Alan Turing: The Man Who Invented the Future

Wilmslow, Cheshire. June 8, 1954.

A housekeeper arrives at the home of Dr. Alan Turing, a forty-one-year-old mathematician who works at the University of Manchester. She finds him dead in his bedroom. On the bedside table sits a half-eaten apple. The post-mortem reveals cyanide poisoning.

The coroner records a verdict of suicide. The apple, it is presumed, was laced with cyanide — a detail that some find too neat, too theatrical, too much like the Snow White fairy tale that Turing was known to love. Whether the apple was intentional symbolism or coincidence, nobody will ever know. Whether the death was suicide or accident — Turing had been conducting cyanide experiments in a room adjacent to his bedroom — nobody will ever fully know.

What is known is this: two years earlier, Alan Turing had been convicted of gross indecency for a consensual homosexual relationship, in a Britain where homosexuality was a criminal offence. He had been given a choice between prison and chemical castration — the injection of synthetic estrogen to suppress his libido. He had chosen the chemical treatment. He had endured a year of it.

The man who had arguably done more than anyone alive to save Britain in the Second World War — the man who had broken the Enigma code, shortened the war by an estimated two years, saved an estimated fourteen million lives — was destroyed by the country he had saved.

He was forty-one years old. He had barely started.

A Child Who Did Not Fit

Alan Mathison Turing was born on June 23, 1912, in Paddington, London, the second son of Julius Mathison Turing, a British civil servant in the Indian Civil Service, and Ethel Sara Stoney, the daughter of the chief engineer of the Madras railways.

His childhood was, by the standards of his class and era, unusual in several ways. His parents spent most of their time in India, and Alan and his older brother John were largely raised in England by a succession of family friends and guardians — a pattern that gave the boys stability of a sort but not the warmth and consistent presence of their own parents. Alan was later guarded about his childhood, not bitter exactly, but not warm about it either. The distance between him and his parents was geographical first and emotional second, but it was real.

At school, Turing was the child who did not quite fit. He was clearly intelligent — almost alarmingly so — but his intelligence expressed itself in ways that sat awkwardly with the educational expectations of the English public school system. He was not interested in the classics, in Latin and Greek and the literary humanities that formed the core of the upper-class English education. He was interested in numbers and science, in the patterns underlying things, in problems that had definite answers rather than matters of interpretation and style.

His handwriting was poor. His presentation was chaotic. He had a tendency to work things out from first principles rather than learning and applying established methods — a habit that frustrated teachers who expected students to demonstrate knowledge of the curriculum rather than independent reasoning. He was, in the language of a later era, a difficult student: brilliant, unconventional, and constitutionally incapable of pretending to be interested in things that did not interest him.

What saved him was mathematics. And what saved mathematics for him was a teacher named D.O. Eperson, who recognized in the young Turing something unusual and nurtured it carefully.

But before Eperson, there was Christopher Morcom.

Christopher Morcom was Turing’s closest friend at Sherborne School, and almost certainly the first person Turing loved. Morcom was brilliant, gentle, interested in astronomy and science, and he and Turing found in each other the intellectual companionship that neither had found anywhere else. They worked on mathematical problems together, discussed science, dreamed about the future.

In February 1930, when Turing was seventeen and Morcom was eighteen, Christopher Morcom died of bovine tuberculosis — a complication of an earlier infection contracted from contaminated milk in childhood. He had been ill before, but the final decline was rapid. Turing was devastated.

The loss of Morcom shaped Turing in ways that are hard to fully trace but impossible to ignore. He threw himself into mathematics with a new intensity — as if by working hard enough, understanding deeply enough, he could somehow honor what Morcom had been and could have become. He began to think, with a seriousness he had not had before, about the nature of mind — about what it meant to think, to be conscious, to have a self. He wrote to Morcom’s mother that he hoped to show that the mind could be immortal even if the body was not — that the patterns of thought that made a person who they were might persist in some form beyond death.

This was the grief of a young man reaching for meaning. But it was also the beginning of a lifelong philosophical inquiry into the nature of mind that would eventually produce the most important paper in the history of artificial intelligence.

Cambridge and the Birth of a Mathematician

Turing went up to King’s College, Cambridge in 1931, and Cambridge gave him what school had not: a world in which his kind of mind was valued, a community of mathematicians who took seriously the problems he found compelling, and the freedom to follow his own intellectual instincts.

He flourished. He took long runs along the river — he was a serious runner, and would remain so throughout his life, eventually reaching near-Olympic standard as a marathon runner. He thought, deeply and continuously, about mathematics. He made friends, cautiously and selectively, among the small number of people who could meet him on his own intellectual ground.

And he read a paper that changed the direction of his life.

In 1935, Turing attended a course of lectures by the mathematician Max Newman on the foundations of mathematics — specifically on the recent work of David Hilbert and the problems he had posed about the nature of mathematical truth. One of Hilbert’s problems was the Entscheidungsproblem — the “decision problem.” Could there be, Hilbert asked, a definite mechanical procedure — an algorithm — that could determine, for any mathematical statement, whether it was provable or not?

This was a fundamental question about the nature of mathematics. If the answer was yes, it would mean that mathematics was, in principle, completely mechanizable — that a sufficiently powerful calculating machine could, in principle, answer any mathematical question. If the answer was no, it would mean that mathematics was fundamentally open — that there were questions it could ask but not answer.

The question captivated Turing. And the way he approached it was characteristic: rather than working with the abstract logical systems that other mathematicians used to think about computation, he invented a concrete model. He imagined a machine — simple, abstract, but precisely defined — that could read symbols from a tape, write symbols, and move the tape back and forth according to a finite set of rules. This machine — now known as the Turing Machine — could, he showed, simulate any mechanical procedure that could be precisely defined.

The Turing Machine was not a practical device. It was a mathematical abstraction — a thought experiment made rigorous. But it was a thought experiment of extraordinary power. By precisely defining what a “mechanical procedure” was — what it meant to follow rules without judgment or insight, purely mechanically — Turing gave himself a tool for thinking about what could and could not be computed.

Using this tool, he proved in 1936 that the Entscheidungsproblem had no solution. There was no algorithm that could determine, for any mathematical statement, whether it was provable. Some problems were undecidable. Mathematics was irreducibly open.

This result — published in a paper called “On Computable Numbers, with an Application to the Entscheidungsproblem” in 1936, when Turing was twenty-three years old — is one of the most important papers in the history of mathematics and computer science. It did not just answer Hilbert’s question. It created the theoretical foundation for computing.

The Turing Machine became the standard model of computation — the abstract device against which the power and limits of computing could be measured. A problem was computable if and only if a Turing Machine could solve it. The Church-Turing thesis — named for Turing and the American logician Alonzo Church, who reached similar conclusions through different methods at roughly the same time — proposed that this captured the full scope of what any computing device could do. Any computation that could be done at all could be done by a Turing Machine.

Every computer ever built, from the earliest room-filling mainframes to the most powerful modern supercomputers to the phone in your pocket, is in a formal mathematical sense a Turing Machine. They are all doing the same thing Turing described in that 1936 paper, just much faster and with much more memory. The architecture of modern computing begins here.

Princeton and the First Machines

After Cambridge, Turing went to Princeton for his PhD, working under the logician Alonzo Church. Princeton was where the intellectual worlds of pure mathematics and actual computing first seriously intersected — John von Neumann was there, and would later design the architecture of the first stored-program computers drawing on ideas that Turing and his contemporaries were developing.

Turing’s PhD work extended his theoretical results from the 1936 paper. But what is most striking about his time at Princeton is what he was already thinking about beyond the mathematics: actually building machines. He was not content with the theoretical Turing Machine. He wanted to build real computing devices.

He returned to England in 1938, turning down an offer from von Neumann to stay at Princeton as his assistant. He had become aware, as had most thoughtful people in Europe by 1938, that war was coming. And he had been approached, before his departure from England, by the Government Code and Cypher School — Britain’s signals intelligence organization, based at a Victorian country house in Buckinghamshire called Bletchley Park.

What happened next would win the war. And it would happen in secret, unknown to the world for the next thirty years.

Bletchley Park: The Work That Won the War

The German military used a cipher machine called Enigma to encrypt their communications. Enigma was, by the standards of its time, an extraordinarily secure encryption system. It used a series of rotating wheels — rotors — and an electrical plugboard to scramble letters according to a key that changed every day. The number of possible configurations was astronomical — often quoted as approximately 159 quintillion — making brute-force decryption essentially impossible with manual methods.

The Poles had made significant progress breaking early versions of Enigma before the war, and their work — shared with Britain and France in 1939 — gave Bletchley Park a crucial head start. But as the war began and the Germans updated and complicated their Enigma systems, the challenge intensified. Naval Enigma in particular — used to direct the U-boat campaign in the Atlantic that was slowly starving Britain of supplies — was resistant to the methods that had worked on earlier versions.

Turing became the central figure in breaking Naval Enigma. He built on the Polish work to develop a codebreaking machine called the Bombe — an electromechanical device that could test thousands of possible Enigma configurations rapidly, eliminating impossible settings and homing in on the correct key. The Bombe did not directly decode messages. It reduced the search space to manageable proportions, allowing human analysts to complete the decryption.

The work was collaborative — Turing was one of several brilliant mathematicians at Bletchley, including Gordon Welchman, Dilly Knox, and others. But his contribution was uniquely important. His 1940 paper on the use of cribs — known plaintext fragments that could help constrain the search for the correct Enigma setting — was foundational. His work on the statistical analysis of Enigma messages developed the mathematical techniques that made the Bombe effective.

Eventually, the Bletchley operation was reading tens of thousands of German messages a day. Naval Enigma was broken. The U-boat threat was contained. The intelligence gathered at Bletchley — known as Ultra — contributed to every major Allied campaign of the war, from North Africa to D-Day. It was, arguably, the single most important intelligence operation in the history of warfare.

Turing himself was recognized within Bletchley as exceptional — as the person who had done more than anyone else to make the cryptanalytic operation work. His colleagues respected him enormously, if they also found him peculiar. He kept his tea mug padlocked to the radiator to prevent it from being borrowed. He cycled to work wearing a gas mask, not as a precaution against gas attacks but because he was allergic to pollen and the mask filtered it out. He buried silver ingots in the woods around Bletchley to protect his savings from the expected German invasion — and then forgot where he had buried them.

He was, by all accounts, a person of remarkable warmth and humor alongside the eccentricities. His colleagues at Bletchley were fond of him. He could be absent-minded and difficult in some ways, but he was never cruel or dismissive. He took the work seriously. He understood what was at stake.

The war ended. Bletchley Park was shut down. The work was classified. And for the next thirty years, Alan Turing could not tell anyone what he had done.

The Automatic Computing Engine: Building the Machine

After the war, Turing turned his full attention to the project he had been thinking about since Cambridge: building an actual computer.

In 1945 he joined the National Physical Laboratory in London and produced a detailed design for a machine he called the Automatic Computing Engine — the ACE. The design was, for its time, extraordinarily sophisticated. Turing had spent the war thinking about machines, about information processing, about the relationship between hardware and the instructions that directed it. His design for the ACE was not just a calculator. It was a stored-program computer — a machine in which the program, the instructions telling the machine what to do, was stored in the machine’s memory alongside the data it was operating on.

This is the architecture of every modern computer. The stored-program concept — the idea that instructions and data should be treated the same way, stored in the same memory, so that a program can manipulate its own instructions — was developed simultaneously by Turing and by the team around John von Neumann at Princeton. The debate over priority has generated considerable heat among historians, but the important point is that both groups arrived at the same fundamental insight independently, and that insight is the foundation of all modern computing.

Turing’s ACE design was not built as he intended — bureaucratic delays and organizational difficulties at the NPL frustrated him enormously, and a simpler pilot version was eventually constructed after he had left. He moved to Manchester in 1948, where Freddie Williams and Tom Kilburn had built one of the first working stored-program computers in the world. At Manchester, Turing could finally do what he had been trying to do for years: actually program an actual machine and see what it could do.

What he found was that the gap between what a machine could theoretically do and what it could practically do in 1948 was vast. The Manchester machine was real, working, and remarkable — but also slow, unreliable, and severely limited in memory. The dream of machine intelligence, which Turing was already thinking about seriously, was clearly not going to be achieved on machines like this. Something much more powerful would be needed.

But the theoretical question — whether machine intelligence was possible in principle, what it would look like, how you would know if you had achieved it — that was a question Turing could address right now, on paper, with the same kind of rigorous thinking he had brought to the foundations of computing in 1936.

”Computing Machinery and Intelligence”: The Paper That Asked Everything

In 1950, Turing published a paper in the journal Mind — a philosophy journal, notably, not a mathematics or engineering journal — that has become one of the most famous and most debated papers in the history of science.

Its title was “Computing Machinery and Intelligence.” Its opening sentence was: “I propose to consider the question, ‘Can machines think?’”

What followed was thirty pages of argument, analogy, thought experiment, and provocation that set the agenda for the field of artificial intelligence before the field had even been named. McCarthy would give AI its name six years later at Dartmouth. Turing gave it its central question.

The paper opened with a problem. The question “Can machines think?” seemed straightforward, but it immediately became entangled in definitional difficulties. What did “machine” mean? What did “think” mean? Turing was impatient with this kind of verbal dispute — he felt it generated more heat than light. So he did something characteristically clever: he proposed to replace the vague question with a precise one that he called the Imitation Game.

The Imitation Game, as originally described, involved three participants: a man, a woman, and an interrogator. The interrogator — in a separate room, communicating only through written messages — had to determine which of the other two was the man and which was the woman. The man was trying to fool the interrogator. The woman was trying to help.

Turing then asked: what would happen if we replaced the man in this game with a machine? The new question — can a machine play the role of the man successfully? — was, Turing proposed, a more precise version of the question “Can machines think?” that could be investigated objectively.

This thought experiment has been known ever since as the Turing Test. The version that entered popular consciousness is simpler than Turing’s original formulation: a machine passes the Turing Test if a human interrogator, communicating through text, cannot reliably distinguish the machine’s responses from a human’s.

The Turing Test was immediately controversial and has remained so for seventy years. Critics pointed out that it tested the ability to imitate human behavior, not the presence of genuine intelligence or consciousness. A machine might pass the test by being a very good imitator without understanding anything — what the philosopher John Searle would later argue with his Chinese Room thought experiment. Others pointed out that the test was culturally specific — passing it would require human-like knowledge and conversational style, and a machine from a different cultural background might fail for reasons unrelated to intelligence.

Turing addressed many of these objections in the paper itself. He anticipated the arguments against machine thinking with striking thoroughness — listing nine objections and responding to each in turn. The objections included theological objections (only God can give souls to beings), mathematical objections (Gödel’s incompleteness theorems show that machines have limitations that minds do not), and what he called Lady Lovelace’s Objection — the argument that machines can only do what they are programmed to do and therefore cannot genuinely originate anything.

His response to Lady Lovelace’s Objection was subtle and important. He agreed that a machine, at any given moment, only does what it has been instructed to do. But he questioned whether this was really different from the situation with humans. Human behavior, he suggested, was also determined by initial conditions — by genetics, education, experience. The fact that the determinants of human behavior were biological and experiential rather than mechanical did not obviously make human output more “original” in any philosophically significant sense.

He also made a prediction. He estimated that by the year 2000, a machine would be able to play the Imitation Game well enough to fool an average human interrogator at least 30% of the time over a five-minute conversation. This was a specific, falsifiable prediction — unusual for a philosophical paper — and it reflected Turing’s confidence that machine intelligence was not just theoretically possible but practically achievable within decades.

He was roughly right, though the timeline was off. By 2000, chatbots could fool some people some of the time. By 2014, a program called Eugene Goostman claimed to have passed the Turing Test by convincing 33% of judges it was human in a particular competition — though the claim was disputed. By 2023, large language models were producing conversations that many people found indistinguishable from human.

The 1950 paper ended with a passage that has been quoted thousands of times and that remains as striking today as when it was written. Turing wrote that he believed the question of whether machines could think was too meaningless to deserve discussion. What he was interested in was the actual question: could machines do what thinking beings do? And he believed that this question had an answer, and that the answer was yes.

He wrote: “We can only see a short distance ahead, but we can see plenty there that needs to be done.”

He was forty years from his death. There was plenty ahead. He was right about that.

The Morphogenesis Paper: The Other Turing

There is a version of Alan Turing’s story that is told primarily through his contributions to computing and AI. This version is true, but it is incomplete. Because Turing’s intellectual interests were far broader than computing, and in his last years he was pursuing a question that had fascinated him since his contemplation of Christopher Morcom’s death: what was the relationship between physical processes and living forms?

In 1952, Turing published a paper on morphogenesis — the biological process by which an organism develops its shape. The paper, titled “The Chemical Basis of Morphogenesis,” proposed a mathematical model for how complex patterns could emerge from simple chemical interactions.

Turing showed that if two chemicals — he called them morphogens — diffused through a tissue at different rates and reacted with each other in the right ways, they could spontaneously produce stable spatial patterns. These patterns — stripes, spots, spirals — corresponded with striking accuracy to the patterns observed in animal coats, fish markings, and plant structures.

The paper was revolutionary. It was the founding document of what is now called mathematical biology — the use of mathematical models to understand biological processes. The specific mechanism Turing identified, now called reaction-diffusion systems, has been confirmed experimentally in numerous biological systems. The patterns on a leopard’s coat, the stripes on a zebra, the arrangement of fingers on a developing hand — all of these show signatures consistent with the Turing mechanism.

This paper revealed a Turing that the AI history books often neglect: a scientist of extraordinary breadth, interested not just in the abstract question of what machines could do but in the deep question of how order and complexity emerged from simple rules. This interest connected his mathematical work on computation, his philosophical work on mind, and his scientific work on biology into a single unified inquiry: how do complex, purposive behaviors emerge from simple underlying processes?

This is, at its deepest level, the question of AI. How does intelligence — with all its complexity, flexibility, and apparent purposiveness — emerge from the relatively simple operations of neurons firing and synapses strengthening? How might it emerge from the matrix multiplications and attention mechanisms of a neural network?

Turing did not live to pursue these questions further. He was prosecuted and destroyed before he could.

The Prosecution: What Britain Did to Its Hero

In January 1952, Turing reported a burglary at his home in Wilmslow to the police. In the course of the investigation, he mentioned — with a candor that in retrospect seems almost impossibly naive — that he had been in a relationship with a young man named Arnold Murray, who he believed had been involved in the burglary.

Homosexual acts between men were illegal in Britain under the Criminal Law Amendment Act of 1885, the same law that had been used to prosecute Oscar Wilde in 1895. Turing was arrested, charged with gross indecency, and tried.

He did not deny the relationship. He appeared to believe, or at least to hope, that the climate had changed enough since 1885 that consensual relationships between adult men might be treated differently. He was wrong.

He was convicted. The court offered him a choice: prison or probation with a requirement to undergo hormonal treatment — the injection of synthetic estrogen, intended to chemically suppress sexual desire. Turing chose the treatment. He underwent it for a year.

The estrogen injections caused him to develop gynaecomastia — breast tissue. They affected his physique, his mood, and his ability to work. His security clearance was revoked — the very clearance that had allowed him to do the work at Bletchley Park that had helped win the war — because homosexuals were considered a security risk, vulnerable to blackmail. He was no longer permitted to consult for GCHQ, the successor organization to Bletchley.

The cruelty of this is almost beyond comprehension. The man who had perhaps contributed more to the Allied victory in the Second World War than any other individual — the man whose work at Bletchley had been kept secret precisely to protect national security, and who had kept that secret faithfully for over a decade — was now deemed a security risk because of whom he loved. The country he had saved destroyed him.

What Turing felt during this period is not fully knowable. He was not a man who wrote extensively about his inner emotional life. His letters from this period are businesslike, mathematical, sometimes darkly humorous. He continued to work — the morphogenesis paper was published during this period. He gave no obvious outward signs of the degree to which the treatment and its consequences were affecting him.

His friend and colleague Robin Gandy, one of the few people close enough to him to know something of what he was going through, said later that Turing had seemed, in some ways, to be coping. But there was a quality to his coping that was itself disturbing — a sense of distance, of detachment, that was new.

In June 1954, Alan Turing died. He was forty-one.

The Legacy Denied and Then Restored

For decades after his death, Alan Turing’s contributions were not publicly known. The work at Bletchley Park remained classified until the 1970s. The general public had no idea that the man who had written about machine intelligence and designed the theoretical foundation of computing had also helped win the Second World War. When the Bletchley secret was finally revealed, it had to be reconstructed partly from memory, partly from classified documents, partly from the recollections of people who had been bound to silence for thirty years.

Even after Bletchley became known, Turing’s reputation took time to build to the stature it deserved. He was a mathematician and computer scientist who had died young and whose work was highly technical. He had no popular books, no famous public disputes, no Nobel Prize — he died before the Nobel Prize for Economics was established, the category in which his work might have been recognized. He was a figure known to specialists long before he became known to the general public.

The rehabilitation was gradual. His theoretical contributions to computing — the Turing Machine, the stored-program concept, the Turing Test — were recognized within computer science as foundational. The Association for Computing Machinery established the Turing Award in 1966, the highest honor in computer science, named for him. Slowly, his contributions to the war effort became more widely known as the Bletchley material was declassified.

In 2009, the British Prime Minister Gordon Brown issued a formal public apology for the treatment Turing had received. The apology was welcomed but felt, to many, insufficient. What had been done to Turing was not just a personal injustice. It was a crime against humanity, in the plainest sense: a state using its power to destroy one of its most extraordinary citizens for no reason other than who he was.

In 2013, Queen Elizabeth II granted Turing a posthumous royal pardon under the Royal Prerogative of Mercy. This too was welcomed and felt insufficient. A pardon implies that the offense existed and is being forgiven. What many felt was appropriate was not a pardon but an acknowledgment that there had been no offense — that Turing had done nothing wrong and that the law under which he was prosecuted was itself a crime.

In 2021, Turing’s image was placed on the British fifty-pound note — the highest denomination of British currency. It was a gesture of enormous symbolic significance. The man who had been destroyed by the British state was now on its money.

Whether any of this constitutes adequate recognition is a question each person must answer for themselves. What cannot be disputed is that Turing’s life and death constitute one of the most profound moral failures in the history of modern states — and one of the most significant intellectual legacies of the twentieth century.

What Turing Means for AI

Alan Turing’s contributions to AI are so fundamental that it is almost impossible to describe the field without them. The theoretical foundation — the Turing Machine, the concept of computable functions, the Church-Turing thesis — is the bedrock on which all of computing and all of AI is built. The Turing Test — whatever its limitations as a practical test, and they are real — gave AI its central question and its central aspiration. The 1950 paper anticipated the major philosophical objections to machine intelligence and provided responses that are still debated and refined today.

But Turing’s meaning for AI goes beyond specific contributions. He established the intellectual character of the field — rigorous, precise, willing to engage seriously with philosophical questions that other disciplines might dismiss as too vague, committed to the idea that intelligence was a natural phenomenon that could in principle be understood and replicated.

He also established the field’s central ethical stakes. The question of what it would mean for a machine to think is not just a technical question. It is a question about the nature of mind, the nature of consciousness, and ultimately about what it means to be human. Turing understood this. He took the philosophical dimensions of his work seriously. He did not treat AI as merely an engineering challenge — he treated it as a question that went to the heart of what human beings were and what they could create.

His own life gave this philosophical inquiry a personal dimension. Turing was a person who was told, in effect, that the way he was made was wrong — that his nature was deviant and criminal. He was prosecuted for being himself. And he had spent his professional life arguing, with great precision and force, that what a being was — what it thought, what it felt, what it could do — was not determined by its outward form or the conventional categories others used to describe it, but by its actual capacities and actual inner life.

The question “Can machines think?” was, for Turing, not entirely abstract. He knew what it felt like to have your inner life judged against a standard you had not set and could not meet. He knew what it felt like to be evaluated not on what you were but on what category people had put you in. His argument for taking machine intelligence seriously — his insistence that the question should be decided by evidence about what machines could actually do, not by prior assumptions about what machines were — has a personal resonance that his biography makes impossible to ignore.

He was, in the end, arguing for the same thing for machines that he deserved himself: to be judged on what you actually are, not on what people expect you to be.

The Question He Left Open

Turing died in 1954. He did not live to see the founding of AI as a formal field at Dartmouth in 1956. He did not live to see the first AI programs, the first AI winters, the first neural network breakthroughs, the deep learning revolution, ChatGPT. He saw none of it.

But the question he asked in 1950 is the question that all of it is attempting to answer. Can machines think? Can they be made to do what thinking beings do? And if they can — if a machine can converse, reason, create, and adapt in ways indistinguishable from a human — what does that tell us about the nature of thinking itself?

These questions have not been answered. Not in seventy years of AI research, not by the most powerful language models in existence, not by the most sophisticated neural networks. The Turing Test has been approached and arguably approached closely by modern AI systems. But the deeper question — whether any of these systems genuinely thinks, genuinely understands, genuinely experiences anything — remains open.

Some researchers believe the question will be answered when machines become sophisticated enough. Others believe the question is malformed — that there is no fact of the matter about whether a machine “really” thinks, just as there is no fact of the matter about whether a thermostat “wants” to maintain temperature. Still others believe the question points to something genuine and important about the nature of consciousness that we do not yet understand well enough to resolve.

Turing was honest about the limits of his own understanding. He wrote, in that famous 1950 paper, that he did not know whether machines could think. He proposed a test that would give us practical grounds for treating them as if they could. He believed this was enough to get on with.

It was enough to get on with. The field he gave its question and its theoretical foundation has grown into one of the most important human endeavors in history. The machines are getting better, faster than almost anyone predicted. The question he asked is more urgent than it has ever been.

He should be here to see it. He was taken too soon, by a cruelty so mundane and so comprehensive that it stands as a permanent reminder of what societies do when they confuse their own conventions with the nature of things.

We are still answering his question. The least we can do is remember who asked it.