For most of modern history, humans have been rather certain that intelligence was something they possessed in a special way. Animals could be clever, certainly. They could learn tricks, recognise danger and occasionally surprise us. But language, reasoning and culture belonged on our side of a fairly substantial conceptual wall.
That wall has been getting lower for a long time. Darwin supplied the most important early blow by placing humans firmly inside the evolutionary continuum. During the past half-century, comparative cognition has done much of the rest. Chimpanzees make and use tools. Corvids solve complicated problems, remember past events and manufacture implements. Parrots manipulate concepts and learn socially. Elephants maintain elaborate social relationships. Cetaceans transmit hunting techniques and vocal traditions between generations. Killer whales, in particular, have become difficult to fit comfortably into the old category of intelligent-but-essentially-instinctive animals.
An orca pod is a peculiar thing to watch if one temporarily forgets that its members are whales. A group of long-lived individuals travels through an enormous territory, maintains enduring relationships, raises young, learns local traditions and cooperates to hunt prey that would be formidable even for a large predator. Different populations specialise in radically different forms of hunting. Some attack fish; others seals; still others hunt sharks or whales. Techniques are learned and transmitted socially. The animals communicate acoustically throughout much of their lives, and different populations maintain characteristic vocal repertoires.
If the same behaviour were observed in humans, we would have few difficulties describing what was happening. Imagine six hunters pursuing difficult prey while wearing radios. They alter position, assume different roles, react to one another and repeatedly exchange sounds through their headsets. Nobody would begin from the presumption that the radio traffic consisted merely of emotional noises. We would assume they were coordinating the hunt and then try to discover what they were saying.
With orcas, the burden of proof has traditionally run in the opposite direction. Researchers are comfortable describing calls, vocal repertoires, dialects, social learning and coordinated hunting, yet the word “conversation” remains dangerous. There are good scientific reasons for caution. Coordination can arise from learned routines, observation of movement and relatively simple signals. We should not smuggle human grammar into an animal merely because its behaviour impresses us. But caution can also conceal a prior assumption: human communication is presumed rich until shown otherwise, while animal communication is presumed simple until extraordinary evidence forces an upgrade.
There is a useful way of seeing the problem that has emerged unexpectedly from artificial intelligence. Modern language models operate almost entirely through numbers. Words are converted into numerical representations and repeatedly transformed through enormous neural networks. No particular floating-point number contains the meaning of “king”, “woman” or “Paris”. Yet relationships among large collections of numbers acquire extraordinary structure. Earlier word-embedding systems produced the famous observation that the relationship between representations of “king” and “queen” resembles the relationship between “man” and “woman”. Modern systems are considerably more complicated, but the underlying lesson survives. Meaning-like structure can exist relationally without being explicitly written anywhere.
Multilingual systems make the point even more strikingly. English, French, Japanese and Arabic use completely different surface symbols, yet sufficiently large models develop internal representations in which semantically related expressions across languages can become systematically aligned. The systems are not simply carrying four dictionaries. They are discovering common structure underneath different streams of symbols.
That suggests a different approach to animal communication. Instead of beginning by asking whether a particular orca call means “salmon”, “danger” or “turn left”, one can ask how the entire communication system is organised. Record the vocalisation, the individuals present, their locations, their movements, the prey, the environment, what happened immediately beforehand and what everyone did afterwards. Do this not hundreds but hundreds of thousands of times. Then allow a machine-learning system to discover the structure without first forcing human categories onto it.
The first objective would not be translation. It would be geometry. Which vocalisations cluster together? Which sequences predict particular changes in another animal's behaviour? Do certain transformations in the acoustic signal correspond to predictable transformations in social or physical circumstances? Does including the vocal stream greatly improve a model's ability to predict what another orca will do next? Do particular patterns precede changes in hunting roles before those changes are otherwise observable?
If such patterns exist, it would become possible to reconstruct something resembling a latent map of an orca's communicative world. Only afterwards would one ask how that map relates to ours.
Humans have done a primitive version of this throughout history whenever two peoples without a shared language encountered one another. Someone points at food and says a word. The other person produces another sound. Both experiment. Mistakes occur. Context gradually anchors a handful of correspondences. Once enough anchors exist, translation becomes increasingly relational. Nobody needs a philosophical theory of meaning before the process can begin.
The interesting possibility is that artificial intelligence could perform that process at vastly greater scale. Humans and orcas inhabit different sensory worlds, but not different universes. Both encounter individuals, movement, distance, pursuit, offspring, food, danger, cooperation, competition, absence and expectation. The categories need not be identical. But the underlying causal structure of the world constrains both species.
If two independently evolved nervous systems build useful representations of the same reality, some of their internal relationships may therefore be structurally similar even when their sensory inputs and outward signals are radically different. Translation, in that case, would not depend on finding an orca equivalent of an English noun. It would depend on discovering that two independently constructed representational spaces preserve some of the same relationships.
This is where a philosophical argument more than half a century old becomes unexpectedly relevant. Ludwig Wittgenstein argued that the meaning of a word is not an invisible object carried around inside it. Meaning arises from use: from the role an expression plays within a set of human practices. His famous observation that if a lion could speak we would not understand it was intended to emphasise how deeply language is embedded in a form of life.
Machine learning suggests a curious extension rather than a straightforward rejection of that idea. If meaning really is embedded in patterns of use, then perhaps the correct way to understand the lion is precisely to observe enough of the lion's form of life. A sufficiently rich record of signals, actions, surroundings and responses might allow the structure of that use to be reconstructed computationally. The lion's words would not need to contain intrinsic meanings. Neither do ours.
The implications extend beyond communication. Much of what humans describe as intelligence may itself be less intrinsic to individual brains than we commonly suppose. A modern person appears extraordinarily capable partly because that person is embedded in a civilisation containing writing, mathematics, maps, libraries, scientific instruments, computers, institutions and other people. An individual does not rediscover calculus, metallurgy or constitutional government. The accumulated cognitive work of previous generations is stored externally and delivered to each new generation.
Humans are therefore not merely clever animals. We are clever animals plugged into an immense external memory system.
Other species appear to possess smaller versions of the same phenomenon. An orca calf does not necessarily discover its hunting repertoire independently. It inherits a social world in which older animals already know where prey is found, how it behaves and how particular hunts are performed. Knowledge persists because it resides in the group. Birdsong traditions can outlive individual birds. Chimpanzee groups preserve tool-using practices. Culture, in other words, is not an all-or-nothing human invention.
Once this is recognised, some apparently obvious comparisons become less obvious. Comparing a solitary animal with a modern human implicitly compares one biological organism with another biological organism plus thousands of years of accumulated external cognition. The resulting gulf is real, but its cause may be partly misidentified.
The same argument complicates the way we think about differences among humans. Every human population exhibits substantial variation in cognitive performance. Some people repeatedly extract useful structure from incomplete information, make good predictions, learn quickly and correct mistakes. Others do these things less reliably. Societies give those differences various labels, some flattering and some crude. The underlying phenomenon does not require intelligence to be treated as a mysterious substance. It can be understood simply as variation in information processing.
Culture changes which differences matter. A person in a rainforest community may possess formidable knowledge of terrain, weather, animals and social relationships that is almost invisible to a conventional psychological test. A modern urban resident may perform well with abstract symbols while being helpless without maps, clocks and electronic memory. Neither observation implies that cognitive differences disappear. It means that environments expose different parts of them.
This matters because the study of intelligence has repeatedly become entangled with social and political questions. Some intellectual traditions have exaggerated individual differences and treated measurements as immutable measures of human worth. Others, reacting to that history, have sometimes been reluctant to acknowledge differences that are plainly observable. Neither move is scientifically necessary. People can vary substantially in cognitive performance without that variation constituting a moral hierarchy.
Something similar may have happened in our treatment of other species. The historical temptation was to place humans in one conceptual category and animals in another, then interpret evidence accordingly. A human exchanging unfamiliar sounds was presumed to possess language that we had not yet decoded. An animal exchanging unfamiliar sounds was presumed to possess calls whose limited functions we had not yet identified.
Artificial intelligence has made that distinction harder to sustain intellectually because it has provided a working demonstration of how sophisticated linguistic structure can emerge from systems in which no individual symbol contains its own meaning. It has also supplied the tools required to search for comparable structure elsewhere.
The decisive experiment may therefore be surprisingly mundane. Put enough hydrophones, cameras and sensors around a hunting pod. Record everything. Identify every participant. Reconstruct their movements in three dimensions. Model what each animal can perceive. Feed the signals and behaviour into systems capable of learning representations without imposing human labels. Then test whether the structure discovered in the vocal stream predicts and controls the structure of collective behaviour.
If it does, the question of whether orcas are “really talking” may begin to look oddly old-fashioned. We may instead find ourselves asking how rich their conversations are, how differently their world is organised from ours and how much of the structure of thought is imposed not by species, but by the shared reality in which intelligent systems have to survive.