intelligence. He possessed parrot intelligence. Stay with me now, that distinction matters.

His cognitive abilities evolved to meet the ecological demands of a highly social, long-lived bird. They remind us that evolution does not produce copies of the same intelligence across different species. It produces different solutions, each shaped by unique evolutionary histories.

3. Minds beyond brains: Expanding the boundaries of cognition

If the study of animal cognition taught us that intelligence extends far beyond humans, recent advances in biology raise an even more provocative question, does cognition necessarily require a brain?

For much of scientific history, the answer seemed obvious. Brains process information, make decisions, and generate behaviour. Therefore, intelligence must originate within a nervous system. Yet over the past few decades, discoveries from developmental biology, microbiology, behavioural ecology, and systems biology have begun to challenge this assumption. Increasingly, researchers are finding sophisticated forms of information processing in systems that possess no brain at all.

These discoveries do not suggest that brains are unimportant. Rather, they invite us to consider whether cognition exists along a continuum, expressed in different ways across the living world.

When the group becomes smarter than the individual

One of the clearest examples comes from social insects. An individual ant possesses only a tiny nervous system and relatively limited behavioural flexibility. Yet together, thousands of ants can construct elaborate nests, allocate labour, discover efficient foraging routes, defend their colonies, and respond dynamically to environmental changes.

Remarkably, no single ant directs these activities. The colony's behaviour emerges through countless local interactions among individuals following relatively simple behavioural rules.

This phenomenon, often referred to as collective intelligence or swarm intelligence, demonstrates that sophisticated decision-making need not reside within a single brain. Instead, intelligence can emerge from networks of interacting individuals.

Honeybees provide one of biology's most elegant examples. When a colony must relocate, hundreds of scout bees search for potential nesting sites. Each scout evaluates a location independently before returning to the swarm and communicating its quality through the famous waggle dance. As more scouts inspect competing sites, support gradually accumulates until the colony converges on a consensus.

No bee possesses complete knowledge of every option. Yet collectively, the colony often reaches remarkably effective decisions. Biologist Thomas Seeley has described this process as a form of "democracy," illustrating how distributed systems can solve complex problems without central control. These examples remind us that intelligence is not always individual. Sometimes it is collective.

Intelligence without neurons?

Perhaps even more surprising are organisms that possess neither brains nor nervous systems. Among the most famous is the slime mould Physarum polycephalum.

At first glance, slime moulds appear deceptively simple. They consist of a single giant cell containing many nuclei and spend much of their lives creeping slowly across decaying vegetation.

Yet laboratory experiments have repeatedly demonstrated behaviours that seem astonishingly sophisticated. Slime moulds can navigate mazes, avoid previously explored regions, optimize transport networks connecting food sources, and even modify future behaviour based on previous environmental conditions.

In one widely discussed experiment, researchers arranged food sources to represent major cities around Tokyo. As the slime mould expanded toward each food source, the network it constructed closely resembled the existing Tokyo railway system, one that engineers had spent decades refining.

Importantly, scientists do not claim that slime moulds "understand" transportation networks. Rather, their behaviour emerges through local interactions between growth dynamics, environmental feedback, and resource optimization.

The lesson is not that slime moulds think like humans. It is that surprisingly effective problem-solving can emerge without neurons.

The idea of basal cognition

These discoveries have inspired a growing area of research known as basal cognition. Rather than restricting cognition to organisms with nervous systems, proponents argue that many living systems continuously acquire information, integrate signals, make decisions, and adapt their behaviour to changing environments.

Biologist Pamela Lyon has suggested that cognition may be understood as a fundamental biological process rooted in life itself rather than an ability that suddenly appeared with brains.

Developmental biologist Michael Levin has extended this idea through studies of regeneration and embryonic development. His research investigates how collections of cells coordinate their behaviour to build tissues, repair injuries, and maintain body structures. During development, cells communicate extensively, respond to changing conditions, and often compensate when normal developmental pathways are disrupted.

Levin argues that these processes resemble problem-solving across multiple biological scales. While individual cells are not conscious in any human sense, they continuously gather information, make context-dependent decisions, and pursue biological goals such as maintaining structure and restoring function. His work has encouraged researchers to think of cognition not as an all-or-nothing property but as something that may exist across a continuum of biological organization.

This remains an active and sometimes controversial area of research. Yet it raises an important possibility. Perhaps brains did not invent cognition, and evolution gradually elaborated increasingly sophisticated forms of information processing that already existed in simpler living systems.

What about plants?

Few topics generate as much public fascination and scientific debate as plant cognition. Plants clearly lack brains and neurons. Nevertheless, they continuously monitor their surroundings. They detect light, gravity, touch, temperature, water availability, nutrient concentrations, herbivore attack, and chemical signals released by neighbouring plants.

In response, they alter patterns of growth, regulate defensive chemicals, communicate with symbiotic fungi, and coordinate physiological responses throughout the organism. Some experiments even suggest that plants can modify future responses based on previous experiences, a phenomenon broadly described as learning-like behaviour.

Should these observations be considered intelligence? Scientists remain divided. Some researchers argue that applying terms such as memory, learning, or cognition to plants helps illuminate the sophisticated information-processing occurring throughout living systems.

Others caution that these words carry psychological implications that may not accurately describe plant biology and risk encouraging anthropomorphic interpretations.

This debate is scientifically healthy. It reminds us that expanding our understanding of cognition requires careful definitions rather than sensational claims.

Whatever terminology ultimately proves most appropriate, one conclusion is difficult to avoid. Plants are not passive organisms, but they are highly dynamic systems that continuously sense, integrate, and respond to their environments with remarkable sophistication.

Intelligence as information processing

Taken together, these discoveries invite us to reconsider one of our deepest assumptions. Perhaps intelligence is not best understood as something possessed only by organisms with large brains.

Perhaps it is better viewed as a spectrum of biological information processing. At, one end are individual cells responding to chemical gradients. Further along are multicellular organisms coordinating development. Then come nervous systems, animal learning, social groups, and human societies. Each level introduces new layers of complexity without erasing the informational processes that came before.

Seen in this light, intelligence is less like a switch that suddenly turns on and more like an evolutionary continuum that gradually expands in complexity across the history of life. Ironically, this perspective also prepares us to understand artificial intelligence. If biology has repeatedly demonstrated that sophisticated problem-solving can emerge from many different architectures—brains, colonies, tissues, or even single cells, perhaps we should not be surprised that engineers have developed yet another.

The question is no longer whether intelligence requires a human brain. The more interesting question is how many different ways nature, and now technology, can process information to solve the challenges they encounter.

4. Artificial Intelligence: Another architecture for intelligence

For most of human history, intelligence was inseparable from biology. Every known example of intelligence came from a living organism. A human brain, a bird brain, an octopus nervous system, a bee colony, or a network of interacting cells, and they were products of evolution.

Then humans built something new. Artificial intelligence represents the first widely recognized example of a non-biological system capable of performing tasks traditionally associated with intelligence. It can identify patterns, generate language, solve complex problems, learn from data, and assist with scientific discovery.

This has led to a profound question, is artificial intelligence a new kind of intelligence, or simply a powerful tool created by human intelligence?

The answer may depend on how we define intelligence itself. If intelligence requires consciousness, subjective experience, emotions, or biological embodiment, then current AI systems remain fundamentally different from living organisms. If intelligence is understood more broadly as the ability to process information, adapt to challenges, and produce effective solutions, then AI begins to occupy a more interesting position.

Rather than viewing AI as a competitor to biological intelligence, it may be more useful to view it as another architecture for information processing.

Intelligence does not require a single blueprint

Evolution provides an important lesson. Nature has never produced one universal design for intelligence.

A mammalian brain, a bird brain, an octopus nervous system, and a social insect colony all process information differently. Their similarities emerge not because they share the same structure, but because evolution repeatedly encountered similar challenges, which included, finding resources, avoiding threats, communicating, learning, and adapting. This phenomenon is known as convergent evolution.

The same principle appears throughout biology. Birds and bats both evolved to perform flight, but their wings developed independently. Sharks and dolphins both evolved streamlined bodies for efficient swimming, despite belonging to completely different branches of the evolutionary tree. Different evolutionary pathways can produce similar functional solutions.

Artificial intelligence may represent another example, but not biological evolution, rather a form of technological evolution.

The mechanisms are entirely different. AI systems do not mutate, reproduce, or experience natural selection in the biological sense. Yet engineers have developed systems that solve information-processing problems using principles unlike those found in any organism.

This does not make AI identical to biological intelligence. It makes it different, and the difference is exactly what evolution has taught us to expect.

The biological inspiration behind AI

Although artificial intelligence is not a replica of the brain, biology has profoundly influenced its development. The earliest artificial neural networks were inspired by the organization of biological neurons. In 1943, neurophysiologist Warren McCulloch and mathematician Walter Pitts proposed a mathematical model of artificial neurons, suggesting that aspects of nervous system activity could be represented computationally. Later developments in machine learning continued to borrow concepts from biology, particularly the idea that complex behaviour can emerge from networks of simple interconnected units.

Modern deep learning systems are not equivalent to brains. A biological neuron is vastly more complex than an artificial one, and the human brain contains billions of neurons organized through intricate biological processes. However, the broader inspiration remains similar, with the regard that, intelligence can emerge from the interaction of many interconnected components.

This idea mirrors one of biology's most important lessons. Complex systems often arise from simple units arranged in the right networks. Which simply means, for example, a single neuron cannot think, a single ant cannot build a colony or a single cell cannot form an organism. Yet together, these components generate remarkable complexity. AI researchers have applied similar principles in computational systems.

Learning from evolution

Biology has not only inspired the architecture of AI, but also inspired its methods. One example is evolutionary computation, where researchers create algorithms that mimic aspects of natural selection. Solutions are generated, tested, modified, and selected based on performance. Over many iterations, effective strategies emerge. The process resembles evolution not because computers are alive, but because both systems explore possibilities through variation and selection.

Another example is swarm intelligence, which draws inspiration from collective behaviours observed in ants, bees, and other social organisms. Ant colony optimization algorithms, for example, use principles derived from ant foraging behaviour to solve complex mathematical problems. Individual ants follow simple rules, yet collective behaviour produces efficient solutions.

Reinforcement learning, one of the foundations of modern AI, also shares conceptual similarities with biological learning. Animals constantly adjust their behaviour based on consequences. Actions associated with successful outcomes become more likely to occur again, while unsuccessful behaviours are reduced. The parallels are not exact, but they reveal a recurring pattern.

Human technology often advances by asking the same question that evolution has been answering for billions of years, how can a system use information to adapt?

AI and the expansion of tool use

Perhaps the most interesting connection between biological and artificial intelligence is the concept of tool use. For much of history, humans considered tool-making one of our defining characteristics. Yet biology has shown that tool use exists throughout the animal kingdom. Some of these examples include, chimpanzees use sticks to extract termites, crows manufacture specialized tools, sea otters use stones to break shells or even octopuses carry coconut shells for protection. Tool use, often, once considered a uniquely human achievement, is now recognized as one of the many strategies, animals use to manipulate their environments.

AI introduces a new dimension. Unlike animals, AI systems do not independently evolve physical tools through biological adaptation. However, they increasingly participate in the creation of tools. Some examples of how they assist humans include, writing software, designing molecules, developing engineering solutions, analyzing scientific data and lots more.

In this sense, AI represents not the end of tool-making but an extension of a much older evolutionary theme, involving, intelligent systems creating external structures that expand their ability to solve problems.

Humans did not stop evolving cognitively when we invented tools, we extended cognition beyond our brains. Therefore, this looked like, books which became external memory, computers becoming external calculation systems and the internet becoming a global information network. AI may represent another stage in this long history of externalized intelligence.

Nature as technology's teacher

The relationship between biology and technology is a bit interesting and also what represents, two sides of the same coin. Nature has spent billions of years experimenting with solutions to survival challenges, and in the regard, humans have repeatedly learned from these solutions.

A good example, is the study of bird flight, which influenced aviation. From the structure of bones, were the inspired lightweight engineering designs.

Others include, the attachment mechanism of plant burrs inspired Velcro and the termite mounds have influenced approaches to passive building ventilation. The field of biomimicry exists because living systems often solve problems with extraordinary efficiency.

Artificial intelligence continues this tradition. It is not separate from biology's story. It is another expression of humanity's attempt to understand, imitate, and extend the principles found throughout the natural world.

A new branch on the tree of problem-solving

The arrival of AI does not require us to abandon biological definitions of intelligence. Instead, it expands the range of systems we can study. Biology has already shown us that intelligence can exist in many forms, which can include, individual brains, social groups, collective systems, developing organisms and complex networks of living cells.

Artificial intelligence adds another possibility, which involves, a non-biological system designed to process information and generate solutions. Whether AI should ultimately be considered intelligent in the same sense as living organisms remains an open philosophical and scientific question. Perhaps the more valuable insight is not whether AI belongs inside or outside the category of intelligence.

Ultimately, the important lesson is that intelligence was never a single category to begin with. Life has always explored different ways of solving problems, but currently, humans have begun exploring one of their own.

5. What makes human intelligence distinctive?

If intelligence exists across many forms of life, and if artificial systems can now perform tasks once considered uniquely human, an important question remains, what, if anything, makes human intelligence distinctive?

The answer is unlikely to be that humans are the only intelligent organisms, as biology has made that increasingly clear. Other species learn from experience, communicate, cooperate, remember, solve problems, and in some cases create tools. The idea that humans possess intelligence while other organisms merely follow instinct has become scientifically outdated.

Yet human cognition does appear unusual in one remarkable respect, with the regard, that, humans are not only intelligent individually, but collectively. Our greatest cognitive achievement may not be the ability of a single human brain to solve problems, but the ability of millions of human minds to accumulate knowledge across generations.

The power of cumulative culture

A newborn human enters the world knowing almost nothing about mathematics, agriculture, medicine, engineering, language, or astronomy. Yet within a lifetime, they can learn ideas developed through thousands of years of collective discovery.

This is the extraordinary power of cumulative culture. Unlike simple social learning, where one individual copy another, cumulative culture allows knowledge to be modified, improved, and built upon over generations.

This can be seen in our day-to-day lives, ranging from a person not needing to rediscover agriculture, as they inherit agricultural knowledge, a scientist does not begin physics from the beginning, as they inherit centuries of observations, experiments, and theories or an engineer not needing to invent the computer from scratch, but build upon mathematics, electronics, materials science, and the work of countless individuals who came before them.

Human intelligence is therefore not contained only within individual brains. It is distributed across communities, languages, institutions, books, technologies, and social networks.

Anthropologist Joseph Henrich has argued that cumulative cultural evolution is one of the most important factors explaining humanity's ecological success. According to this perspective, humans thrive not simply because individual humans are exceptionally clever, but because human societies create systems that preserve and refine knowledge over time. The collective intelligence of humanity exceeds the capabilities of any single individual.

Language as an information technology

At the centre of cumulative culture is language. Many animals communicate, from birds singing, whales produce complex vocalizations, primates using gestures and calls or bees communicate the location of resources through symbolic movements. However, human language possesses an extraordinary combination of flexibility, abstraction, and generative power.

We can communicate not only about immediate experiences but also about events that never happened, places that do not exist, theories we cannot directly observe, and ideas that may not be physically present.

Language allows knowledge to escape the limitations of individual experience. A person can learn about extinct species they have never seen, distant galaxies they will never visit, or microscopic organisms invisible to the naked eye.

In this sense, language acts as a technology for transferring intelligence between minds. Writing extended this process further, including books, manuscripts, databases, and digital networks became forms of external memory, allowing information to persist beyond the lifespan of individuals. Human intelligence became increasingly distributed outside the brain.

The extended mind

This idea has influenced philosophical discussions about the relationship between cognition and technology. Philosophers Andy Clark and David Chalmers proposed the concept of the "extended mind," arguing that cognitive processes are not always confined within the boundaries of the brain. External tools, such as notebooks, computers, maps, and other information systems, can become integrated into how humans think and solve problems.

Consider a scientist using a computer simulation, a navigator relying on a map, or a student taking notes. These tools are not merely passive storage devices. They actively shape reasoning and decision-making.

Human intelligence has always extended beyond biology, an example, is how the modern digital world simply makes this more visible. This perspective also changes how we think about artificial intelligence. AI systems do not replace human intelligence so much as they become another layer in an already long history of humans extending cognition beyond the individual brain.

AI and the human knowledge network

Modern artificial intelligence depends heavily on the accumulated products of human intelligence. Large language models, for example, learn patterns from vast collections of human-generated information, including books, scientific literature, websites, and other forms of written communication. Their capabilities emerge from exposure to the enormous archive of human cultural production.

This does not mean AI is simply copying humanity. Machine learning systems identify patterns and generate outputs through processes fundamentally different from human thought.

However, the relationship between AI and human culture highlights an important truth, in that, intelligence does not exist in isolation.

Human cognition itself has always been embedded within larger networks of information. A scientist, engineer or even students working today benefits from centuries of accumulated knowledge. Human intelligence is deeply social.

Cooperation as a cognitive advantage

Another defining feature of human intelligence is our ability to cooperate extensively with individuals who are not close relatives. Many social animals cooperate, for example, wolves hunt together, primates form alliances or birds coordinate group behaviours. On the other hand, humans have developed cooperation at an extraordinary scale.

We can collaborate with strangers across continents toward shared goals, build universities, governments, research institutions, and global scientific communities. This capacity requires sophisticated communication, trust, social learning, and the ability to follow shared rules and abstract concepts.

The human brain evolved not only to solve physical problems but also to navigate complex social environments. Some researchers have suggested that the challenges of cooperation itself may have contributed to the expansion of human cognition. Understanding other individuals, predicting their behaviour, negotiating relationships, and maintaining social bonds all require considerable cognitive flexibility. Human intelligence is therefore inseparable from human sociality.

The human difference is not superiority

Recognizing what makes humans unusual does not require placing humans above other forms of intelligence. Evolution does not create rankings, but rather creates diversity. An example can include, the intelligence of a crow is not a failed version of human intelligence or the intelligence of an octopus is not an incomplete attempt to become mammalian. They represent different evolutionary solutions.

Likewise, human intelligence represents a particular evolutionary pathway shaped by language, cooperation, teaching, and cumulative culture. Our uniqueness lies not in possessing intelligence itself, but in the extraordinary way intelligence operates through our species.

Humans create knowledge that survives beyond individuals, building upon discoveries made by people long gone. We communicate ideas across generations and construct technologies that amplify our cognitive abilities. In present day, we have created artificial systems that participate in this expanding network of information processing.

Intelligence beyond the individual

Perhaps the most important lesson from biology is that intelligence has rarely been purely individual. From a brain with a network of neurons to an ecosystem becoming a network of interactions, each represents a different level at which information can be stored, exchanged, and transformed. Human intelligence is extraordinary not because humans are isolated geniuses, but because we are exceptionally good at connecting minds across time and space. That may be the most important distinction to carry into the age of artificial intelligence.

The future of intelligence may not belong to humans or machines alone. It may emerge from the increasingly complex relationships between biological minds, artificial systems, and the vast networks of knowledge they create together.

Conclusion: Intelligence as a landscape, not a ladder

The arrival of artificial intelligence has created a moment of uncertainty. For many people, AI appears to challenge something deeply connected to human identity. If machines can write, analyze, create, and solve problems, then what remains uniquely human? If intelligence is no longer exclusive to biological minds, how should we understand our own place within the natural world?

Yet perhaps these questions reveal more about our previous assumptions than about artificial intelligence itself. For much of history, intelligence was imagined as a hierarchy. Humans occupied the highest position, separated from other organisms by a sharp boundary. Animals were often viewed primarily through the lens of instinct, while intelligence was treated as a defining human possession.

Modern biology has steadily dissolved that boundary. Research in animal cognition has revealed complex learning, communication, cooperation, and problem-solving across many species. From birds manufacturing tools, octopuses solving puzzles, primates maintaining complex social relationships to bees collectively making decisions. Even organisms without brains, such as plants, demonstrate remarkable abilities to sense, respond, and adapt to their environments.

None of this reduces the uniqueness of human intelligence, instead, it changes what uniqueness means. Humans are not exceptional because we alone possess intelligence. We are exceptional because of the particular way intelligence operates through our species, in terms of language, cooperation, teaching, symbolic thought, and cumulative culture. Human knowledge does not disappear when an individual happens to die. It can be preserved, modified, and expanded by future generations.

Seen from this perspective, AI is not an abrupt departure from the history of intelligence, it is part of a much longer pattern. Throughout evolution, intelligence has repeatedly emerged in new forms whenever organisms faced complex challenges. Some of this can be seen in terms of natural selection producing nervous systems capable of learning and prediction, social species developing collective intelligence through cooperation or humans developing cultural intelligence through shared knowledge.

Artificial intelligence represents another transition, which involves, the emergence of engineered systems designed to process information in increasingly sophisticated ways.

Whether AI should ultimately be considered intelligent in the same sense as living organisms remains an open scientific and philosophical question. Current AI systems do not possess the biological histories, embodied experiences, emotions, or evolutionary pressures that shaped living minds. Their strengths and limitations differ fundamentally from those of organisms. Perhaps the most valuable contribution of AI is not that it provides a final answer to the question of intelligence.

It is that it forces us to ask better questions. Instead of asking, "Which species, or which system, is the most intelligent?", we might ask, "What problems does each form of intelligence solve, and why did it evolve or emerge in that particular way?"

This shift mirrors one of biology's greatest lessons, where, nature does not produce a single ideal solution, but instead produces diversity. The eye of an eagle, the sonar of a bat, the camouflage of an octopus, and the communication networks of social insects are not competing attempts to become the same thing. They are different solutions shaped by different histories.

AI has expanded what intelligence means, and perhaps the most important discovery ahead will not be finding out whether machines can become more like us. It will be understanding just how many different ways there are to think, adapt, and solve the challenges of existence.