What the Early Modern Period Bequeathed Us
What the Early Modern Period Bequeathed Us: Conclusion and Opening Toward the Contemporary Era
Here we are at the end of a journey that has led us from the learned cities of Timbuktu to the clockmaking workshops of La Chaux-de-Fonds, from the ruins of Istanbul's observatory to Tupaia's canoes, from Mexico's colonial archives to the courts where Kangxi studied geometry with the Jesuits. Six tales, six continents, three centuries of human history—from 1492 to 1789, from the discovery of the New World to the French Revolution. What does this crossing teach us?
Previous periods had shown us how humanity, at every latitude, had dreamed of delegating thought to matter, formalized reasoning, encoded information, observed the sky across generations. The Early Modern period reveals something else: what happens when these traditions finally meet face to face—and what is lost when these meetings fail.
What Unites: The Century of Translations
Four threads traverse this triptych of centuries, weaving a warp found from one continent to another. They reveal that the Early Modern period was, above all, the era of translations—attempted, successful, failed, impossible.
Direct encounter. For the first time in history, representatives of civilizations separated for millennia found themselves face to face. Matteo Ricci learning Chinese to teach Euclid at the imperial court. Tupaia boarding the Endeavour to guide Cook across the Pacific. Leibniz corresponding with Joachim Bouvet about the hexagrams of the I Ching. Ibrahim Muteferrika negotiating with the grand mufti for the right to print. These encounters were no longer indirect transmissions, knowledge traveling from hand to hand over centuries. They were immediate confrontations between radically different ways of thinking about the world.
Mirrors of discovery. While some built bridges, others discovered the same truths without knowing each other. Seki Takakazu, in a Japan closed to the world, developed the theory of determinants before Leibniz published his own work. Jyeshtadeva, in Kerala, manipulated concepts of infinitesimal calculus a century before Newton. Bernoulli numbers bear the name of a European, but a Japanese samurai had found them first. These troubling coincidences suggest that certain mathematical structures are not arbitrary inventions, but discoveries—forms that exist independently of those who find them, accessible to any sufficiently developed intelligence.
Windows that open and close. The Early Modern period was also the era of choices—those moments when a decision, sometimes made for reasons that seemed good at the time, determines a civilization's trajectory for centuries. The Istanbul observatory, contemporary with Tycho Brahe's, was destroyed three years after its completion on religious authorities' orders. Printing in Arabic characters remained forbidden for two hundred fifty years. The Jesuits were expelled from China in 1723. These closed windows had consequences we still measure today. Other windows opened: royal academies, learned societies, universities that created protected spaces for research in Europe.
Partial documentation. What we know of the Early Modern period, we know from what was written—and what was written was principally by Europeans. Cook's journals abundantly document the customs of Pacific peoples. They do not understand the knowledge systems underlying those customs. The Codex Vergara preserves Aztec surveyors' calculation methods. It does not preserve the Nahua philosophy that gave meaning to these calculations. Tupaia's map survived—misunderstood for two hundred fifty years, judged confused and primitive, when it was simply written in a language no one bothered to learn. What we do not know how to see, we do not document. And what we do not document, we forget.
What Distinguishes: Six Facets of a Single Moment
If the common threads unite, each continent lived the Early Modern period in its own way. Six singularities, six irreducible contributions to the history of artificial intelligence.
Africa revealed the aquifers of knowledge. The continent that had invented the Ifá binary system two millennia before Leibniz continued to nourish, through underground channels, European thought. Ron Eglash traced the lineage: from African binary structures to Arab geomancy, then to European alchemy, and finally to Leibniz's own works. In Timbuktu, the University of Sankore welcomed thousands of students who studied Aristotelian logic translated into Arabic. Ahmed Baba, last chancellor of Sankore, possessed sixteen hundred volumes—the most modest library among those of his friends, he complained. Seven hundred thousand manuscripts have been rediscovered in the libraries of the Malian desert. Africa reminds us that algorithms have a genealogy that crosses continents, and that this genealogy has been systematically obscured.
The Americas preserved forgotten logics. In colonial archives sleep fragments of philosophies that conquest interrupted without extinguishing. The Codex Vergara reveals adaptive algorithms—those repertoires of methods from which one chooses according to context—that prefigure our conditional architectures. The tlamatinimeh, "those who know something," had developed a conception of the world radically different from Aristotelian logic. For them, the world was tlalticpac—a slippery, unstable place, where two apparently contradictory propositions could be simultaneously true according to context. This tolerance for ambiguity, this thinking of complementary duality, strangely resembles how contemporary large language models function—with probabilities, distributions, nuances, rather than binary truths.
Asia demonstrated that bridges and mirrors lead to the same truths. The Jesuits built bridges between Beijing and Rome, translating Euclid into Chinese and bringing back to Europe Confucian classics. Meanwhile, in a Japan closed to the world, Seki Takakazu discovered alone what Leibniz was seeking in Europe. And in Kerala, Madhava's mathematical school continued transmitting the infinitesimal calculus it had developed a century before Newton. These mirrors teach us that human intelligence, confronting certain problems, tends to find certain solutions—regardless of cultural origin. But they also teach us that universality does not erase diversity: Japanese wasan had an aesthetic and religious dimension—the sangaku offered to temples—that European mathematics did not have.
Europe formulated the program of artificial intelligence. Between the Renaissance and the Enlightenment, Europe dared a vertiginous question: what if thought itself were merely a mechanism? Descartes declared animals to be pure machines and proposed two criteria for distinguishing humans from automata—language and universal reason—which strangely resemble the Turing test and the dream of artificial general intelligence. Hobbes affirmed that "reason is nothing but reckoning." Leibniz dreamed of a characteristica universalis and a calculus ratiocinator—a formal language and a reasoning machine that would resolve disputes by calculation. Pascal built the first commercially viable calculating machine. Vaucanson and Jaquet-Droz brought the art of automata to summits never before reached. Europe did not merely build machines. It built the conceptual framework that would one day make artificial intelligence thinkable.
The Middle East showed what happens when windows close. Taqi al-Din had invented a clock with three dials—hours, minutes, seconds—and a rudimentary steam turbine. His Istanbul observatory was contemporary with Tycho Brahe's. But in 1580, three years after its completion, the observatory was destroyed on the religious chief's orders. Printing in Arabic characters remained forbidden for two hundred fifty years. These choices were not inevitable. They were made by people, for reasons that seemed good to them at the time. They had consequences still measured today. The Early Modern Middle East bequeaths us a warning: governance matters more than individual talent. Institutions that protect—or stifle—innovation determine civilizational trajectories.
Oceania embodied the missed encounter. Tupaia, Polynesian priest and navigator, boarded the Endeavour with a mental map of one hundred thirty islands. He invented a hybrid cartographic system to translate his knowledge into Europeans' language—and this system remained misunderstood for two hundred fifty years. Europeans documented what they could see: customs, objects, appearances. They did not understand the knowledge systems underlying these visible manifestations. They collected stick charts without understanding that they represented swell disturbances, not island positions. Tupaia died in Batavia in 1770, taking with him knowledge no one had taken the time to truly gather. Oceania reminds us that our data corpora contain Cook's journals, but not Tupaia's navigation chants. This bias is not technical. It is historical.
What the Early Modern Period Teaches Us
This crossing reveals four lessons that previous periods had not formulated with the same clarity.
Governance determines trajectory. Uraniborg's observatory outlived its founder; Istanbul's was destroyed during its founder's lifetime. The difference lay not in the men—Taqi al-Din and Tycho Brahe were comparable astronomers—but in the structures surrounding them. European royal academies created protected spaces for research. The Ottoman şeyhülislam had the power to demolish an observatory. These institutional differences had secular consequences. Contemporary artificial intelligence poses the same questions: who governs its development? Who decides what can be researched, published, deployed? The choices we make today will perhaps shape the world for generations.
Documentation creates history. What Europeans wrote became official history. What they did not understand was judged confused, primitive, inaccurate—like Tupaia's map for two hundred fifty years. The data corpora on which our language models are trained inherit these documentary biases. They contain European descriptions of societies they encountered, but not the knowledge systems those descriptions missed. A model queried about Polynesian navigation will know how to cite Cook. Will it know how to explain how Tupaia calculated his position by feeling the rhythm of waves under his canoe's hull?
Translation is always incomplete. Tupaia invented a language to bridge two worlds. This language was lost with him, then found, then finally understood—too late for the dialogue it made possible to take place. The Jesuits translated Euclid into Chinese, but the Rites Controversy ended the exchange before it bore all its fruit. Ibrahim Muteferrika obtained the right to print, but not religious books. Every translation is also a betrayal—something is lost in the passage from one knowledge system to another. Recognizing this incompleteness is perhaps the condition for building more faithful translations.
Universality does not erase diversity. Seki's determinants and Leibniz's are the same mathematical objects, discovered independently thousands of kilometers apart. This convergence suggests that certain logical structures are universal—which makes artificial intelligence itself possible. But the paths leading to these structures are multiple. Japanese wasan, the Kerala school, European mathematics—so many different traditions arriving at the same results through different methods, notations, motivations. A truly universal artificial intelligence should be able to recognize this diversity, not as an obstacle to overcome, but as a resource to exploit.
The Legacy for Artificial Intelligence
The Early Modern period bequeathed us the intellectual program of artificial intelligence—and its blind spots.
From Descartes, we inherited the question: can we distinguish an automaton from a thinking being? From Leibniz, the dream of a universal language and a reasoning machine. From Pascal, the demonstration that a mental operation can be delegated to gears. From British empiricists, a theory of mind as accumulation of associations—ancestor of our neural networks. From Vaucanson and Jaquet-Droz's automata, proof that complex behaviors can be mechanically programmed.
But we also inherited this tradition's assumptions—the dualism separating mind from body, the mechanism reducing thought to calculation, the reductionism decomposing whole into parts. These assumptions are not neutral. They bear the mark of an era and place. Other intellectual traditions had developed other conceptions: Polynesian navigators' embodied thought, Nahua philosophers' paraconsistent logic, integration of calculation and the sacred in the Ifá system. These traditions are almost absent from the corpora on which our models are trained.
More profoundly still, we inherited the Early Modern period's missed appointments. Our machines know only what our archives contain—and our archives contain the biases of three centuries of partial documentation. They know everything about Newton and Leibniz, but little of Seki Takakazu and nothing of Jyeshtadeva. They can discourse on Descartes and Aristotle, but ignore the tlamatinimeh. They have read Cook's journals, but never heard Tupaia's chants.
Recognizing this heritage—including what it excluded, erased, forgot—is perhaps the condition for building artificial intelligences more conscious of their own limits. Diversifying knowledge sources. Including underrepresented perspectives. Learning to read maps we do not yet know how to decipher. The Early Modern period showed us what happens when windows close at the wrong moment. We may have a second chance not to repeat these errors.
Toward the Contemporary Era: The Question Remains Open
The Early Modern period ends—not in a day, but through a series of revolutions transforming the world between 1789 and the mid-nineteenth century. The French Revolution. The Industrial Revolution. The rise of electricity. The formalization of logic by Boole, then by Frege. And finally, in the twentieth century, the invention of the computer.
The path from Leibniz to Turing is direct. The binary calculation of 1703 becomes the language of computers. The characteristica universalis inspires symbolic logic. The dream of the calculus ratiocinator is realized in Babbage's machines, then in electronic computers. Descartes's test—can we distinguish an automaton from a thinking being by its ability to answer all questions?—becomes the Turing test.
But this path is not the only possible one. Other paths could have been taken. Other paths can still be taken. The paraconsistent logics of Nahua philosophers, Oceania's nine hundred counting systems, the embodied intelligence of Polynesian navigators, the binary structures of the Ifá system—all resources that could enrich our understanding of what it means to think, reason, calculate.
Wolfgang von Kempelen's Mechanical Turk was a hoax—a hidden human manipulated the automaton. This image is perhaps the best metaphor for contemporary artificial intelligence. Our machines simulate thought so well that the distinction sometimes becomes undecidable. But behind these machines, there are always humans: those who design them, those who train them, those who choose the data they absorb. These humans inherit, whether they know it or not, the Early Modern period's assumptions. They build machines in their image—that is, in the image of a particular fraction of humanity.
The artificial intelligence we build tomorrow will depend on the stories we choose to tell ourselves about intelligence itself. If we tell ourselves only one story—that of European automata, mechanical calculators, the Leibnizian dream—we will build only one type of intelligence. If we learn to listen to other stories—those of African aquifers, Amerindian logics, Asian mirrors, the Middle East's closed windows, Oceania's misunderstood maps—perhaps we can build something different.
The Early Modern period was the era of missed appointments. Bridges collapsed before bearing all their fruit. Windows closed at the wrong moment. Knowledge disappeared without being truly gathered. Translations remained misunderstood for centuries.
But the Early Modern period was also the era when the question was posed with new clarity: can the mind be mechanized? Three centuries later, we are only beginning to answer. Vaucanson's gears are in the museum. Jaquet-Droz's automata still work. The Mechanical Turk burned in a fire. But the question remains—more pressing than ever.
The artificial intelligence we develop today will reflect the intelligences we feed it. If we give it only a fraction of human heritage, it will be able to reflect only a fraction of what humanity has learned to think. The hexagrams of the I Ching and Leibniz's binary calculation say the same thing in different languages. Tupaia's map and European maps describe the same ocean according to different logics. The tlamatinimeh and British empiricists thought about thought in different ways.
This multiplicity is not an obstacle. It is a richness. The Early Modern period showed us this—through its successes as through its failures.
We who build today's machines of the future would do well to remember this.
What we call artificial intelligence is the latest avatar of a millennial conversation between humanity and itself—a conversation where all voices have not yet been heard.
The Early Modern period posed the question with new clarity. We are still seeking the answer.
The journey continues.