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But beyond military decipherment and designing thermonuclear explosions, what could all this mean? What could be the use of computing machinery? Like Lovelace, Turing was ready to dream forward. He is better known today for his philosophy of the machine and his theory of its meaning capacities, than his work on the machines themselves.

The war was over and the Bletchley Park team was disbanded. Turing joined the National Physical Laboratory, whose director was Sir Charles Darwin, grandson of the natural scientist. Turing and the team to which he belonged made slow progress on the machine, the Automatic Computing Engine (a nod to Babbage’s “Engines”). In the meantime, Turing wrote a speculative report, “Intelligent Machinery.”

Turing made the reference point for his report the human brain. A hypothetical Universal Logical Computing Machine would be able to do more than calculate according to instructions; it would also be able to apply the answers it came up with as new instructions to itself. “Whenever the content of [the machine’s] storage was altered by the internal operations of the machine, one would naturally speak of the machine ‘modifying itself.’” By this process, the machine would also be able to teach itself in a way analogous to children’s learning, using a system of rewards and punishments for right and wrong answers.²³⁹ (Behaviorism^§MS3.1.2i was at the time the psychological theory of choice.)

“The electrical circuits which are used in electronic computing machinery seem to have the essential properties of nerves,” Turing said. Without having to go through the cumbersome process of creating a body, an electronic brain could be given organs of sight (television cameras), speech (loudspeakers), and hearing (microphones), by means of which it could learn games, languages, translation of languages, and mathematics, though he wondered about language because this was rather too dependent on locomotion. Compared with these large promises, the report was also at times rather more circumspect, the machine merely “mimicking education,” learning only by laboriously undertaking an enormous number of minimal mathematical steps.²⁴⁰

Sir Charles Darwin was not impressed: “a schoolboy’s essay … not suitable for publication” was his verdict.²⁴¹ Considering him useless to the ACE project, Darwin sent Turing back to Cambridge for a year’s sabbatical. The report was not published until after Turing’s death.

By the time the sabbatical was over, Turing had a new job, at the University of Manchester. As it happens, this was where Ludwig Wittgenstein^§MS2.1.1a had begun his mathematical and philosophical journey.

In Manchester, they had started to build an electronic computer using seven tons of surplus parts delivered there from the scrapped Bletchley Park machines. “Manchester 1” had 1024 bits of random access memory, with paper-tape input and output – this last mechanism had been Turing’s suggestion. By 1949, announced The Times, “the mechanical brain” in Manchester had done something that was practically impossible to achieve in paper. It had found some previously undiscovered, extremely large prime numbers.²⁴²

Turing decided it was time to try again with the theoretical ideas that had been so cursorily consigned to the proverbial dustbin by Sir Charles Darwin. In 1950, he published “Computing Machinery and Intelligence” in the philosophy journal, Mind. His question: how would you be able to determine when a machine was intelligent? His answer has come to be called the “Turing Test.”

Turing proposed an “imitation game,” where a machine and a person are behind a screen. Each is asked questions, with the source of the answers masked by a teleprinter. If the smartness of answers are indistinguishable to the human questioner, the machine might be deemed as intelligent as a person.

Playing such a game, the machine had certain advantages. The number of discrete states of which the Manchester machine was capable was, said Turing, about 10^50,000. Moreover, a universal machine was able to conduct any kind of operation depending on how they were programmed. And more: “By observing the results of its own behaviour it can modify its own programmes so as to achieve some purpose more effectively.” By calculation based on its own previous calculations, such a machine can learn. It can be “used to help in making up its own programmes …; a machine undoubtedly can be its own subject matter.”²⁴³

To this extent, computing machinery could perform some of the same operations of calculation as humans, though much faster. “Parts of modern machines which can be regarded as analogues of nerve cells work about a thousand times faster than the latter.” So, the “view that machines cannot give rise to surprises is due, to a fallacy …, the assumption that as soon as a fact is presented to a mind all consequences of that fact spring into the mind simultaneously with it.”²⁴⁴ Things can present themselves as surprises after a lot of working over data, and some of the working over that machines can do is too laborious for manual calculation. Today, these methods are called “machine learning” and “artificial intelligence.”^§AS1.4.6c

Having specified these capacities, Turing reframed the question that began the paper. With these capacities, “the question ‘Can machines think?’ should be replaced by ‘Are there imaginable digital computers which would do well in the imitation game?’”²⁴⁵

Philosopher of language John R. Searle^§MS2.3.1b was to mount what has become the best known challenge to Turing, in the hypothetical case of the Chinese room. Two people are behind Turing’s screen; the first one knows Chinese and the second doesn’t but has a dictionary. A third person asks each the meaning of words, and will get correct answers from both. The two people behind the screen seem equally smart because they can both type out the right answers. Like the second person, computers cannot know Chinese; they can only seem to know Chinese.²⁴⁶

The target of Searle’s critique was “artificial intelligence.” But Turing never made such an overblown claim. His claim was to no more than “mechanical intelligence,”²⁴⁷ and this intelligence was in fact a trick. Turing had a dry sense of humor, and with some of its absurd counter-arguments, “Computing Machinery and Intelligence” can be read as a humorous text. The computer, he said, exhibited no more than a semblance of intelligence. If it was smart enough, it would be able to trick a gullible user in an “imitation game.” The computer itself was the joker. Perhaps Turing had Wittgenstein’s “language game” in mind, though Wittgenstein had not been joking.^§MS2.3.2a Sir Charles Darwin was not joking, either.

Here is our re-reading of Turing. Calculation is one narrowly circumscribed function of meaning, a function which in our multimodal grammar we have called quantity. By transposition into quantity we can reduce a Chinese character to the zeros and ones that lie behind Unicode.^§MS0.2.1a The character makes sense as something in the world expressed in our sensuous experience though non-quantitative functions. Or we have the word for the character spoken in voice synthesis that also reduces the word represented by that character to zeros and ones, albeit a totally different set of zeros and ones from text. Because, even in the binary world the instruction tables for text and speech are fundamentally different and can only be aligned by probabilistic statistics.

The zeros and ones in the middle, between creation and rendering, are an extremely narrow species of written text, completely unreadable except by a machine. When, following an instruction to calculate, they come out of the machine, and they are mechanically reconstructed as text or sound. In the middle is a huge amount of very fast calculation, but the elemental textual units are far too minimal and too many for the human mind to be able to read, even for their mathematical meaning.

Now we’re back to routines like those developed in Bletchley Park to fight the German War Machine: encipherment > practically unintelligible, mathematically expressed text > decipherment. The stuff between encipherment and decipherment is no more than calculation, the logic of which humans have created by clever reduction, and the speed of which has been mechanized to beyond mortal capacities. We are back with Lovelace.

Nor is the computer so very different from other novel inventions of Lovelace’s time. When in 1825 the steam locomotive first went faster than a person walking or a horse pulling a stage coach, people marveled – would a human body disintegrate at twenty miles per hour, a speed until then thought possible only for angels?²⁴⁸ Bodies moving fast, numbers adding fast. Such are the powers of the mechanical prostheses of modern invention.

The question, then, is what is the scope of transposition into quantity? What are its affordances? Our answer, in sympathy we believe with the insights of both Lovelace and Turing: only things that are countable. By counting, instances can become concepts.^§MS1.1 By counting across a grade, qualities^§MS1.3.1 can be measured and named. By relating variables, curves can be drawn.^§MS1.2.2d By counting the pixels and numbering their colors, an image can be deconstructed then reconstructed.^§MS1.2.2e By numbering time and place, we can organize our days and find our way.^§AS1.3 And by giving a huge number of things a huge number of mostly unspeakable alphanumerical names, we have created for ourselves an unfathomably long list of things to count. That’s a lot, but also, that’s about all.

Turing’s mortal fate was to be determined by another kind of decidability, born of a different series of binaries. On 7 February 1952, he reported to the police a burglary at his house. When the case came to court, Turing confessed, “I tried to mislead you about my informant … I picked him up in Oxford Street, Manchester … I have been an accessory to an offense in this house … I had an affair with him.”

The informant’s friend had stolen Alan’s things, but that was soon ignored, and Turing was committed to trial on three charges of indecency. He wrote a grimly jokey letter about it to a friend, signing off:

Turing believes machines think

Turing lies with men

Therefore machines do not think

Yours in distress

Alan²⁴⁹

He was sentenced by the court to undergo “treatment,” and this included estrogen implants whose effect was chemical castration.²⁵⁰

Turing was found dead from poisoning on 7 June 1954. He had a small lab in the house where he used cyanide to conduct electrical experiments. The coroner’s court ruled suicide, but his mother, whom he had never liked but who went on to write for him a hagiography, insisted the death was accidental.²⁵¹

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²³¹ Dermot Turing, 2015, Prof: Alan Turing Decoded, a Biography, Brimscombe Port, UK: The History Press, pp. 113–47; George Dyson, 2012, Turing’s Cathedral: The Origins of the Digital Universe, New York, NY: Pantheon Books, pp. 253–57.

²³² George Dyson, 2012, Turing’s Cathedral: The Origins of the Digital Universe, New York, NY: Pantheon Books, pp. 7, 64ff., 257–59.

²³³ John von Neumann, 1945, “First Draft of a Report on the Edvac,” Philadelphia, PA: Moore School of Electrical Engineering, University of Pennsylvania.

²³⁴ George Dyson, 2012, Turing’s Cathedral: The Origins of the Digital Universe, New York, NY: Pantheon Books, pp. 287–88.

²³⁵ John von Neumann and Oskar Morgenstern, 1953 [2007], Theory of Games and Economic Behavior, Princeton, NJ: Princeton University Press.

²³⁶ John von Neumann, 1958, The Computer and the Brain, Yale, NH: Yale University Press.

²³⁷ George Dyson, 2012, Turing’s Cathedral: The Origins of the Digital Universe, New York, NY: Pantheon Books, pp. 42, 95, 298.

²³⁸ Arthur C. Clarke, 1968, 2001: A Space Odyssey, New York, NY: Signet, p. 156.

²³⁹ A.M. Turing, 1948, “Intelligent Machinery,” A Report to the National Physical Laboratory, pp. 8, 13–14.

²⁴⁰ Ibid., pp. 9–10, 11.

²⁴¹ Dermot Turing, 2015, Prof: Alan Turing Decoded, a Biography, Brimscombe Port, UK: The History Press, pp. 203, 213.

²⁴² Ibid., pp. 212–17.

²⁴³ A.M. Turing, 1950, “Computing Machinery and Intelligence,” Mind 59:433–60, pp. 441, 449.

²⁴⁴ Ibid., p. 451.

²⁴⁵ Ibid., p. 442.

²⁴⁶ John R. Searle, 1980, “Minds, Brains, and Programs,” Behavioral and Brain Sciences (3):417–57, pp. 417–18; John R. Searle, 2014, “What Your Computer Can’t Know,” New York Review of Books; Daniel C. Dennett, 1998, Brainchildren: Essays on Designing Minds, Cambridge, MA: MIT Press, p. 5; Stevan Harnad, 1989, “Minds, Machines and Searle,” Journal of Theoretical and Experimental Artificial Intelligence 1:5–25; Hubert L. Dreyfus, 1992, What Computers Still Can’t Do: A Critique of Artificial Reason, Cambridge, MA: MIT Press.

²⁴⁷ George Dyson, 2012, Turing’s Cathedral: The Origins of the Digital Universe, New York, NY: Pantheon Books, p. 310.

²⁴⁸ Wolfgang Schivelbusch, 1977 [2014], The Railway Journey: The Industrialization of Time and Space in the Nineteenth Century, Berkeley, CA: University of California Press.

²⁴⁹ Dermot Turing, 2015, Prof: Alan Turing Decoded, a Biography, Brimscombe Port, UK: The History Press, pp. 246–47.

²⁵⁰ David Leavitt, 2006, The Man Who Knew Too Much: Alan Turing and the Invention of the Computer, New York, NY: W.W. Norton.

²⁵¹ Sara Turing, 1959 [2012], Alan M. Turing, Oxford, UK: Oxford University Press, pp. 114–18.

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Cope, Bill, and Mary Kalantzis. 2020. Making Sense: Reference, Agency and Structure in a Grammar of Multimodal Meaning. Cambridge UK: Cambridge University Press, pp. 163-169. https://doi.org/10.1017/9781316459645

The “Turing Test” §MS1.3.2c